medical-data-tools

To inspect DICOM metadata and create de-identified copies for clinical research, you can analyze file headers and modality mixes, then generate anonymized versions for sharing. This workflow requires the pydicom library to read and manipulate the imaging data.

Yes, you can extract features from ECG and EMG biosignals using NeuroKit2. The Skill processes physiological signals like ECG, PPG, EDA, RSP, and EMG to extract analytical features without making patient-specific diagnoses or treatment decisions.

The best way to profile a clinical cohort table and check label balance is to use pandas to summarize clinical datasets. This approach checks code distributions and subgroup characteristics to provide a comprehensive overview of the dataset's composition.

Yes, you need pandas and pydicom installed to analyze medical imaging data, along with neurokit2 for biosignal analysis. These dependencies are required for data manipulation, DICOM file reading, and physiological signal processing.

The primary limitation is that this Skill cannot make patient-specific diagnoses or treatment decisions. It is designed strictly for research and data engineering tasks like inspecting imaging metadata, summarizing biosignals, and profiling tabular medical datasets.