OptiPredict: Innovative Eye Health Monitoring System Using Deep Learning

Abstract

The current research offers a low-cost, hand-held, and automatic ocular disease detection system called "OptiPredict" that combines hardware with deep learning technology to present high-quality nonmydriatic retinal imaging and analysis. For the prototype fundus camera, the authors use the following: a disposable 20-diopter lens; a Raspberry Pi; a hand-held infrared-sensitive camera; and dual infrared and white light LEDs. It measures 133mm × 91mm × 45mm and weighs only 386 grams. It costs ₹15,400. The best part is that high-quality retinal imaging is possible without pupil dilation. The deep learning-based diagnostic system is optimized for the process of processing fundus images, based on such hardware. This system consists of a module of multilevel classification, using pre-trained models, which include VGG-19, ResNet-50, and Vision Transformer specifically for the type of ocular features for the concerned eye conditions that can lead to better accuracy in classification. Enhanced diagnosis is achieved with feature extraction using LBP through analyzing subtle texture variation and diagnosing between healthy and pathological conditions. Our binary classification framework classifies images as "normal" or "abnormal," ensuring that reliable detection is achieved for all forms of ocular diseases. Together with the cloud deployment, "OptiPredict" enables real-time diagnostic functionality, scalable to extend over different healthcare settings-remote and under-resourced areas. An optional predictive analytics module supplies risk assessment scores based on historical patient data supporting early intervention and tracking of disease progression. This research is a high portability, cost-effective advanced image-based classification solution that will comprehensively address all key challenges in ocular diagnostics. It holds great potential for enhancing timely and accurate access to ocular health care in underserved areas and mobile health care settings.

Specification

Description:The present invention, titled "OptiPredict," introduces an affordable, portable, and automated system for ocular disease detection by integrating advanced hardware and deep learning technology. The system provides high-quality, non-mydriatic retinal imaging and analysis to deliver effective diagnostic solutions in diverse healthcare settings, especially in remote and under-resourced areas.
The system comprises multiple interconnected modules, each labelled for ease of reference. These modules include an Input Module (101), Data Collection and Preprocessing Module (102), Feature Extraction Module (103), Classification Module (104), Cloud-Based Deployment Module (105), Predictive Analytics Module (106), Security and User Interface Modules (107), and Continuous Learning Module (108). Each module's purpose and configuration are detailed below.
Input Module (101): This module incorporates the retinal image capturing hardware and data input functionalities. It combines a compact and lightweight fundus camera, comprising a Raspberry Pi, an infrared-sensitive camera, dual infrared and white light LEDs, and a disposable 20-diopter lens. The device, measuring 133mm × 91mm × 45mm and weighing 386 grams, fits easily into a white coat pocket, providing high-quality retinal imaging without the need for pupil dilation. The images captured by this hardware serve as the primary input for disease detection. The Input Module (101) also integrates patient data, such as age, gender, and medical history (e.g., smoking status, diabetes). The system's user interface ensures compatibility with both web and mobile platforms, allowing easy data entry and access for healthcare professionals.
Data Collection and Preprocessing Module (102): This module processes raw input data to prepare it for analysis, consisting of several preprocessing stages: (i) image normalization to adjust pixel intensity values, creating uniformity across devices; (ii) contrast enhancement to improve feature visibility; (iii) artifact removal to remove visual obstructions; (iv) image resizing and cropping to standardize dimensions and remove irrelevant parts; and (v) segmentation and Local Binary Pattern (LBP) transformation, which segments and converts images into LBP format, emphasizing texture patterns critical for accurate classification. These steps enhance model robustness and ensure consistent performance across datasets.
Feature Extraction Module (103): This module uses LBP to capture detailed texture information from the pre-processed fundus images, which is crucial for distinguishing between healthy and abnormal retinal images. LBP transformation focuses on capturing variations in the retinal structure, which are indicative of potential ocular diseases.
Classification Module (104): Leveraging pre-trained deep learning models like VGG-19, ResNet-50, and Vision Transformer, the classification module categorizes the retinal images as either "normal" or "abnormal." This binary classification framework uses a multi-tiered ensemble approach, with each model contributing its strength to improve accuracy, even with imbalanced datasets.
Predictive Analytics Module (105): This module provides risk assessment scores and predictive insights, utilizing historical patient data and current diagnostic results to forecast disease progression. The analytics support early intervention by providing clinical recommendations based on the patient's specific risk factors.
Cloud-Based Deployment Module (106): This module enables real-time processing and cloud-based hosting, facilitating access to the system across remote geographic locations. It supports auto-scaling functionality to adjust processing power based on demand, ensuring smooth integration with healthcare workflows, especially in resource-constrained settings.
Security and User Interface Modules (107): To ensure patient data privacy, the system includes end-to-end encryption, data anonymization, and access control. These features comply with global standards (e.g., GDPR, HIPAA). The user interface offers easy image upload, result display, and tracking functionalities for both clinicians and patients across desktop and mobile devices.
Continuous Learning Module (108): This module allows the system to adapt to new data by automatically retraining its deep learning models, ensuring continuous improvement and maintaining accuracy as new disease patterns emerge.
Alternative Embodiments:
While the system described represents the primary embodiment, alternative versions may incorporate additional deep learning models, modified preprocessing techniques, or cloud-based setups to meet the needs of specific healthcare environments or regional constraints.

Working Example:
In a typical application, a fundus image is captured by the Input Module (101) and input into the system with other inputs . The image undergoes preprocessing in the Data Collection and Preprocessing Module (102), where it is normalized, contrast-enhanced, and segmented. The Feature Extraction Module (103) then extracts relevant features, and the Classification Module (104) categorizes the image as "normal" or "abnormal." If abnormalities are detected, the Predictive Analytics Module (106) generates a risk assessment and clinical recommendations.
, Claims:1. An Automated Ocular Disease Detection System, comprising:
• A portable fundus camera (101) designed to provide non-mydriatic retinal imaging using a Raspberry Pi-based device, incorporating an infrared-sensitive camera, dual infrared and white light LEDs, and a disposable 20-diopter lens, with a compact form factor measuring 133mm × 91mm × 45mm and weighing 386 grams, enabling convenient and high-quality retinal imaging without pupil dilation.
• A classification module (104) utilizing pre-trained deep learning models including VGG-19, ResNet-50, and Vision Transformer, wherein each model is specifically configured to analyze fundus images for detection of ocular diseases by identifying unique features within the retinal images, improving diagnostic accuracy for various eye conditions.
• A feature extraction module based on Local Binary Patterns (LBP)(103), wherein the LBP technique is used to capture and quantify intricate textural variations in fundus images, thus enhancing the system's ability to accurately differentiate between healthy and pathological ocular states.
• A cloud-based deployment architecture (106), configured to support real-time diagnostic processing and scalability, enabling the system to provide diagnostic services across diverse clinical environments, including remote and under-resourced healthcare settings.

2. A Method for Ocular Disease Detection from Fundus Images, comprising:
• Preprocessing steps, including normalization, scaling of pixel values, and data augmentation techniques (such as rotation, flipping, and brightness adjustment), aimed at enhancing the generalization ability of the deep learning model.(102)
• Texture feature extraction through Local Binary Patterns (LBP), wherein LBP effectively captures subtle textural differences in fundus images, aiding in the accurate recognition of ocular diseases.
• Deep learning-based classification(105) using VGG-19, ResNet-50, and Vision Transformer models within a binary classification framework designed to handle dataset imbalances by assigning weighted significance to underrepresented class es, ensuring robust classification performance. Deployment on a cloud-based infrastructure, providing real-time processing and scalable access to diagnostic capabilities, extending to both metropolitan and remote healthcare settings.

3. The System of Claim 1, wherein the Fundus Camera Integrates a Raspberry Pi module, infrared-sensitive camera, and dual LED lighting to deliver high-quality retinal images in a portable and cost-effective design, making it feasible for use in mobile healthcare environments, particularly in underserved areas.Is optimized for non-mydriatic imaging, allowing retinal imaging without the need for pupil dilation, thereby improving patient comfort and reducing preparation time in clinical settings.

4. The System of Claim 1, further comprising An optional predictive analytics module, designed to utilize historical patient data and fundus image analysis to generate risk assessment scores, allowing healthcare providers to track disease progression and recommend early clinical interventions based on detected trends and patterns.

5. The System of Claim 1, wherein the Cloud-Based Deployment Architecture: Includes auto-scaling capabilities that adjust processing power in real-time based on diagnostic demand, ensuring consistent throughput and efficient performance, even in environments with fluctuating workloads or large-scale healthcare institutions.

6. The System of Claim 1, further comprising Security and data protection mechanisms, including end-to-end encryption of communications between the user interface and backend infrastructure, ensuring compliance with data protection regulations (such as GDPR and HIPAA) to safeguard patient privacy and secure diagnostic data.

7. The System of Claim 1, further comprising An interactive user interface, designed to allow easy uploading of fundus images by patients or healthcare professionals, displaying diagnostic results and treatment recommendations, and enabling disease progression tracking. The interface is optimized for compatibility with both mobile and desktop platforms, enhancing accessibility for diverse users.

8. The System of Claim 1, further comprising A continuous learning module, wherein the deep learning models are automatically retrained based on new patient data, ensuring that the system's diagnostic accuracy and predictive capabilities remain up-to-date and continuously improve in response to evolving ocular disease patterns.

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