AI service — AI Medical · Radiology
Musculoskeletal imaging
Detect fractures on radiographs and estimate skeletal age, with accuracy that varies sharply by body part.
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.
Fracture detection is one of the most requested automated reads and one of the most uneven. Accuracy is good on wrist and long bone, and weak on ribs and spine, so a deployment is scoped to the body parts where it holds rather than sold as a general fracture detector.
Radiographs arrive as DICOM and come back as a probability with a localisation heatmap. Bone age estimation is a separate, well-behaved task with error around four to six months against radiologist consensus.
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 musculoskeletal imaging 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
- Scoped fracture triage — Flag likely fractures on the body parts where accuracy is documented.
- Bone age reporting — Estimate skeletal age consistently across a paediatric caseload.
- Teaching and audit — Re-read an archive to build teaching sets or measure reader variation.
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