Endoscopic ultrasound · Upper-GI subepithelial lesions
Find the lesion. Differentiate with clarity.
AI-assisted identification and differentiation of upper gastrointestinal subepithelial lesions, distinguishing leiomyoma from gastrointestinal stromal tumors in live and previously acquired EUS images.
01 · Intended use
AI assistance for upper‑GI subepithelial lesions.
Sonomind® is a deep-learning technology developed to identify and differentiate subepithelial lesions of the upper gastrointestinal tract during endoscopic ultrasound.
It distinguishes leiomyoma from gastrointestinal stromal tumors in live and acquired EUS images, supports targeted sampling and generates standardized AI-driven reports.
The clinical challenge
Different lesions can look remarkably similar on EUS.
Leiomyomas and gastrointestinal stromal tumors may share overlapping sonographic characteristics. Clear differentiation can guide the need, location and urgency of tissue sampling.
“The critical step is converting a subtle subepithelial image into a focused diagnostic pathway.”
How it works
One continuous path from ultrasound to targeted sampling.
Sonomind® supports the EUS workflow while keeping sampling strategy and final interpretation in expert hands.
EUS acquisition
Live or previously acquired endoscopic ultrasound images enter the analysis workflow.
Lesion localization
The AI identifies an upper-GI subepithelial lesion and tracks it across EUS frames.
Layer and texture analysis
The model assesses lesion boundaries, echogenic texture and layer of origin.
Lesion differentiation
The finding is differentiated between leiomyoma and gastrointestinal stromal tumor.
Report and sampling
Bounding-box guidance and a standardized AI report support targeted tissue sampling.
AI capabilities
Designed for focused subepithelial‑lesion assessment.
Diagnostic accuracy presented for Sonomind®
Subepithelial-lesion identification
Automatically identifies upper-GI subepithelial lesions in endoscopic ultrasound images.
Leiomyoma–GIST differentiation
Analyzes sonographic patterns to assist distinction between leiomyoma and gastrointestinal stromal tumors.
Real-time and retrospective analysis
Works during live EUS and with previously acquired examinations.
Sampling-target guidance
Places bounding boxes around relevant tissue to support precise sampling.
Standardized AI reporting
Generates a structured AI-driven diagnostic report for expert validation.
Scientific validation
A multicentric transatlantic EUS study.
Deep Learning for Detection and Differentiation of Subepithelial Lesions: A Multicentric Transatlantic Endoscopic Ultrasound Study
American College of Gastroenterology Annual Scientific Meeting · 2025
View study presentation ↗Clinical benefits
Earlier clarity. Better‑targeted sampling.
Earlier and more accurate diagnosis of subepithelial lesions
↗Fewer unnecessary procedures through more focused decision-making
↗Target selection for sampling with bounding-box guidance
↗Real-time and post-procedural EUS analysis in one workflow
↗Standardized AI-driven diagnostic reporting
↗Sonomind®
See beneath the surface. Differentiate with clarity.
Talk to our team ↗This technology is part of DigestAID's research and development pipeline. Availability and regulatory status may vary by market.