OCCULOVISION: A WEARABLE, INTEGRATED DEVICE FOR EYE DISEASE PREDICTION AND DIAGNOSIS

Abstract

ABSTRACT “OCCULOVISION: A WEARABLE, INTEGRATED DEVICE FOR EYE DISEASE PREDICTION AND DIAGNOSIS” The present invention discloses a wearable integrated device for eye disease prediction and diagnosis: Occulovision. The device comprises a module 1, wherein; the module 1 further comprises; a lens arrangement, a secondary structures for mounting and positioning the eye, and an apparatus necessary for eye imaging; a module 2 wherein, the module 2 further comprises; an essential camera, a sensors, a microprocessors, and other controllers needed for image processing; and a module 3 wherein, the module 3 further comprises; a smartphone wherein, the smartphone in module 3 communicates with a cloud for Machine Learning (ML) analysis. The device: Occulovision makes eye care more accessible, improves diagnostic accuracy, and lowers the cost and complexity of traditional eye examination methods, making it an efficient and user-friendly solution for today's eye care.

Specification

Description:OCCULOVISION: AN INTEGRATED DEVICE FOR EYE DISEASE PREDICTION AND DIAGNOSIS
FIELD OF THE INVENTION
This invention relates to the field of ophthalmology, specifically to diagnostic devices and methods for eye disease prediction and diagnosis. Subfields include medical imaging, telehealth, artificial intelligence (AI), machine learning (ML), augmented reality (AR), and virtual reality (VR).
BACKGROUND OF THE INVENTION
Current eye examination techniques frequently rely on bulky, expensive equipment such as slit lamps, autorefractors, and tonometers. These tools, which are typically found in specialized eye clinics, are not only expensive but also require patients to visit these clinics, posing accessibility challenges, particularly for those living in remote areas or with limited mobility. For many patients, having to travel to a specialized clinic for comprehensive eye exams presents significant challenges, which are exacerbated by the time and cost involved. As a result, people in underserved areas may face delays in receiving necessary eye care and diagnosis.
Existing telehealth solutions for eye care are convenient, but they often lack the diagnostic capabilities available during in-person visits. While these telehealth solutions enable remote consultations, they frequently fall short of the comprehensiveness and accuracy of traditional eye exams performed with specialized equipment. This capability gap means that telehealth users may miss out on critical diagnostic information, resulting in suboptimal outcomes, especially for people with complex eye conditions that necessitate comprehensive evaluations.
Furthermore, traditional eye exams frequently require multiple separate tests, each performed with a different device. This not only raises the overall cost of eye care, but it also delays the examination process for the patient. Patients must go through a series of steps, including refraction tests, intraocular pressure measurements, and detailed retinal scans, all of which add to the time, cost, and inconvenience of the visit. For many patients, these disjointed procedures can be overwhelming, discouraging regular eye exams.
While portable diagnostic tools have emerged to meet the demand for more accessible eye care, they frequently lack the advanced capabilities needed for a complete, accurate diagnosis. These devices may perform basic assessments, but they do not offer comprehensive diagnostics or advanced features such as AI-driven analysis. Furthermore, they typically lack the ability to correct lens prescriptions in real time, limiting their usefulness for comprehensive eye care. These limitations prevent portable tools from providing the same diagnostic depth as traditional in-clinic equipment.
Occulovision addresses these shortcomings by offering a portable, all-in-one solution that combines advanced technologies to perform a thorough eye examination and diagnosis. This device streamlines the process by combining AI-driven analysis, real-time remote diagnostics, and the ability to correct lens prescriptions. It provides a highly accurate and efficient alternative to traditional methods. Occulovision not only improves access to eye care for people in remote or underserved areas, but it also lowers the cost and time associated with traditional eye exams, making it a practical and dependable tool for overall eye health management.
OBJECT OF THE INVENTION
The principal object of the present invention is to develop a wearable integrated device for eye disease prediction and diagnosis.
Another object of the present invention is to develop a wearable integrated device for eye disease prediction which is easy to transport and use, making it ideal for mobile eye care units, rural clinics, and home visits.

SUMMARY OF THE INVENTION
The present invention discloses a wearable integrated device for eye disease prediction and diagnosis.
In aspect of the invention the device comprises; a module 1, wherein; the module 1 further comprises; a lens arrangement, a secondary structures for mounting and positioning the eye, and an apparatus necessary for eye imaging; a module 2 wherein, the module 2 further comprises; an essential camera, a sensors, a microprocessors, and other controllers needed for image processing; and a module 3 wherein, the module 3 further comprises; a smartphone wherein, the smartphone in module 3 communicates with a cloud for Machine Learning (ML) analysis.
BREIF DESCRIPTION OF DRAWING
Figure 1: Shows sample of the Image Dataset Used for Model Creation
Figure 2: Shows flowchart of the device
Figure 3: Shows the modular bifurcation of modules 1, 2 and 3
Figure 4: Shows side view of assembled Occulo-Vision Headset
DETAILED DESCRIPTION OF THE INVENTION
Various modifications and alternative forms, specific embodiment thereof have been shown by way of example in the figures and will be described below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure.
The terms "comprises", "comprising", or any other variations thereof used in the disclosure, are intended to cover a non-exclusive inclusion, such that a device, system, assembly that comprises a list of components does not include only those components but may include other components not expressly listed or inherent to such system, or assembly, or device. In other words, one or more elements in a system or device proceeded by "comprises… a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or device.
It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "includes but is not limited to," etc.).
The present invention discloses a wearable integrated device for eye disease prediction and diagnosis.
In aspect of the invention the device comprises; a module 1, wherein; the module 1 further comprises; a lens arrangement, a secondary structures for mounting and positioning the eye, and an apparatus necessary for eye imaging; a module 2 wherein, the module 2 further comprises; an essential camera, a sensors, a microprocessors, and other controllers needed for image processing; and a module 3 wherein, the module 3 further comprises; a smartphone wherein, the smartphone in module 3 communicates with a cloud for Machine Learning (ML) analysis.
In an embodiment of the aspect disclosed, wherein, the machine learning analysis is performed by a pre-trained Convolutional Neural Network (CNN), specifically the VGG19 model, optimized for image classification and medical diagnostics.
In another embodiment of the aspect disclosed, wherein, the module 2 is connected with a head strap and an ear piece.
Present invention discloses, Occulovision; is a comprehensive, portable eye diagnostic system made up of three interconnected modules, each of which is critical to its functionality and performance. Module 1 houses the optical system, which consists of specialized lenses and positioning mechanisms. These lenses are designed to capture precise high-resolution images of the eye, including the retina, cornea, and other key eye structures. The positioning mechanisms keep the lens system in proper alignment with the patient's eye, resulting in stable and accurate image acquisition. This setup is designed for ease of use and accuracy, eliminating the need for large, stationary equipment.
Module 2 houses the core hardware for image processing and analysis. Module 2 includes the essential camera, sensors, microprocessors, and other controllers needed for image processing. This module processes the raw images captured by Module 1 and prepares them for analysis by sending the data to Module 3. The sensors are particularly important for adjusting the orientation and alignment of the eye, ensuring high-quality, layer-by-layer imaging.
The Occulovision system's core functionality is based on advanced AI algorithms that are trained on a large dataset of eye images to detect and diagnose a wide range of eye diseases. These AI algorithms examine captured images for signs of conditions such as glaucoma, cataracts, macular degeneration, diabetic retinopathy, and others. The AI system uses machine learning to continuously improve its accuracy and reliability over time, allowing it to provide quick and accurate diagnoses. This AI-driven diagnostic capability distinguishes Occulovision from traditional diagnostic methods, enabling early detection and timely intervention in eye diseases.
Module 3 includes the connected smartphone, which serves as the device's user interface, computing platform, and communication hub. The smartphone acts as the central hub for computing, interfacing, and powering the system. It communicates with the cloud for Machine Learning (ML) analysis, processes the live feed for predictions, and sends data to doctors for real-time assessments. This module provides connectivity between the other two modules and integrates drivers and software required for the system's proper functioning.
One of Occulovision's distinguishing features is its ability to transmit diagnostic data in real time to remote specialists via the connected smartphone. This capability enables specialists to remotely assess a patient's condition, making the system particularly useful in telemedicine applications. Healthcare providers can collaborate, monitor patient progress, and provide timely interventions regardless of where the patient is. This remote diagnostic capability improves access to quality eye care, particularly for those living in underserved or rural areas.
The invention incorporates a Deep Learning model based on a pre-trained Convolutional Neural Network (CNN), specifically the VGG19 model, which is optimized for image classification and medical diagnostics. The solution leverages a large dataset of eye images obtained from open-source platforms like Kaggle, which includes both normal and diseased eye samples (Figure1) (e.g., cataracts and glaucoma).
The CNN model extracts meaningful features from the images, such as edges and textures, which help in identifying patterns related to eye diseases. The model uses 16 convolutional layers in VGG19, with pooling and ReLU (Rectified Linear Unit) activation layers that aid in both data processing and feature selection. Pooling layers reduce the spatial size of the image data, allowing for more concentrated and meaningful chunks of information to be analyzed. Activation layers further enhance the learning capability of the model, enabling it to understand complex relationships between the input image and the predicted output.
Figure 2, The workflow diagram provides an overview of how the individual modules interact with each other to form a complete diagnostic system. Each segment Module 1 for imaging, Module 2 for image processing, and Module 3 for computation and communication operates as a functional unit, contributing to the overall performance of the system.
Figure 3, This diagram illustrates the specific components and their roles within each module. Module 1 focuses on lens and imaging components, Module 2 includes camera and sensor systems for image processing, and Module 3 represents the smartphone interface that facilitates real-time analysis and communication with the cloud.
Figure 4, This figure depicts the assembled device, showing how the patient wears the headset. The image processing and diagnostic system is fully integrated into a wearable form factor, making it accessible and easy to use.
In conclusion, the invention offers a robust solution for the early detection of eye-related disorders using a combination of deep learning algorithms and an innovative, modular device. By addressing challenges in medical imaging, particularly for the eye, and providing real-time diagnostics and communication capabilities, the invention can significantly improve patient outcomes through early intervention.
The comprehensive approach of the present invention; Occulovision, which incorporates AI, ML, AR, and VR within a single portable device, distinguishes it from these conventional methods, offering a significant technological advancement in eye care. Other solutions include traditional eye diagnostic tools like slit lamps and autorefractors, which are found in clinical settings. These devices are bulky and cannot be used outside specialized facilities. Other portable solutions may lack the comprehensive capabilities of Occulovision, such as the integration of AI for automated diagnosis and AR/VR for corrective lens suggestions.
Occulovision's design ensures numerous advantages over existing eye diagnostic tools. By making diagnostics more accessible, it alleviates the burden of traveling for eye exams, especially in areas where eye care services are limited. The AI-driven analysis provides fast and accurate diagnostic results, allowing for timely intervention when necessary. Its ability to function as both a lens correction tool and a diagnostic device for complex diseases makes it an all-encompassing solution. Moreover, by replacing multiple traditional tools with a single device, Occulovision presents a cost-effective option for eye diagnostics. The device stands out due to its all-in-one approach, combining multiple diagnostic capabilities in a single portable device. Unlike conventional tools, it leverages AI, ML, AR, and VR to provide real-time, automated analysis and remote diagnostic features. This makes it distinct from traditional eye diagnostic equipment, which typically lacks remote connectivity and automated diagnostic capabilities.
Occulovision's modular design allows for easy transport and use, making it ideal for mobile eye care units, rural clinics, and home visits. The combination of high-quality imaging, AI-powered analysis, and real-time remote diagnostics provides a complete, all-in-one solution for modern eye care. Occulovision eliminates the need for multiple devices and complex setups, providing a simple, cost-effective, and efficient way to diagnose and monitor eye health. This invention, which incorporates advanced technologies, makes cutting-edge eye care more accessible to more patients, promoting early detection, better treatment outcomes, and greater access to healthcare services.
Although embodiments for the present subject matter have been described in language specific to structural features, it is to be understood that the present subject matter is not necessarily limited to the specific features described. Rather, the specific features and methods are disclosed as embodiments for the present subject matter. Numerous modifications and adaptations of the system/component of the present invention will be apparent to those skilled in the art, and thus it is intended by the appended claims to cover all such modifications and adaptations which fall within the scope of the present subject matter.
, Claims:We claim;
1. A wearable integrated device for eye disease prediction and diagnosis comprising;
a) a module 1, wherein; the module 1 further comprises; a lens arrangement, a secondary structures for mounting and positioning the eye, and an apparatus necessary for eye imaging;
b) a module 2 wherein, the module 2 further comprises; an essential camera, a sensors, a microprocessors, and other controllers needed for image processing; and
c) a module 3 wherein, the module 3 further comprises; a smartphone
wherein, the smartphone in module 3 communicates with a cloud for Machine Learning (ML) analysis.
2. The device as claimed in claim 1 wherein, the machine learning analysis is performed by a pre-trained Convolutional Neural Network (CNN), specifically the VGG19 model, optimized for image classification and medical diagnostics.
3. The device as claimed in claim 1 wherein, the module 2 is connected with a head strap and an ear piece.

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