dicom-metadata-extract

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 are metadata_json.

Instructions

• Read skill_manifest.yaml before changing arguments, side effects, or validation gates.
• Run scripts/extract_metadata.py through the documented command below; keep outputs under a caller-provided run directory.
• If a host agent exposes run_script, use run_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_v1 on evidence packs before treating the run as reviewed evidence.

Available Scripts

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

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