DigestAID develops deep learning solutions for the automatic detection of digestive lesions in endoscopic procedures, enhancing diagnostic accuracy in gastrointestinal healthcare. Their technology addresses the challenge of accurately identifying lesions in the digestive and pancreatobiliary tracts, improving patient outcomes through non-invasive examination methods.
Funding
$520K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Endoscopic procedures for detecting digestive lesions are often limited by the accuracy of human observation, leading to potential misses and delayed diagnoses. The complexity of visual data in the digestive and pancreatobiliary tracts makes it challenging to identify subtle lesions consistently.
Solution
DigestAID develops deep-learning solutions designed to automatically detect digestive lesions during endoscopic procedures, thereby enhancing diagnostic accuracy and improving patient outcomes. Their AI-powered technologies analyze endoscopic video feeds in real-time, identifying potential lesions in both the digestive and pancreatobiliary tracts, as well as in functional tests. DigestAID offers a suite of tools for various endoscopic modalities, including capsule endoscopy, cholangioscopy, endoscopic ultrasound, and high-resolution anoscopy. By providing clinicians with AI-driven insights, DigestAID aims to reduce the risk of overlooking clinically relevant lesions, leading to faster diagnoses and more effective treatment plans.
Target Audience
DigestAID's primary customers are gastroenterologists, endoscopists, and other medical professionals who perform or interpret endoscopic procedures for the diagnosis and management of digestive diseases.
Features
- AI-powered detection of lesions in capsule endoscopy, cholangioscopy, and endoscopic ultrasound
- Automatic stratification of the hemorrhagic potential of lesions
- Real-time analysis of endoscopic video feeds
- Detection and classification of ulcers and erosions in the small bowel and colon
- Optimized detection and characterization of biliary strictures
- Differentiation of mucinous and non-mucinous cystic lesions during EUS
- Detection and differentiation of anorectal motility patterns
- Automatic identification, classification, and reporting of esophageal manometry according to the Chicago 4.0 classification