Digestaid - Artificial Intelligence Development SA
Digestaid focuses on developing accurate deep learning solutions for detection of digestive lesions, aiming to revolutionize gastrointestinal practice with AI technologies across digestive and pancreatobiliary tracts, as well as functional tests. The company leverages a team of young and senior gastroenterologists and engineers to innovate in digestive healthcare.
Industries
Nr. of Employees
small (1-50)
Digestaid - Artificial Intelligence Development SA
Patents
Products
AI algorithm suite for digestive imaging
A suite of deep learning algorithms for detection, classification and scoring of gastrointestinal lesions across capsule endoscopy and endoscopic video modalities.
AI algorithm suite for digestive imaging
A suite of deep learning algorithms for detection, classification and scoring of gastrointestinal lesions across capsule endoscopy and endoscopic video modalities.
Services
Software-based analysis for capsule endoscopy videos providing automated detection and categorization of small-bowel and colonic lesions (ulcers, erosions, vascular lesions, protuberant lesions, blood).
Real-time computer vision module for intra-procedural support in endoscopic modalities (cholangioscopy, EUS, anoscopy, device-assisted enteroscopy) to identify suspicious features and assist visual diagnosis.
Software analysis of anorectal and high-resolution esophageal manometry recordings to extract relevant metrics and provide automated interpretations.
Software-based analysis for capsule endoscopy videos providing automated detection and categorization of small-bowel and colonic lesions (ulcers, erosions, vascular lesions, protuberant lesions, blood).
Real-time computer vision module for intra-procedural support in endoscopic modalities (cholangioscopy, EUS, anoscopy, device-assisted enteroscopy) to identify suspicious features and assist visual diagnosis.
Software analysis of anorectal and high-resolution esophageal manometry recordings to extract relevant metrics and provide automated interpretations.
Expertise Areas
- AI for digestive endoscopy
- Capsule endoscopy image analytics
- Computer vision for live endoscopy
- Clinical validation of diagnostic AI
Key Technologies
- Convolutional neural networks (CNNs)
- Deep learning for medical image analysis
- Real-time video inference
- High-throughput inference optimization
News & Updates
Our group shared research on deep learning algorithms for the automatic detection of features associated with inflammatory activity, with two studies selected for oral presentation.
Participated with 14 scientific contributions on AI applications in gastroenterology, including a pioneer study on differentiating mucinous from non-mucinous pancreatic cystic lesions, with one study selected for oral presentation.
Presented four scientific communications on AI in capsule endoscopy, including detection of ulcers, erosions, and blood in the gastrointestinal tract, with some works selected for oral communication and poster awards.
Presented research on deep learning for automatic identification and differentiation of small bowel lesions, with one work awarded as best oral communication.
Presented work on device-assisted enteroscopy and gastrointestinal lesion detection using convolutional neural networks, with two studies selected for oral presentation.
Coverage of DigestAID's participation and presentations at ECCO 2024.
Our group shared research on deep learning algorithms for the automatic detection of features associated with inflammatory activity, with two studies selected for oral presentation.
Participated with 14 scientific contributions on AI applications in gastroenterology, including a pioneer study on differentiating mucinous from non-mucinous pancreatic cystic lesions, with one study selected for oral presentation.
Presented four scientific communications on AI in capsule endoscopy, including detection of ulcers, erosions, and blood in the gastrointestinal tract, with some works selected for oral communication and poster awards.
Presented research on deep learning for automatic identification and differentiation of small bowel lesions, with one work awarded as best oral communication.
Presented work on device-assisted enteroscopy and gastrointestinal lesion detection using convolutional neural networks, with two studies selected for oral presentation.
Coverage of DigestAID's participation and presentations at ECCO 2024.