AI service — AI Medical · Radiology
Lung cancer screening
Estimate one-to-six-year lung cancer risk from a single low-dose CT, and detect and score pulmonary nodules.
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.
Low-dose CT screening produces a large number of scans and a small number of cancers. These models score risk from a single scan — no clinical variables needed — or detect nodule candidates with a malignancy probability attached.
Volumes arrive from your PACS and come back as a risk score or a nodule list with locations. The risk model is validated on three independent cohorts, which is more external evidence than most models on this site carry.
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 lung cancer screening 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
- Screening triage — Rank low-dose CT studies by risk to focus reader and follow-up capacity.
- Interval setting — Use multi-year risk to decide who returns and when.
- Nodule workup — Detect and score nodule candidates for a radiologist to confirm.
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