dicom-metadata-extract
Extracts selected metadata from a single DICOM file and flags standard-tag Protected Health Information (PHI) presence, aiding medical data compliance reviews and preliminary screening. Provides standardized JSON metadata output for downstream processing. Not intended for anonymization or clinical use.
npx skills add https://github.com/nvidia/skills --skill dicom-metadata-extractBefore / After Comparison
1 组Users manually browse all tags in a DICOM file to identify standard-tag PHI, typically taking about 20 minutes and prone to missing critical fields.
This skill automatically extracts metadata and flags PHI tags, reducing review time to 1 minute and minimizing human oversight.
DICOM Metadata Extract
Purpose
- Used for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence. Not for anonymization or clinical use.
- Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.
- Manifest I/O: inputs are
dicom_path; outputs aremetadata_json.
Instructions
- Read
skill_manifest.yamlbefore changing arguments, side effects, or validation gates. - Run
scripts/extract_metadata.pythrough the documented command below; keep outputs under a caller-provided run directory. - If a host agent exposes
run_script, userun_script("scripts/extract_metadata.py", args=[...]); otherwise run the Bash/Python command shown below. - Check the emitted JSON and run
medagent.verifiers.dicom_metadata_quality_v1on evidence packs before treating the run as reviewed evidence.
Available Scripts
| Script | Purpose | Arguments |
|---|---|---|
scripts/extract_metadata.py | Primary entrypoint declared by skill_manifest.yaml. | PATH_TO_DICOM [--output OUT.json] |
Prerequisites
- Runtime requirements: Python packages listed in
runtime.side_effects.pip_packages. - Run commands from the repository root unless an existing section below says otherwise.
Limitations
- Small PS3.15-inspired standard-tag subset only; not a complete Basic Application Confidentiality Profile implementation.
- Private tags not checked
- Burnt-in pixel PHI not detected
- Multi-frame handling minimal
- Not for clinical deployment, regulatory de-identification, autonomous diagnosis, patient-facing use.
Troubleshooting
| Error | Cause | Fix |
|---|---|---|
| Missing dependency or import error | Runtime package drift from skill_manifest.yaml. | Install the packages declared in the manifest or use the documented setup command. |
| Empty or schema-invalid output | Wrong input path, unsupported modality, or upstream failure. | Re-run with a known fixture and inspect the wrapper JSON plus stderr. |
| Validation gate failure | Output violated a declared engineering invariant. | Keep the failed evidence pack and use the gate message to repair inputs or wrapper code. |
Reads one DICOM file with pydicom and emits JSON on stdout.
python scripts/extract_metadata.py PATH_TO_DICOM
python scripts/extract_metadata.py PATH_TO_DICOM --output result.json
Output includes transfer_syntax, modality, grouped study/series/image
metadata, phi_present, and phi_tags_found.
Use this as the smallest end-to-end example of a Medical AI Skills skill. Do not use it for anonymization, private-tag review, pixel PHI detection, or clinical interpretation.
For second-pass evidence review, generate a trusted run:
python -m eval_engine.run_trusted skills/dicom-metadata-extract \
--fixture skills/dicom-metadata-extract/fixtures/sample_ct.dcm \
--out runs/dicom_metadata_trusted
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