AI service — AI Medical · Cardiology
ECG interpretation
Classify rhythm and diagnostic labels from 12-lead and single-lead ECG, and turn waveforms into embeddings your own models can build on.
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.
An ECG is a small file and a large volume: most hospitals hold millions. These models read the waveform directly and return multi-label diagnosis probabilities, or an embedding that a classifier of your own is trained on with a fraction of the labels a from-scratch model would need.
Waveforms arrive over your API, from a device export or from a bulk archive, and come back as scored labels with the model version recorded. Throughput here is high enough that a whole archive is usually a matter of hours rather than weeks.
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 ecg interpretation 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
- Archive re-reading — Score an existing waveform archive for a specific diagnosis or cohort.
- Label-efficient classifiers — Train your own endpoint on embeddings rather than on raw waveforms.
- Single-lead and wearable data — Apply the single-lead models to patch and wearable recordings.
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