BioTransport Systems Development
BioTransport Systems Development offers an AI-powered screening tool that analyzes a single eye photo to assess diabetes risk in under 60 seconds, without needles or lab tests. The platform provides results as low, moderate, or high risk levels and supports periodic monitoring for both patients and clinicians. Users can access the free tool from any device, with anonymized image data used to refine the underlying algorithms.
- Healthcare Technology
- Digital Health
- Artificial Intelligence
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Traditional diabetes screening typically requires blood tests, fasting, lab visits, and clinical appointments, creating barriers related to cost, transportation, time, and aversion to needles. These obstacles lead to delayed detection and missed screening opportunities, particularly for individuals with limited access to healthcare facilities.
Solution
BioTransport Systems Development provides an AI-powered tool that screens for diabetes risk non-invasively using a simple photograph of the eye. Users upload a clear photo of their left or right eye, answer a few basic questions, and receive a risk assessment in under 60 seconds—no needles, fasting, or laboratory visits required. The platform reports 95% accuracy in detecting early and late diabetes signs and presents results as low, moderate, or high risk levels, with guidance to follow up with a doctor for higher-risk results. The tool is designed for periodic use to track changes over time and is useful for both people with and without diabetes, while the company collects anonymized image data and user feedback to refine its machine-learning models.
Target Audience
Primary users are individuals seeking convenient, needle-free diabetes risk screening at home, as well as clinicians and patients who want to monitor risk changes over time. The tool also serves public health programs and researchers interested in large-scale, low-cost screening approaches.
Features
- AI-powered ocular image analysis for diabetes risk detection with 95% reported accuracy
- Results delivered in under 60 seconds with clear low, moderate, or high risk level indicators
- Fully at-home workflow with no blood draws, fasting, or clinical visits required
- Free and instant risk assessments accessible via web interface on any device
- Anonymized image handling with no direct identifiers stored, ensuring user privacy
- Feedback and outcomes collection mechanism to continuously improve diagnostic algorithms
- Companion research into Physics-Informed Neural Networks (PINN) for modeling intravitreal drug transport in ocular disease treatment