Automated quality checking
Detect artifacts, lead reversals, clipping, baseline wander, and noise. Flag “needs reacquisition” early to avoid downstream churn.
Reduce cardiologist review time, improve dataset quality, and ship regulatory-ready evidence faster. We provide automated QC, measurement verification, and documentation designed for clinical and submission workflows.
Works with 12-lead ECG, Holter-derived strips, and telemetry exports. Deployment: cloud API or on-prem.
A practical validation layer that plugs into your labeling and adjudication workflows—built to reduce rework, boost trust, and speed timelines.
Detect artifacts, lead reversals, clipping, baseline wander, and noise. Flag “needs reacquisition” early to avoid downstream churn.
Validate QT/QTc, QRS, PR and rhythm annotations with uncertainty estimates and structured adjudication notes.
Dossiers with subgroup analysis, bias checks, versioned datasets, and full audit trails—ready for review packages.
A four-step process that fits trials, labeling pipelines, and model development.
Secure upload via API/SDK or on-prem batch. Support for common ECG formats and exports.
Automatic flagging + prioritization so experts see the right cases first.
Measurement checks, uncertainty, and structured notes. Optional cardiologist sign-off.
Export datasets + dashboards + validation dossier with audit trail and subgroup results.
Choose the model that fits your product and delivery motion.
Value: Add ECG pre-validation as a platform module to shorten expert time and improve annotation consistency.
Value: Speed cardiac safety workflows and reduce cardiologist effort with AI first-pass adjudication support.
Value: Use a ready validation layer so you can focus on the model, not building compliance plumbing.
Value: Improve acquisition quality early, reduce screen failures, and standardize multi-site data capture.
Cloud API, on-prem container, plugins, or SDKs—choose what your governance allows.
REST API for scalable processing with optional SLA. Ideal for platforms and CROs.
Docker container for sensitive environments. Keep data inside your network.
Python/R/MATLAB libraries to embed validation into existing analysis pipelines.
Run a small pilot with your data and see how QC + structured adjudication improves speed and reliability.
Short answers your buyers and reviewers typically ask.
No. It is a validation layer that triages, standardizes, and documents decisions—so clinicians spend time on the right cases and the evidence is easier to audit.
Yes. The page includes an on‑prem Docker option and SDKs. That supports strict data governance and “no external transmission” policies.
A versioned dataset export, summary metrics, a validation report template, and an audit trail of flags/edits suitable for your internal review process.
We can slice and report by available demographics and device/site metadata (where provided), highlighting performance deltas and potential biases.
Tell us what you’re building and we’ll suggest a pilot shape, integration path, and evidence outputs.