Pre-training scale · videos
Largest endoscopy video dataset in the literature
Dermyer et al. 2025
Wang et al. 2023
Yao et al. 2023
Chaitanya et al. 2024
Byrne et al. 2025
Foundation models for endoscopy
Episode 39 — Interview with Matt Schwartz
Virgo & Rajpurkar Lab partner on next-gen endoscopy AI foundation model
Latest conversation with Matt Schwartz
Foundation Model Platforms & APIs Market Map
Building foundation models for endoscopy
AI in Endoscopy — Market Report
Inside Virgo's research roadmap
Virgo launches EndoML at DDW 2025
AI in Endoscopy — turning lost video into insight
Virgo launches AI-powered EndoML platform
Countdown to AI super-intelligence in GI
Meet EndoDINO — a SOTA foundation model for endoscopy
Foundation Model Series — Advancing Endoscopy
Virgo launches EndoML powered by EndoDINO
AGA Innovation conversation
EndoDINO — paper
Founding Virgo
The future of endoscopy data
EndoDINO
Pre-training scale · videos
Dermyer et al. 2025
Wang et al. 2023
Yao et al. 2023
Chaitanya et al. 2024
Byrne et al. 2025
Pre-training scale · images
EndoDINO (Virgo)
Dermyer et al. 2025
ArgesFM (J&J)
Chaitanya et al. 2024
GastroNet-5M
Jong et al. 2026
Etro (Roche)
Yao et al. 2023
Why endoscopy
Tap a marker to explore
Estimates from WHO, IARC GLOBOCAN, CDC, and peer-reviewed literature.
Validation
HyperKvasir · 3-class Mayo Endoscopic Scoring
Dermyer et al. 2025
Oquab et al., Meta AI 2024
Huang et al., CVPR 2017
Yao et al. 2023
Wang et al., MICCAI 2023
Macro F1, linear probe on frozen backbone. Comparator values from each model's original publication; see chart for citations.
UNIFI Phase 3 · Ustekinumab in UC
Placebo arm
Treatment arm
AUROC, 5-fold CV. EndoDINO video embeddings vs. 21 standard UC clinical covariates. Data presented at UEGW 2025.
148
US medical centers
3M+
US procedures
46.6%
non-White representation
0.713
Shannon diversity index
4-class Mayo Endoscopic Scoring
EndoDINO ViT-g/14 delivers leading performance on Mayo endoscopic scoring with a frozen backbone.
Mayo 0
Normal or inactive
disease
Mayo 1
Mild
disease
Mayo 2
Moderate
disease
Mayo 3
Severe
disease
Frame-level predictions aggregated per procedure.
Scored by EndoDINO.
Validated in head-to-head comparison with 3rd party models
How EndoDINO learns
One model. Every downstream task: scoring, detection, prediction, biomarker discovery.
01
Raw endoscopy video from the procedure stream.
02
Frames extracted, deduplicated, and curated for optimal distribution.
03
Population-scale pretraining on unlabeled endoscopy images.
04
A reusable embedding for any downstream task.
Capabilities
Proof point · UNIFI, Phase 3 UC
Saved
Avoided
Virgo presented data at UEGW 2025 showing that EndoDINO was capable of predicting placebo responders from baseline colonoscopy videos. If the UNIFI trial had incorporated this in its trial design, the trial could have reached the same readout faster and at lower cost.
Control for placebo variability. Reduce trial size and increase statistical powering.
Precision enrichment: identify likely responders from AI analysis of baseline endoscopy.
Generate powerful real-world data priors using EndoDINO at population scale.
UC and CD endoscopic healing assessment beyond ordinal Mayo and SES-CD categories.
The data moat
Capture is the foundation of everything downstream. Real-world endoscopy video (at population scale, longitudinal, and continuously growing) is what makes a foundation model for GI possible. Models built on smaller datasets plateau. Ours compound.
The platform
Foundation model
01Virgo's foundation model for endoscopy. One model base for scoring, prediction, detection, and biomarker work, trained on the full procedure, not just the frame.
Build environment
02The environment for building on top of EndoDINO. A GI-specific model layer for clinical and research workflows.
Request access
Manuscript, UEGW 2025 poster, benchmark results, and partnership models. Sent directly to qualified researchers and partners.