PORTABLE AI-INTEGRATED DEVICE FOR EARLY DETECTION AND CONTINUOUS MONITORING OF GLAUCOMA RISK

Abstract

Portable AI-Integrated Device for Early Detection and Continuous Monitoring of Glaucoma Risk" This invention describes a Portable Artificial Intelligence-Integrated Device for Early Detection and Continuous Monitoring of Glaucoma enables non-invasive self-screening by utilizing a high-resolution optical imaging system, including retinal scanners and optical sensors, to capture detailed images of the retinal nerve fiber layer and optic nerve head. The device is equipped with advanced artificial intelligence algorithms trained on retinal image datasets, analyzing these parameters to detect early signs of glaucoma. The system provides real-time feedback through an intuitive touchscreen interface, displaying diagnostic results in an easy-to-understand format. It dynamically adjusts focus, exposure, and lighting conditions based on ambient factors, ensuring accurate image capture. The device also compares current data with historical trends, flagging potential glaucoma progression when thresholds are exceeded. Equipped with wireless communication capabilities, it allows secure transmission of diagnostic data to healthcare providers for remote monitoring. The portable design makes it suitable for home use, facilitating continuous glaucoma monitoring and early intervention.

Specification

Description:[0001] This invention relates to the field of medical sciences more particularly a Portable Artificial Intelligence-Integrated Device designed for the early detection and continuous monitoring of glaucoma. The invention integrates a high-resolution optical imaging system and advanced artificial intelligence algorithms to analyze retinal nerve fiber layer thickness and optic nerve head parameters, both of which are critical for identifying glaucoma in its early stages. The device is equipped with self-screening capabilities, allowing users to independently assess their glaucoma risk through an intuitive touchscreen interface. It dynamically adjusts imaging conditions based on real-time factors such as focus, exposure, and lighting, ensuring precise and high-quality image capture. Furthermore, the system allows for secure wireless transmission of diagnostic data to healthcare providers for remote monitoring. This invention is particularly suited for non-clinical environments, enabling users to perform regular screenings at home while maintaining medical-grade accuracy and reliability. The invention enhances accessibility to glaucoma detection, improving patient outcomes through timely diagnosis and intervention.

PRIOR ART AND PROBLEM TO BE SOLVED

[0002] Glaucoma, often referred to as the "silent thief of sight," is a progressive eye disease that affects millions of people worldwide and is a leading cause of irreversible blindness. The condition is characterised by damage to the optic nerve, usually caused by elevated intraocular pressure (IOP), resulting in the gradual loss of peripheral vision. Left untreated, glaucoma can lead to total blindness. The disease's progression is typically slow and asymptomatic in its early stages, making early detection vital for effective intervention and treatment. Despite advancements in treatment options, glaucoma remains challenging to manage, particularly because it is often detected only after significant damage has occurred. One of the biggest challenges in glaucoma care is the early diagnosis and continuous monitoring required to prevent vision loss. Conventional diagnostic methods involve the use of specialised equipment, such as tonometers, visual field analysers, and optical coherence tomography (OCT) devices. These instruments are expensive, bulky, and typically only available in clinical settings. They require trained professionals to operate, further restricting their accessibility. As a result, glaucoma diagnosis and monitoring are usually confined to hospitals or specialty eye clinics, which poses a major problem for individuals in remote or underserved areas.
[0003] Moreover, patients diagnosed with glaucoma need regular monitoring to assess disease progression and adjust treatment plans as necessary. However, the dependence on clinical visits for regular check-ups can result in delays, especially for elderly or mobility-impaired individuals. This gap in access to frequent screenings can worsen the disease before the necessary interventions are made. Additionally, access to specialised ophthalmic equipment in many developing regions is limited, exacerbating the problem and leaving many glaucoma cases undiagnosed until it's too late.The need for a more accessible, cost-effective, and portable solution to glaucoma detection and monitoring is clear. A device that can facilitate early diagnosis, allow for frequent self-screening, and reduce the reliance on clinical settings would greatly improve the management of glaucoma. Such a solution would empower patients, especially those in remote or underserved regions, to take a more proactive role in their eye health. It would also alleviate the burden on healthcare systems by reducing the need for frequent clinic visits for routine monitoring.Technological advancements in artificial intelligence (AI) and portable medical devices present an opportunity to address these unmet needs. AI algorithms can analyse critical retinal parameters, such as retinal nerve fiber layer (RNFL) thickness and optic nerve head (ONH) changes, allowing for the early detection of glaucoma. Coupled with portable imaging technology, these AI-driven systems can perform detailed retinal scans in a compact, user-friendly device. Such devices could enable patients to perform glaucoma screenings at home or in mobile clinics without requiring specialised equipment or professional supervision.

[0004] One prior art describes Glaucoma diagnosis method using fundus image and apparatus for the same. The disclosed glaucoma diagnosis method involves using a fundus image and related apparatus. It includes amplifying data by generating multiple transformed images from a preprocessed original fundus image. Different learning models are trained on these transformed images, and a glaucoma determination model is created from their outputs. The original fundus image is then diagnosed for glaucoma classification using this model. Another prior art mentions Open angle glaucoma recognition method, medium and system based on sparse selection. The invention pertains to AI-based identification of open-angle glaucoma, focusing on a method, medium, and system using sparse selection. This method involves inputting patient imaging results, extracting and combining feature vectors into a matrix, and creating an examination result matrix. It includes inputting physiological data, constructing a Laplacian matrix, and developing a prediction function using sparse selection theory to determine an optimal mapping matrix. The approach leverages L2,1/2 matrix norm and semi-supervised learning to enhance prediction accuracy and practical value. Another prior art describes Apparatus, system and method for diagnosing glaucoma. The invention discloses a device, system, and method for diagnosing glaucoma. The device includes a data input unit that receives the subject's clinical information and a diagnosis unit that uses a machine learning model to determine glaucoma presence and generates a graphic chart explaining the diagnosis. Clinical information includes upper, lower, and posterior retinal nerve fiber layer thicknesses, intraocular pressure, and pattern standard deviation from visual field tests.
[0005] To resolve the abovementioned problem, a portable glaucoma detection device is designed to revolutionise the early detection and monitoring of glaucoma. It uses AI algorithms to analyse key retinal parameters such as RNFL thickness and ONH dimensions, providing real-time feedback on potential glaucoma risks. The high-resolution retinal scanner captures detailed retinal images, enabling the AI to detect subtle changes indicative of the disease. With a user-friendly touchscreen, the device simplifies the self-screening process for patients, making it easier for them to track their eye health regularly. Wireless connectivity, including Wi-Fi and Bluetooth, ensures that data can be securely transferred to cloud-based systems for healthcare providers to monitor and intervene when necessary. The lightweight design and long-lasting battery enable the device to be used in diverse settings, such as homes and mobile clinics, improving accessibility to glaucoma screening and early detection efforts.

THE OBJECTIVES OF THE INVENTION:
[0006] Glaucoma is a leading cause of irreversible blindness, particularly affecting older adults. It is characterised by progressive damage to the optic nerve, often due to elevated intraocular pressure (IOP), resulting in gradual peripheral vision loss and, in advanced stages, total blindness. Since glaucoma progresses slowly and without symptoms until significant damage occurs, early detection is critical.
[0007] It has already been proposed that the tonometers and optical coherence tomography (OCT) devices, are effective but confined to clinical settings, expensive, and require trained professionals, limiting access for patients in remote areas. Frequent monitoring is essential but challenging due to the need for repeated clinic visits. These limitations delay diagnosis and increase the risk of vision loss. To address this, there is a growing need for portable, accessible, and user-friendly glaucoma screening tools that enable real-time monitoring. Technological advances in AI, portable imaging, and wireless data transfer offer solutions for more frequent, reliable glaucoma screenings outside of clinics.
[0008] The principal objective of the invention is to develop and provide a portable artificial intelligence-integrated device capable of facilitating the early detection and continuous monitoring of glaucoma by utilizing advanced artificial intelligence algorithms for the analysis of retinal nerve fiber layer thickness and optic nerve head parameters. The device is structured for non-invasive, real-time scanning and offers seamless data transfer to cloud-based storage systems, enabling remote monitoring and timely medical intervention. This system incorporates a high-resolution retinal scanner, an intuitive touchscreen interface, and secure wireless connectivity, all within a compact, lightweight design suitable for home and clinical use, improving accessibility and enabling regular screenings.
[0009] Another objective of the invention is to enable comprehensive and accurate detection of early-stage glaucoma through the integration of optical sensors and a high-resolution camera capable of capturing detailed images of the retinal nerve fiber layer and optic nerve head. The device is designed to detect even subtle variations in these parameters, which are critical indicators of the onset of glaucoma.
[0010] A further objective of the invention is to employ advanced artificial intelligence algorithms trained on extensive retinal datasets to facilitate real-time analysis of retinal nerve fiber layer thickness and optic nerve head parameters. This analysis is performed automatically, providing instant feedback on the likelihood of glaucoma based on established medical criteria.
[0011] A further objective of the invention is to provide an intuitive and user-friendly touchscreen interface that allows individuals with minimal training to navigate the self-screening process efficiently. The device displays results in a format that is comprehensible to the patient while allowing for more detailed data to be shared with healthcare professionals.
[0012] A further objective of the invention is to ensure secure, real-time transmission of diagnostic data through integrated wireless modules, including Wi-Fi and Bluetooth, allowing for continuous and remote monitoring by healthcare providers. The system is equipped with robust encryption protocols to safeguard patient data during transfer and storage.
[0013] A further objective of the invention is to integrate power efficiency and safety features, including a rechargeable battery designed to support prolonged use, along with automatic shut-off functionality to prevent overheating and data loss, ensuring the durability and reliability of the system in both clinical and non-clinical settings.

SUMMARY OF THE INVENTION
[0014] Current technology for glaucoma diagnosis includes tools like tonometers, visual field analysers, and optical coherence tomography (OCT) devices. Tonometers measure intraocular pressure (IOP), visual field analysers assess peripheral vision loss, and OCT provides detailed retina and optic nerve imaging. These methods are highly effective for diagnosing glaucoma but come with significant limitations The main drawback is that these devices are expensive, bulky, and typically only available in clinical settings, requiring trained professionals to operate them. This limits access to timely glaucoma screenings, especially for patients in remote or underserved areas. Additionally, patients with glaucoma or those at high risk require frequent monitoring to manage the disease, but regular clinic visits are time-consuming, inconvenient, and costly These technological limitations create barriers to early detection and continuous monitoring, increasing the risk of delayed diagnosis and treatment. The need for more accessible, portable, and user-friendly screening tools remains unmet, driving the development of new solutions like AI-integrated, portable devices for home use.

[0015] So here in this invention an AI-integrated glaucoma screening device is engineered to enable early detection of glaucoma by analysing retinal nerve fiber layer (RNFL) thickness and optic nerve head (ONH) parameters. It features a high-resolution camera with optical sensors that capture detailed retinal images, allowing AI algorithms to detect patterns indicative of glaucoma. The AI, trained on extensive datasets, instantly analyses RNFL and ONH measurements, providing real-time feedback on the touchscreen interface. The ergonomic, portable design allows easy handling, making the device suitable for home use, mobile clinics, or regular health screenings. Wireless connectivity facilitates seamless data transfer to cloud databases, enabling healthcare providers to monitor them remotely. With secure encryption protocols, patient data is protected during transmission and storage. The device's rechargeable battery and automatic shut-off function ensure safety and reliability during use. Overall, this device improves early diagnosis and glaucoma monitoring, significantly advancing ocular health management.

DETAILED DESCRIPTION OF THE INVENTION

[0016] While the present invention is described herein by example, using various embodiments and illustrative drawings, those skilled in the art will recognise invention is neither intended to be limited that to the embodiment of drawing or drawings described nor designed to represent the scale of the various components. Further, some features that may form a part of the invention may need to be illustrated with specific figures for ease of illustration. Such om and glass from the road using a vacuum suction mechanism and a magnetic mechanism attached to the machine at the bottom end. The metal that form disclosed. Still, on the contrary, the invention covers all modification/s, equivalents, and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. The headings are used for organisational purposes only and are not meant to limit the description's size or the claims. As used throughout this specification, the worn "may" be used in a permissive sense (That is, meaning having the potential) rather than the mandatory sense (That is, meaning, must).

[0017] Further, the words "an" or "a" mean "at least one" and the word "plurality" means one or more unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents and any additional subject matter not recited, and is not supposed to exclude any other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents acts, materials, devices, articles and the like are included in the specification solely to provide a context for the present invention.
[0018] In this disclosure, whenever an element or a group of elements is preceded with the transitional phrase "comprising", it is also understood that it contemplates the same component or group of elements with transitional phrases "consisting essentially of, "consisting", "selected from the group comprising", "including", or "is" preceding the recitation of the element or group of elements and vice versa. Before explaining at least one embodiment of the invention in detail, it is to be understood that the present invention is not limited in its application to the details outlined in the following description or exemplified by the examples. The invention is capable of other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is for description and should not be regarded as limiting. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention belongs. Besides, the descriptions, materials, methods, and examples are illustrative only and not intended to be limiting. Methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention.
[0019] The present invention disclose a Portable Artificial Intelligence-Integrated Device for Early Detection and Continuous Monitoring of Glaucoma Using Retinal Nerve Fiber Layer Analysis and Optic Nerve Head Parameters is designed to address the critical need for accessible, accurate, and non-invasive glaucoma screening. Glaucoma, a progressive optic neuropathy that can lead to irreversible blindness if left untreated, necessitates early detection and consistent monitoring to prevent vision loss. Current diagnostic techniques require clinical intervention and the use of stationary, specialized equipment, which limits accessibility and delays diagnosis. This device aims to overcome these limitations by enabling individuals to perform regular self-screenings in non-clinical settings, such as their homes, while facilitating continuous monitoring by healthcare professionals through real-time data transmission.
[0020] The primary purpose of the system is to enable early detection of glaucoma by analyzing retinal nerve fiber layer thickness and optic nerve head parameters, which are critical indicators of glaucoma progression. The device uses advanced artificial intelligence algorithms to provide instant feedback on the risk of glaucoma, allowing users to take prompt action in consultation with healthcare professionals. By empowering individuals with the ability to monitor their eye health independently, the system improves the likelihood of early intervention, thereby reducing the risk of irreversible damage to the optic nerve and preserving vision.
[0021] The system is designed for ease of use, ensuring that patients with minimal technical expertise can operate the device and navigate through the screening process. Its portable and ergonomic design allows it to be used in diverse environments, from homes to mobile clinics, making it accessible to a broad demographic, including those in remote or underserved areas. The device also facilitates continuous monitoring by enabling frequent screenings without the need for specialized personnel, providing real-time insights into the progression of the condition. In addition to its core function of early detection, the system is designed with features that ensure data security, user safety, and operational efficiency. It offers seamless integration with digital healthcare ecosystems, allowing for secure data transfer and remote analysis. This continuous monitoring system facilitates timely medical intervention by healthcare providers, ensuring that any significant changes in retinal parameters are addressed without delay. Through its robust design, the device not only improves access to glaucoma screening but also enhances patient outcomes by enabling personalized and proactive healthcare management.
[0022] The device features a compact, rectangular body with gently rounded edges to facilitate comfortable handling during use. It is encased in a smooth, matte-finish exterior, providing a tactile surface that ensures a secure grip while maintaining an elegant, professional appearance. The dimensions of the device are optimized for one-handed operation, allowing it to be easily maneuvered and positioned during retinal scanning. The overall structure exudes a sense of precision and reliability, with clean lines and a streamlined form factor that minimizes unnecessary bulk. The front face of the device is dominated by a high-resolution display screen, seamlessly integrated into the design. This touchscreen interface is expansive yet proportionate, offering users a clear and intuitive means of navigating through various functions and viewing diagnostic results. The screen is coated with a specialized anti-glare material, ensuring visibility even in brightly lit environments. The display is bordered by a subtle, narrow frame that enhances the device's sleek aesthetic without detracting from its functionality. This frame is designed to be unobtrusive, blending harmoniously with the overall design to maintain visual consistency.
[0023] On the side and rear panels, the device incorporates discreet ports for wireless connectivity and charging, all flush with the surface to preserve the smooth exterior. These ports are strategically placed to allow for easy access while ensuring they do not interfere with the user's operation of the device. The charging port is accompanied by a small, unobtrusive indicator light, which subtly alerts the user to the device's battery status without disrupting the clean lines of the design. The rear surface of the device is ergonomically contoured to provide additional comfort during extended use, with a slight curve that aligns with the natural shape of the hand. This contouring not only enhances the device's aesthetic appeal but also improves its functionality, reducing user fatigue during prolonged screenings. The rear panel is also reinforced with a lightweight yet durable material designed to withstand the rigors of regular transport and handling, ensuring that the device remains in pristine condition throughout its lifecycle.
[0024] The probe features a cylindrical shape, slightly tapered towards the tip to facilitate accurate alignment with the user's eye. The front portion of the probe, which makes contact with the user's eye area during scanning, is fitted with a high-precision optical lens that captures detailed images of the retinal nerve fiber layer and the optic nerve head. The lens is protected by a transparent, scratch-resistant covering that ensures both durability and the maintenance of image clarity over time. This protective layer is composed of medical-grade materials to ensure the safety and comfort of the user during the scanning process, minimizing any risk of irritation or discomfort during repeated use Encircling the optical lens is a soft, hypoallergenic silicone rim, ergonomically designed to gently rest against the user's face, ensuring a secure and comfortable fit while preventing any excess light from entering the scanner. This design element is critical for ensuring accurate image capture, as it minimizes interference from external light sources, thereby enhancing the precision of the readings. The rim is replaceable and can be easily sanitized between uses, adhering to strict hygiene standards for medical devices.
[0025] The probe is equipped with a ring of subtle, integrated LED lights surrounding the optical lens. These lights provide the necessary illumination for capturing high-resolution images of the retina and optic nerve head, ensuring that the scan remains clear and detailed, even in low-light conditions. The lighting system is intelligently designed to be non-invasive, emitting a soft, diffused light that does not cause discomfort to the user during operation. The intensity of the light is automatically adjusted based on the ambient lighting conditions to optimize image quality while ensuring the user's comfort The rear of the probe houses a small sensor array, including infrared sensors and depth-measuring components, that assist in aligning the probe with the user's eye at the optimal distance for scanning. These sensors communicate with the main processing unit to guide the user during the alignment process, providing real-time feedback on the touchscreen interface to ensure proper positioning and effective image capture.
[0026] Internally, the probe contains advanced micro-optical components that interface with the artificial intelligence system. Once the retinal images are captured, the data is instantly transmitted to the processing unit within the device for analysis. The probe's internal wiring and connection points are carefully engineered to ensure minimal interference or data loss during transmission, maintaining the integrity of the retinal images as they are analyzed for retinal nerve fiber layer thickness and optic nerve head parameters. Externally, the probe is finished with the same matte, anti-glare surface as the rest of the device, ensuring a unified and polished appearance. It is lightweight yet robust, designed to withstand regular handling and repeated use without compromising its precision or durability. The probe's design is understated yet sophisticated, prioritizing the user's comfort and ease of use while maintaining the high level of functionality required for accurate glaucoma detection.
[0027] The Portable Artificial Intelligence-Integrated Device comprises several sophisticated components, each meticulously designed and integrated to ensure the efficient and precise functioning of the device. The integration of these components is critical to achieving the system's objective of accurate glaucoma detection, continuous monitoring, and seamless user interaction. Each component plays a pivotal role in enabling the device to fulfill its purpose, and their interaction is crucial for the successful operation of the system.
[0028] At the heart of the device is the high-resolution optical imaging system, which includes the retinal scanner and the optical sensors. It is equipped on a probe of the Portable Artificial Intelligence-Integrated Device to ensure optimal functionality during retinal scanning. The tip is used to facilitate accurate alignment with the user's eye, allowing for precise positioning that enhances the quality of the images captured during the screening process. This tapering also provides ergonomic ease, allowing the user to hold the device comfortably during operation. At the front of the probe, the portion that comes into proximity with the user's eye during scanning is equipped with a high-precision optical lens. This lens is engineered to capture detailed and high-resolution images of the retinal nerve fiber layer and optic nerve head, which are critical in the early detection of glaucoma. The optical lens is encased in a transparent, scratch-resistant covering that not only ensures durability but also maintains the clarity of the images over time, even with frequent use. The protective covering is composed of medical-grade materials, specifically chosen to guarantee the safety and comfort of the user. This layer is designed to minimize the risk of irritation or discomfort, making the device suitable for repeated use without compromising the patient's well-being. Surrounding the optical lens is a soft, hypoallergenic silicone rim, which has been ergonomically designed to gently rest against the user's face during operation. This rim ensures a secure and comfortable fit, helping to keep the probe stable during scanning, thereby preventing any unintentional movement that could affect the accuracy of the results. Additionally, this silicone rim serves a critical function in enhancing the precision of the image capture by blocking out excess light from external sources. By minimizing interference from ambient light, the probe is able to provide clear and accurate retinal images, which are essential for the subsequent analysis performed by the device's artificial intelligence algorithms. The silicone rim is designed with hygiene and user safety in mind. It is easily replaceable and can be sanitized between uses, adhering to the stringent hygiene standards required for medical devices. This feature is particularly important for multi-user environments, such as clinics or mobile health settings, ensuring that the device can be used by multiple patients while maintaining the highest standards of cleanliness and preventing the spread of contaminants. These components work in tandem to capture detailed images of the retinal nerve fiber layer and optic nerve head. The imaging system is capable of detecting minute changes in these parameters, which are vital indicators of early-stage glaucoma. The optical sensors continuously adjust to the user's eye position, ensuring that the captured images are precise and clear. This imaging data is critical for the subsequent stages of analysis and forms the foundation upon which the artificial intelligence algorithms operate. The imaging system's ability to capture high-fidelity data is the first step in the detection process, and without this, the device would not be able to perform its primary function of early glaucoma detection.
[0029] The optical imaging system is housed within the probe of the device, which is ergonomically designed to facilitate direct alignment with the user's eye. The tip of the probe plays a crucial role in positioning the optical imaging system at the optimal distance from the retina, ensuring that the captured images are clear and distortion-free. The cylindrical, slightly tapered structure of the probe enhances the user's ability to comfortably align the device, thereby reducing any errors in positioning that could negatively impact the quality of the scan. This feature ensures that even users without specialized training can achieve accurate results, a key factor in the device's design.
[0030] At the core of the optical imaging system is the high-precision optical lens. This lens is engineered to focus on the retinal nerve fiber layer and optic nerve head with exceptional clarity, capturing the minute details required for early glaucoma detection. The optical lens is made from highly durable materials and is encased in a scratch-resistant, transparent covering. This covering is critical for maintaining the lens's integrity over time, especially in environments where the device is subjected to frequent use. Furthermore, the protective covering is composed of medical-grade materials, ensuring that it is safe to use on sensitive areas of the face, particularly around the eyes. It reduces the risk of irritation while maintaining optimal image clarity, allowing the device to function without compromise even after multiple uses.
[0031] Surrounding the optical lens is an integrated set of optical sensors. These sensors continuously monitor the alignment of the probe with the user's eye, adjusting the focus and other imaging parameters in real-time to capture the highest-quality images. The sensors play a vital role in ensuring that the images are neither overexposed nor underexposed, compensating for variations in ambient light conditions. This constant adjustment process allows the optical imaging system to adapt to the user's environment and ensure that the captured images maintain the necessary fidelity for accurate analysis. By continuously monitoring and adjusting the focus, the optical sensors guarantee that even minute changes in the retinal nerve fiber layer and optic nerve head, which are early indicators of glaucoma, are accurately detected.
[0032] The optical sensors operate by continuously tracking the position and alignment of the probe in relation to the user's eye. This real-time monitoring ensures that the retinal scanner remains correctly positioned throughout the scanning process, reducing the risk of image distortion or misalignment, which could otherwise compromise the accuracy of the diagnostic results. By tracking subtle movements, the sensors dynamically adjust the focus of the optical lens, ensuring that the captured images remain sharp and clear regardless of any minor changes in the probe's distance from the eye. This function is particularly critical in home-use environments, where users may not have the technical expertise to maintain perfect alignment manually. The sensors compensate for these variations, ensuring consistent and reliable performance.
[0033] In addition to focus adjustments, the optical sensors play a vital role in managing light exposure. The retinal nerve fiber layer and optic nerve head are highly sensitive structures, and their accurate imaging requires careful management of light conditions. The sensors are equipped with photometric capabilities, allowing them to continuously measure the ambient light levels during scanning. Based on these measurements, the sensors adjust the exposure settings of the imaging system, ensuring that the retinal images are neither overexposed nor underexposed. This adjustment is crucial for maintaining the fidelity of the captured images, as either extreme in exposure can lead to loss of detail, which is particularly problematic when attempting to detect early signs of glaucoma. The sensors ensure that the images retain the necessary contrast and clarity for accurate analysis by the artificial intelligence algorithms The optical sensors also include an automatic light compensation feature, which adjusts the intensity of the illumination emitted by the integrated LED lights surrounding the optical lens. These sensors work in real-time, compensating for any fluctuations in external lighting conditions. If the user is operating the device in a brightly lit room or an environment with changing light levels, the sensors adjust the light intensity to optimize the image capture conditions. This ensures that the retinal images are not compromised by shadows or glare, which can interfere with the precision of the scanning process. The ability to modulate the internal light source in response to external conditions demonstrates the high level of sophistication and adaptability built into the device's optical sensors.
[0034] Furthermore, the optical sensors are designed to detect and analyze minute changes in the retinal nerve fiber layer and optic nerve head, which are early indicators of glaucoma. By constantly monitoring these retinal structures during scanning, the sensors provide the system with highly detailed and accurate data. This data is then transmitted to the device's artificial intelligence processing unit, which uses it to assess the user's glaucoma risk. The sensors' ability to capture these subtle changes is critical for early detection, as early-stage glaucoma may not present obvious visual symptoms to the patient. The sensors thus enable the device to detect changes that would otherwise go unnoticed, facilitating timely medical intervention. The interaction between the optical sensors and the other components of the device, such as the optical lens and the artificial intelligence system, is seamless. The sensors provide the raw data needed for the lens to capture accurate images and for the artificial intelligence system to perform its analysis. Without the constant adjustments made by the sensors, the quality of the images would be inconsistent, potentially leading to unreliable results. The sensors' real-time adjustments ensure that each image is optimized for diagnostic purposes, allowing the artificial intelligence algorithms to make precise assessments of retinal nerve fiber layer thickness and optic nerve head parameters.
# Threshold values for the optical sensor parameters
FOCUS_THRESHOLD = 0.01 # Tolerance for focus (sharpness)
EXPOSURE_THRESHOLD = 0.5 # Mid-range exposure value (scale from 0 to 1)
LIGHT_THRESHOLD = 150 # Lighting level to control the intensity of the internal light source

# Function to calculate sharpness (focus) based on image Laplacian variance
def calculate_focus(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
laplacian_var = cv2.Laplacian(gray, cv2.CV_64F).var()
return laplacian_var

# Function to adjust focus
def adjust_focus(image, current_focus):
focus_value = calculate_focus(image)
if abs(focus_value - current_focus) > FOCUS_THRESHOLD:
if focus_value < current_focus:
print("Focus adjustment: Increase focus.")
else:
print("Focus adjustment: Decrease focus.")
# Implement motor or lens adjustment code here
return focus_value

# Function to calculate average brightness (exposure level) in the image
def calculate_exposure(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
avg_brightness = np.mean(gray)
return avg_brightness / 255 # Normalize to 0-1 scale

# Function to adjust exposure
def adjust_exposure(image, current_exposure):
exposure_value = calculate_exposure(image)
if abs(exposure_value - current_exposure) > EXPOSURE_THRESHOLD:
if exposure_value < current_exposure:
print("Exposure adjustment: Increase exposure.")
else:
print("Exposure adjustment: Decrease exposure.")
# Implement camera exposure adjustment code here
return exposure_value

# Function to adjust lighting
def adjust_lighting(ambient_light_level):
if ambient_light_level < LIGHT_THRESHOLD:
print("Lighting adjustment: Increase internal lighting.")
# Implement light intensity increase code here
else:
print("Lighting adjustment: Decrease internal lighting.")
# Implement light intensity decrease code here

# Example main loop for processing real-time data
def monitor_image(image, current_focus, current_exposure, ambient_light_level):
new_focus = adjust_focus(image, current_focus)
new_exposure = adjust_exposure(image, current_exposure)
adjust_lighting(ambient_light_level)
return new_focus, new_exposure

# Example input (mock) for real-time image and sensor data
mock_image = np.zeros((512, 512, 3), dtype=np.uint8) # Placeholder for an actual retinal image
current_focus = calculate_focus(mock_image)
current_exposure = calculate_exposure(mock_image)
ambient_light_level = 120 # Example of ambient light sensor reading
# Running the monitor function
new_focus, new_exposure = monitor_image(mock_image, current_focus, current_exposure, ambient_light_level)

[0035] The interaction between the optical lens and the optical sensors is seamless, with both components working together to ensure that the retinal images are captured with the highest degree of accuracy. The sensors adjust the lens focus based on the distance from the user's retina, ensuring that the device captures a sharp and detailed image of the retinal nerve fiber layer and optic nerve head. This precision is critical for detecting the subtle changes in these parameters, which serve as early indicators of glaucoma progression. Without this level of interaction between the lens and the sensors, the device would not be able to deliver the high-fidelity images necessary for reliable glaucoma detection.
[0036] This interaction is fundamental to the device's ability to detect glaucoma in its early stages, as it ensures that every image captured reflects the subtle variations in the retinal nerve fiber layer and optic nerve head that are key indicators of glaucoma progression. The optical sensors are engineered to continuously monitor the position of the device relative to the user's eye, calculating the optimal focal length in real-time. These sensors work in direct coordination with the high-precision optical lens, adjusting the lens focus dynamically based on the user's eye position and movement.
[0037] The optical lens serves as the primary component for capturing high-resolution images of the retina, and its performance is critically dependent on its ability to maintain precise focus. The lens captures images of the retinal nerve fiber layer and the optic nerve head, both of which are highly sensitive and require exceptional clarity to identify early-stage glaucoma indicators. The sensors integrated around the lens provide continuous feedback on the distance between the lens and the retina. As the user's eye moves or as slight shifts in position occur, the sensors automatically adjust the focus of the lens to maintain image sharpness and clarity. This real-time adjustment process is what enables the device to capture high-fidelity images, even when there are small variations in alignment.
[0038] The seamless nature of the interaction between the optical lens and sensors ensures that the device compensates for any external factors that might otherwise compromise image quality. For instance, minor shifts in the user's position are immediately detected by the sensors, and the lens is adjusted accordingly to keep the retinal structures in focus. This adjustment is critical for detecting subtle changes in the retinal nerve fiber layer thickness or optic nerve head parameters, which are often the first indicators of glaucoma development. Without this constant, dynamic adjustment, the images captured by the device would likely suffer from blurriness or distortion, making it difficult for the artificial intelligence algorithms to analyze the data accurately.
[0039] Furthermore, the optical sensors provide real-time feedback to the system on the lighting conditions and exposure levels. They adjust the lens's exposure settings based on ambient light conditions, ensuring that the images captured are neither overexposed nor underexposed. This interaction guarantees that the images retain the necessary contrast and detail, making it easier for the artificial intelligence system to detect the fine structural changes associated with glaucoma. The optical sensors and lens work together to create a balanced and adaptive imaging environment, ensuring that no matter the external conditions, the device can consistently produce reliable, high-quality images for analysis.
[0040] The precision of this interaction is what enables the device to perform its primary function-accurate, early detection of glaucoma. By continuously adjusting the focus and ensuring optimal imaging conditions, the optical sensors and lens allow the system to detect even the most minute changes in the retinal nerve fiber layer and optic nerve head. These changes are often imperceptible to the naked eye but are crucial for diagnosing glaucoma at an early stage when treatment is most effective. Without this level of integration and seamless interaction between the lens and sensors, the device would not be able to deliver the high-fidelity images that are essential for reliable glaucoma detection. This interaction forms the backbone of the device's imaging system, ensuring that it meets the stringent demands of precision and accuracy required for effective glaucoma monitoring.
[0041] Once the imaging data is captured, it is immediately transmitted to the internal processing unit, which houses the artificial intelligence algorithms. The processing unit is equipped with advanced machine learning models specifically trained on a vast dataset of retinal images. This enables the device to recognize patterns associated with glaucoma. The artificial intelligence system processes the retinal nerve fiber layer thickness and optic nerve head parameters, providing real-time analysis of the captured images. The integration between the imaging system and the artificial intelligence algorithms is seamless, ensuring that there is no delay in the processing of the data. The results of the analysis are instantaneously displayed on the touchscreen interface, allowing the user to receive immediate feedback on their glaucoma risk. The artificial intelligence algorithms not only analyze the current data but also compare it with historical data stored in the system, enabling the device to monitor the progression of the disease over time.
# Threshold values
RNFL_THICKNESS_THRESHOLD = 85 # Threshold for Retinal Nerve Fiber Layer thickness in micrometers
ONH_DIAMETER_THRESHOLD = 1.5 # Threshold for Optic Nerve Head diameter in millimeters

# Historical data storage (for simplicity, stored as arrays)
historical_rnfl_data = []
historical_onh_data = []

# Simulated machine learning model (random forest in this case)
# Normally, you would load a pre-trained model here
glaucoma_model = RandomForestClassifier(n_estimators=100)

# Preprocessing function for incoming image data
def preprocess_image_data(image_data):
# Example preprocessing: Normalizing pixel data, feature extraction for RNFL and ONH
image_normalized = image_data / 255.0 # Normalize image pixel values
rnfl_thickness = extract_rnfl_thickness(image_normalized)
onh_diameter = extract_onh_diameter(image_normalized)
return rnfl_thickness, onh_diameter

# Function to extract Retinal Nerve Fiber Layer thickness from image (simulated)
def extract_rnfl_thickness(image):
# Placeholder for actual RNFL extraction logic
# In practice, you would use image processing techniques to calculate the thickness
return np.random.uniform(80, 100) # Simulating RNFL thickness between 80-100 micrometers

# Function to extract Optic Nerve Head diameter from image (simulated)
def extract_onh_diameter(image):
# Placeholder for actual ONH diameter extraction logic
return np.random.uniform(1.4, 1.6) # Simulating ONH diameter between 1.4-1.6 mm

# Function to compare current RNFL and ONH parameters with historical data
def compare_with_historical_data(current_rnfl, current_onh):
rnfl_deviation = np.std(historical_rnfl_data)
onh_deviation = np.std(historical_onh_data)

rnfl_progression = abs(current_rnfl - np.mean(historical_rnfl_data)) > RNFL_THICKNESS_THRESHOLD * 0.05
onh_progression = abs(current_onh - np.mean(historical_onh_data)) > ONH_DIAMETER_THRESHOLD * 0.05

return rnfl_progression, onh_progression

# Function to analyze the extracted features and provide real-time feedback
def analyze_glaucoma_risk(rnfl_thickness, onh_diameter):
features = np.array([rnfl_thickness, onh_diameter]).reshape(1, -1)

# Scale the features
scaler = StandardScaler()
features_scaled = scaler.fit_transform(features)

# Predict glaucoma risk using the pre-trained model
glaucoma_risk = glaucoma_model.predict(features_scaled)

if glaucoma_risk == 1:
return "High Glaucoma Risk"
else:
return "Low Glaucoma Risk"

# Main function to process and analyze new retinal image data
def process_image(image_data):
# Preprocess the incoming image to extract RNFL and ONH parameters
rnfl_thickness, onh_diameter = preprocess_image_data(image_data)

# Analyze current data for glaucoma risk
glaucoma_risk_feedback = analyze_glaucoma_risk(rnfl_thickness, onh_diameter)

# Compare with historical data for disease progression
if len(historical_rnfl_data) > 0 and len(historical_onh_data) > 0:
rnfl_progression, onh_progression = compare_with_historical_data(rnfl_thickness, onh_diameter)
if rnfl_progression or onh_progression:
print("Progression of Glaucoma Detected.")
else:
print("No significant progression detected.")

# Store the current data for future comparison
historical_rnfl_data.append(rnfl_thickness)
historical_onh_data.append(onh_diameter)

# Display real-time feedback to the user
print(f"RNFL Thickness: {rnfl_thickness} µm, ONH Diameter: {onh_diameter} mm")
print(f"Glaucoma Risk: {glaucoma_risk_feedback}")

# Example input (mock) for new retinal image
mock_image = np.zeros((512, 512, 3), dtype=np.uint8) # Placeholder for actual image input

# Process the new retinal image
process_image(mock_image)
[0042] Here using the above system it is capable of real-time analysis of retinal nerve fiber layer (RNFL) thickness and optic nerve head (ONH) parameters, specifically for glaucoma risk assessment. It is built using machine learning principles, and the random forest classifier in this case has been pre-trained on a dataset of retinal images with known glaucoma indicators. When a new image is processed by the device, the artificial intelligence system extracts features from the retinal images, including RNFL thickness and ONH diameter. These features are then analyzed using the machine learning model to provide immediate feedback on the patient's risk of developing glaucoma. The threshold values, such as RNFL_THICKNESS_THRESHOLD and ONH_DIAMETER_THRESHOLD, are critical because they define the normal range for these retinal parameters. Deviations from these values, particularly beyond a 5% margin, indicate potential progression of glaucoma. The artificial intelligence system does not rely solely on the current snapshot of data but also compares the extracted parameters with historical data stored within the device. This allows for continuous monitoring of disease progression over time. If significant deviations are detected in the RNFL thickness or ONH diameter compared to historical trends, the system will flag this as a progression of the disease.
[0043] By processing these images in real-time and comparing them with previously stored data, the system can provide users with immediate, actionable insights. The AI-driven feedback is instantaneously displayed to the user on the device's interface, allowing for rapid decision-making. The continuous monitoring of changes in the retinal nerve fiber layer and optic nerve head over time is vital for detecting glaucoma early, as these changes can be subtle but are critical indicators of disease progression. This advanced AI system is designed to improve the accuracy and timeliness of glaucoma detection and allow for proactive management of the condition.
[0044] The process analysis a retinal images in real-time for early detection and continuous monitoring of glaucoma by focusing on retinal nerve fiber layer thickness and optic nerve head parameters. The artificial intelligence model, trained on a vast dataset of retinal images, allows the system to identify patterns associated with glaucoma, providing immediate feedback to the user. By continuously extracting and analyzing retinal nerve fiber layer thickness and optic nerve head diameter from each captured image, the system can detect subtle changes that are critical indicators of glaucoma progression. The integration between the optical sensors, imaging system, and artificial intelligence algorithms ensures seamless, real-time analysis with no delay in processing.
[0045] The threshold values, specifically the retinal nerve fiber layer thickness threshold of 85 micrometers and optic nerve head diameter threshold of 1.5 millimeters, are chosen based on clinical data that defines the normal range of these parameters in healthy individuals. These thresholds serve as baseline reference points for detecting deviations that might indicate early stages of glaucoma. If the retinal nerve fiber layer thickness or optic nerve head diameter deviates by more than 5 percent from the normal range or the user's historical data, the system flags these changes as potential indicators of disease progression. This threshold-based approach ensures that the artificial intelligence system is sensitive enough to detect even the smallest deviations, which are often imperceptible during a clinical examination but may signal the early onset of glaucoma.
[0046] By comparing the current retinal measurements to both predefined thresholds and historical data stored within the system, the algorithm can track the progression of the disease over time. This continuous monitoring enables early intervention, as the system not only analyzes the current state of the retina but also recognizes trends in the data. This dynamic and intelligent comparison process is crucial for the timely detection of glaucoma, allowing users to take prompt action before the disease progresses to an advanced stage. The use of these threshold values enhances the system's ability to provide reliable, high-fidelity diagnostic information, ensuring that even minute changes are detected, analyzed, and presented to the user in real-time.
[0047] The touchscreen interface is another critical component of the system, designed to facilitate easy navigation and user interaction. It provides a clear and intuitive display of the diagnostic results, guiding the user through the self-screening process with minimal input required. The interface is responsive to touch and allows the user to access various features of the device, including the option to transmit data to healthcare providers. The interaction between the artificial intelligence system and the touchscreen interface is integral to the user experience, as it allows the complex data analysis to be presented in a simplified format that can be easily understood by individuals without medical training. The user can view their glaucoma risk in a visual format, making the device not only a diagnostic tool but also an educational platform that empowers patients to take control of their health.
[0048] The interface is highly responsive and designed with precision to accommodate real-time feedback from the artificial intelligence system. As the user interacts with the device, the touchscreen guides them through each step of the self-screening process. The simplicity of the design ensures that even individuals with minimal experience in medical devices can easily follow the instructions, ensuring that the retinal scanning process is performed correctly and efficiently. This user-centric approach eliminates the need for specialized training, allowing users to independently assess their glaucoma risk. The touchscreen layout is structured to be accessible, with large, clearly labeled buttons and prompts that reduce the potential for error during operation.
[0049] One of the key aspects of the touchscreen interface is its integration with the device's artificial intelligence algorithms. Once the retinal images are captured and analyzed, the results are instantaneously displayed in a simplified, user-friendly format. The artificial intelligence system processes complex data, including retinal nerve fiber layer thickness and optic nerve head parameters, and translates this information into a clear visual representation of the user's glaucoma risk. This ensures that users, regardless of their medical background, can easily interpret the results. For example, the device may use color-coded indicators, graphs, or visual aids to depict whether the user's retinal parameters fall within normal ranges or if further medical attention is required. This visual clarity is essential for ensuring that the self-screening process is both informative and actionable.
[0050] The touchscreen interface also includes functionality that allows the user to securely transmit their diagnostic data to healthcare providers. This feature is seamlessly integrated into the system, enabling remote monitoring by clinicians. With a simple tap, users can share their results via encrypted wireless communication, ensuring that their personal health information remains confidential while still providing healthcare professionals with the data needed for ongoing care. This feature not only enhances the device's utility as a personal health tool but also positions it as part of a broader digital health ecosystem, facilitating continuous and collaborative glaucoma management.
[0051] In addition to displaying diagnostic results, the touchscreen interface serves as an educational platform, providing users with insights into their eye health. The device may offer educational prompts or explanations that help users understand the significance of the results, further empowering them to make informed decisions about their eye care. By integrating these educational features, the device transcends its role as a mere diagnostic tool, fostering greater patient engagement and encouraging proactive health management.
[0052] Wireless connectivity modules, including Wi-Fi and Bluetooth, play a vital role in the system's functionality, particularly in facilitating real-time data transfer. These modules are integrated with the processing unit to ensure that the diagnostic data can be securely transmitted to cloud-based storage systems. The interaction between the wireless connectivity components and the data encryption protocols is crucial to maintaining the security and privacy of patient data. The data transmission process is seamless, allowing healthcare providers to access the patient's diagnostic results remotely and intervene when necessary. This connectivity also ensures that the device can operate within a broader digital healthcare ecosystem, enabling integration with electronic health records and other medical platforms.
[0053] The rechargeable battery system is another fundamental component, providing the necessary power for all operations of the device. The battery is designed to support prolonged usage without frequent recharging, allowing for multiple screenings on a single charge. The interaction between the battery system and the device's power management unit ensures efficient energy consumption, with features such as automatic shut-off to prevent overheating. The power management unit is integrated with the wireless modules, ensuring that the device can remain operational even during extended data transmission sessions.
[0054] The structural components of the device, including the exterior casing and ergonomic design, ensure durability and ease of use. The materials chosen for the casing are lightweight yet robust, designed to withstand the rigors of frequent transport and handling. The interaction between the structural components and the internal hardware is carefully engineered to ensure that the device remains portable and easy to handle without compromising on performance. The external design also ensures that the components within the device, such as the optical system and processing unit, are well-protected from external damage, ensuring longevity and reliability.
[0055] The cloud integration and mobile app complement the device's functionality, offering a user-friendly interface with a minimalist dashboard. The app provides real-time updates on the user's eye health, scan results, and data history, with cloud-based storage ensuring secure backups. Data is encrypted and transmitted wirelessly through the Wi-Fi/Bluetooth module, and the app is compatible with both iOS and Android, syncing data after each scan. The device ensures patient privacy through robust encryption of all data stored on the device and transmitted to the cloud. Each patient's data is stored under a unique ID number, with access restricted to authorised healthcare providers. The software includes an automatic data wipe feature that activates after a pre-set number of failed login attempts, preventing unauthorised access. The secure data management system ensures patient data is protected, with encryption protocols embedded in the device's firmware. The system relies on AES-256 encryption chips installed on the motherboard to safeguard all data transmitted to the cloud or stored locally. Multi-factor authentication adds another layer of security, requiring a passcode or biometric input, such as a fingerprint, to access sensitive information.
[0056] The working process begins when the user activates the device via the touchscreen interface. This user-friendly interface serves as the primary medium for interaction, guiding the user through the self-screening process. The interface prompts the user to align their eye with the device's optical scanning probe, ensuring that the retinal nerve fiber layer and optic nerve head are properly positioned for imaging. The device's ergonomic design, along with real-time feedback from optical sensors, ensures that the user achieves proper alignment without requiring technical expertise. Once the eye is correctly positioned, the optical imaging system, consisting of a high-resolution lens and integrated optical sensors, captures detailed images of the retina. These images focus specifically on the retinal nerve fiber layer and optic nerve head, which are critical areas for glaucoma detection. The optical sensors continuously monitor the alignment and adjust the focus of the lens as needed, ensuring that high-quality images are captured even if minor shifts in the user's position occur. In addition to focus adjustments, the optical sensors dynamically adjust lighting and exposure settings, compensating for variations in ambient light to ensure that the images are clear and suitable for analysis.
[0057] After the images are captured, the data is instantly processed by the device's artificial intelligence system, which is trained on a vast dataset of retinal images. The system analyzes key parameters, such as retinal nerve fiber layer thickness and optic nerve head dimensions, to identify patterns associated with glaucoma. The artificial intelligence model compares these current measurements with predefined clinical thresholds, as well as the user's historical data stored in the system, to detect any deviations that could indicate the onset or progression of glaucoma. This real-time processing ensures that the user receives immediate feedback on their glaucoma risk. The diagnostic results are then displayed on the touchscreen interface in a simplified, easy-to-understand format. The interface may use visual aids, such as color-coded indicators or graphs, to represent whether the user's retinal measurements fall within normal ranges or if further medical attention is required. This visual representation allows users, even those without medical training, to interpret the results quickly and accurately. Additionally, the system provides educational prompts to help users understand the implications of the results, further empowering them to take proactive steps in managing their eye health.
[0058] In cases where the system detects a potential risk of glaucoma or disease progression, the user has the option to securely transmit the diagnostic data to their healthcare provider through wireless connectivity. This feature allows clinicians to remotely monitor the user's condition and intervene if necessary, making the device a critical tool not only for self-screening but also for ongoing, professional medical management. Throughout the process, the system continuously monitors key operational parameters, such as battery life, internal temperature, and data integrity, ensuring that the device functions smoothly and safely during the self-screening procedure. The touchscreen interface keeps the user informed of any system alerts, such as low battery or alignment issues, ensuring that the self-screening process is as smooth and efficient as possible.
[0059] Here's a detailed case study example where Sarah, a 55-year-old woman with a family history of glaucoma, is concerned about her eye health. Her ophthalmologist has recommended regular screenings but due to her busy schedule, she struggles to visit the clinic frequently. Sarah decides to use the Portable AI-Integrated Glaucoma Screening Device, a modern solution designed for convenient and accurate home-based screenings. Sarah starts by powering on the device using the power button. The internal systems activate, and the automatic lens cover retracts. The device performs a self-check to ensure all components are functioning properly. On the touchscreen interface, Sarah follows the setup wizard that guides her through the alignment instructions. The device prompts her to position her eye correctly in front of the retinal scanner.
[0060] Sarah receives guidance through the touchscreen and subtle vibrations from the feedback motor, helping her align her eye precisely with the scanner. The retinal scanner emits infrared light to illuminate her retina. The high-resolution camera captures detailed images of her retinal nerve fiber layer (RNFL) and optic nerve head (ONH). The captured images are then sent to the AI analysis module. The AI module processes the retinal images using advanced algorithms. These algorithms compare the captured data with a comprehensive database of glaucomatous patterns. The AI assesses key parameters like RNFL thickness and ONH morphology, generating a risk score indicating Sarah's likelihood of having glaucoma. The risk score is displayed on the device's touchscreen, providing Sarah with a clear summary of her glaucoma risk. She reviews the visual representation of her results and notes that her risk level is within the normal range. Simultaneously, the device uploads the data to a secure cloud-based system via Wi-Fi. Her ophthalmologist receives the results in real-time and confirms the absence of immediate concerns.
[0061] Throughout the session, the device's battery management system ensures consistent operation. The vibration feedback helps Sarah maintain proper eye alignment, enhancing image accuracy. The device provides alerts if the battery level is low, ensuring reliability during use. Sarah uses the accompanying mobile app to track her eye health progress over time. The app sends reminders for periodic screenings and allows her to share her results with her ophthalmologist for future evaluations. By utilising the Portable AI-Integrated Glaucoma Screening Device, Sarah benefits from convenient and accurate glaucoma monitoring without the need for frequent clinic visits. The device's advanced technology, combined with its user-friendly design, enables early detection and timely medical consultation, contributing to effective glaucoma management and peace of mind.
[0062] While there has been illustrated and described embodiments of the present invention, those of ordinary skill in the art, to be understood that various changes may be made to these embodiments without departing from the principles and spirit of the present invention, modifications, substitutions and modifications, the scope of the invention being indicated by the appended claims and their equivalents.



FIGURE DESCRIPTION
[0063] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate an exemplary embodiment and explain the disclosed embodiment together with the description. The left and rightmost digit(s) of a reference number identifies the figure in which the reference number first appears in the figures. The same numbers are used throughout the figures to reference like features and components. Some embodiments of the System and methods of an embodiment of the present subject matter are now described, by way of example only, and concerning the accompanying figures, in which

[0064] Figure 1 demonstrates Portable Artificial Intelligence-Integrated Device for Early Detection and Continuous Monitoring of Glaucoma with high-resolution optical lens at the front of the tapered probe, surrounded by optical sensors that adjust focus and lighting in real-time. The hypoallergenic silicone rim ensures user comfort and alignment during scanning. The touchscreen interface is centered on the device for navigation and displaying diagnostic results. The wireless communication module allows secure transmission of data to healthcare providers, while the rechargeable battery provides portability and long-term use. , Claims:
1. A portable artificial intelligence-integrated device for early detection and continuous monitoring of glaucoma, comprising:
a high-resolution optical imaging system including a retinal scanner and optical sensors, wherein said optical sensors are configured to continuously monitor the alignment of the device with a user's eye and adjust the focus and lighting conditions in real-time to capture detailed images of the retinal nerve fiber layer and optic nerve head;
an artificial intelligence system operatively connected to said optical imaging system, wherein said artificial intelligence system is trained on a dataset of retinal images and is configured to analyze retinal nerve fiber layer thickness and optic nerve head parameters for detecting patterns indicative of glaucoma;
a touchscreen interface configured to guide the user through the self-screeningprocess and display real-time feedback of glaucoma risk based on the analysis of said artificial intelligence system; and
a wireless communication module configured to transmit diagnostic data to healthcare providers for remote monitoring and further analysis, wherein said device is portable and designed for use in non-clinical environments.
2. The device as claimed in claim 1, wherein said optical sensors are further configured to dynamically adjust exposure and lighting intensity based on ambient light conditions, ensuring optimal image quality, and wherein said exposure is adjusted to maintain a brightness level within a threshold range of 0.5 to 1.0 on a normalized scale, and lighting intensity is adjusted when ambient light levels fall below 150 lux.
3. The device as claimed in claim 1, wherein said artificial intelligence system is further configured to compare the current retinal nerve fiber layer thickness and optic nerve head parameters with historical data stored in the device to monitor glaucoma progression over time, and wherein a threshold deviation of 5% or greater in retinal nerve fiber layer thickness or optic nerve head diameter from historical values triggers an alert for potential glaucoma progression.
4. The device as claimed in claim 1, wherein said touchscreen interface is configured to display diagnostic results using a visual representation system, including color-coded indicators and graphical aids, to allow for easy interpretation by users without medical training, and wherein said visual indicators are triggered when retinal nerve fiber layer thickness falls below a threshold of 85 micrometers or optic nerve head diameter exceeds a threshold of 1.5 millimeters.
5. The device as claimed in claim 1, wherein said wireless communication module is equipped with encryption protocols to ensure the secure transmission of personal health data to healthcare providers.
6. The device as claimed in claim 1, comprising a rechargeable power source and an automatic power management system configured to monitor battery life and adjust power consumption based on usage conditions to optimize device longevity during operation.
7. The device as claimed in claim 1, wherein said optical imaging system is designed with a hypoallergenic silicone rim surrounding the optical lens, ensuring comfortable and secure alignment with the user's eye while minimizing light interference during the scanning process.

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