Projects

AI Assistant for Visually Impaired People

We will develop multiple smart phone applications to gain accessibility for visually impaired people.

Customized Ocular Software Solutions

At Biotransport Systems Developments, we specialize in developing customized ocular software solutions tailored to the unique needs of clinics. Our software enhances operational efficiency by streamlining workflows and reducing inefficiencies.

Multi-modal Image Processing and Analysis

Our Multi-Modal Image Processing and Analysis platform provides key insights into disease mechanisms and accelerates drug development by analyzing diverse imaging data. It empowers clinicians and researchers to develop more targeted and effective treatments. 1. Developing new biomarkers. 2. Feature enhancing for image database creation. 3. Highlight key pathologies on imaging data.

Postvitreal Detachment (PVD)

At Biotransport Systems Developments, we will evaluate the factors for high-risk patients in PVD (retinal tear). We will develop ML/DL methods to identify and determine pathological features in vitreo-retinal disorders to evaluate the quality of training datasets and simultaneously provide diagnoses for PVD status and the necessity of further examination and/or treatment. Such algorithms would be crucially important in predicting PVD occurrence and progression, so that timely and adequate treatment can be instituted.

Iridology: Big Scale Disease Detection

Harnessing advanced AI and image processing, Biotransport Systems Developments transforms iridology into a scalable diagnostic tool for early disease detection. Our platform enables non-invasive, accurate, and cost-effective healthcare solutions to enhance patient outcomes.

Dry Eye

This innovative work aims to revolutionize the diagnosis of dry eye syndrome by developing a cutting-edge method that effectively distinguishes healthy eyes from those affected by dryness by focusing on critical biomarkers such as tear break-up time, tear meniscus height, and meibomian gland structure. This way we can detect subtle structural differences that are often overlooked. The integration of DINOv2 embeddings with ALPNet unlocks the potential for few-shot segmentation, enhancing feature extraction and leading to accurate identification of dry eye disease.