AI service — AI Medical · Neurology
Stroke and haemorrhage detection
Detect and measure intracranial haemorrhage on non-contrast head CT, for worklist ordering and volume reporting.
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.
Head CT for suspected stroke is the clearest case for automated triage: the finding is time-critical and the volume of normal studies is high. These models return a haemorrhage segmentation with a volume, or per-slice subtype probabilities that order a reading queue.
Studies arrive from your PACS and come back as masks and scores. We state plainly what the open models are: they support triage and measurement, and they are not the regulatory-cleared triage devices sold for this indication.
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 stroke and haemorrhage detection 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
- Worklist ordering — Rank head CT studies so likely-positive cases reach a reader first.
- Volume measurement — Report haemorrhage volume consistently rather than by visual estimate.
- Retrospective audit — Re-read an archive to build a research or quality-assurance cohort.
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