Problem / Objective

Traditional eye disease diagnosis is a time-consuming and costly process, often requiring specialist consultations. In regions with limited healthcare access, diagnosing conditions like diabetic retinopathy and hypertension-related eye diseases can be challenging, delaying early intervention and treatment.

Solution / Approach

The AI-powered eye disease detection system utilizes a deep learning model to classify eye images and detect diseases related to diabetes and hypertension. The system is designed to be user-friendly, with a React-based frontend that allows users to upload eye images for analysis. The TensorFlow model, deployed on a FastAPI server, processes these images and provides predictions on whether the user has conditions like diabetic retinopathy or other hypertension-related eye issues. The system's pre-trained model is based on a large dataset of labeled images sourced from online repositories, hospitals, and clinics. These images include both healthy eyes and eyes affected by various diseases, ensuring that the model can accurately differentiate between different conditions.

Impact / Implementation

This AI-based system has the potential to revolutionize eye disease diagnosis, making it faster, more affordable, and more accessible, particularly in areas with limited access to healthcare services. By automating the diagnostic process, the system reduces the burden on healthcare professionals, allowing them to focus on more complex cases while routine diagnoses are handled efficiently by the AI model.

Early detection of diseases such as diabetic retinopathy and hypertension-induced eye conditions is critical to preventing severe outcomes like vision loss. The system empowers users to get an early diagnosis, enabling them to seek timely medical intervention. This is especially beneficial for patients in rural or underserved areas, where access to specialists is limited. Additionally, the system’s use of a large, diverse dataset ensures it can provide accurate results for different types of eye diseases, improving overall diagnostic accuracy. In the long run, this AI-powered solution could significantly reduce the healthcare costs associated with traditional diagnostic methods, while improving patient outcomes through early intervention and treatment.

https://fxis.ai/