AI service — AI Medical · Surgery and endoscopy
Endoscopy video analysis
Detect and segment polyps in colonoscopy video and turn endoscopic footage into features for detection and classification.
Send us your data volume, throughput and latency targets and any constraint we should design around. You get a proposed configuration, a benchmark on your own data, and a known cost per unit of work before you commit.
Colonoscopy is a real-time task and an archive at the same time. These models detect and outline polyps frame by frame, or produce spatio-temporal features that a detection or classification head of your own is trained on.
Video arrives as recorded clips or a live stream and comes back as masks, boxes and optical-diagnosis classes. The cross-centre accuracy drop is documented and significant, which is why local benchmarking is not optional here.
Every output is clinical decision support, not a diagnosis: a qualified professional reviews and signs it. Where clinical use requires regulatory approval in your jurisdiction, that approval remains yours to hold — we provide the infrastructure, the model operations and the audit trail behind it.
What we size for
We build the endoscopy video analysis pipeline around the workload you actually have: data format, accuracy target, latency, throughput, concurrency, retention and scheduling. Start with a pilot, then scale production capacity without changing a line of your integration.
Common use cases
- Detection support — Flag polyps in recorded or live colonoscopy for the endoscopist.
- Quality assurance — Review recorded procedures for missed findings and withdrawal quality.
- Research cohorts — Segment an endoscopy archive consistently for a study.
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