From medical reports to usable information.
A pipeline for turning medical reports in different formats into structured data, checking traces, and exploring the results through dashboards and an AI assistant.
The problem to solve.
A large archive and an annual flow handled manually. Before analyzing reports, the system needs to extract information, remove direct identifiers, and distinguish usable traces from those requiring review.
Validate traces before AI analysis.
The quality gate compares each trace’s profile with distributions derived from reports already validated by the client. Angles and derivatives help identify noise, contact problems and other artifacts.
Classification is not a clinical decision: cases requiring attention remain with staff.
Inside the system.
Incoming reports
The pipeline is designed for 200,000 reports a year and is intended to automate a manual process.
Digitization
The document becomes structured data: extraction and validation in a single flow.
De-identification
Sensitive documents: direct identifiers are removed before downstream analysis.
Trace quality check
Each lead is cropped from the PDF at its own scale. The grid and background are removed to isolate the black-and-white trace. Angles and derivatives are calculated point by point, then the profile is checked against percentile ranges derived from reports already validated by the client. Each trace is classified as within range, borderline, or reject. Traces affected by poor electrode contact, noise, or other recording artifacts fall into the reject group.
Tags + KPIs
Each report is enriched with tags and indicators, turning the archive into a queryable dataset.
AI classification
Each report is classified with a confidence score: invalid, pediatric, or pathological. Staff retain final decision-making authority.
Dashboard + AI Assistant
Interactive dashboards let staff explore the data and run natural-language queries.
What it enables.
Extraction, checks, tags and exploration become part of one workflow. Dashboards and a natural-language assistant make the archive queryable.
Reports requiring review are separated from routine processing. The stated volumes describe the intended system scope, not an achieved processing result.