D
dicom-metadata-extract
by @nvidiav
4.3(120)
用于从单个DICOM文件中提取选定元数据,并标记标准标签中是否存在受保护健康信息(PHI),辅助医疗数据合规审查和初步筛查。提供标准化JSON输出,便于下游处理。不可用于匿名化或临床决策。
安装方式
npx skills add https://github.com/nvidia/skills --skill dicom-metadata-extractcompare_arrows
Before / After 效果对比
1 组使用前
用户需要手动浏览DICOM文件中的所有标签,识别可能包含PHI的标准标签,通常需要20分钟左右,且容易遗漏关键字段。
使用后
此技能自动提取元数据并标记PHI标签,将审查时间缩短至1分钟,并减少人为疏漏。
SKILL.md
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
用户评价 (0)
发表评价
效果
易用性
文档
兼容性
暂无评价
统计数据
安装量1.9K
评分4.3 / 5.0
版本
更新日期2026年9月17日
对比案例1 组
用户评分
4.3(120)
5
37%
4
43%
3
13%
2
5%
1
2%
为此 Skill 评分
0.0
兼容平台
🤖claude-code
时间线
创建2026年7月26日
最后更新2026年9月17日