MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202641091373 A) filed by Akshaya College Of Engineering And Technology on July 28, 2026, for Ai – Powered Dry Eye Disease Detection.
Inventors include Dr. V. Nivedhitha; Neela Yaswanth; Perla Mahendra Reddy; Sirivela Madhava; and Aravindh Srinivasan.
The application for the patent was published on July 31, 2026, under issue no. 31/2026.
Abstract: ABSTRACT AI – POWERED DRY EYE DISEASE DETECTION Dry Eye Disease (DED) is a common ocular condition that affects a large population and can lead to discomfort, irritation, and vision-related problems if not detected at an early stage. Traditional diagnostic methods rely heavily on clinical tests and manual evaluation by ophthalmologists, which are time-consuming, subjective, and may lead to inconsistent results. To overcome these limitations, this project proposes an AI-based system for the detection of dry eye disease using multiple sources of data. The proposed system integrates machine learning and deep learning techniques to analyze symptom-based data, eye images, and blink patterns. A Multi-Layer Perceptron (MLP) model is used for analyzing structured symptom data, while MobileNet is employed for efficient image classification. Additionally, the VGG-19 model is utilized for detecting eye blink patterns, which serve as an important indicator of dry eye disease. The combination of these models enhances the overall accuracy and reliability of the system. The system is implemented using Python and various libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, and OpenCV. A user-friendly interface is developed using Streamlit, allowing users to input data and view results easily. The system processes the input data through preprocessing, feature extraction, model training, and prediction stages to generate accurate outputs. Experimental results demonstrate that the proposed system is capable of effectively detecting dry eye disease with improved accuracy compared to traditional methods. The integration of multiple data sources provides a comprehensive analysis, reducing dependency on manual diagnosis and minimizing human error. In conclusion, the proposed AI-based system offers an efficient, reliable, and scalable solution for early detection of dry eye disease and has potential applications in healthcare, telemedicine, and remote monitoring systems
Disclaimer: Curated by HT Syndication.
