image-algorithm-validator

# Image Algorithm Validator Skill ## Purpose The Image Algorithm Validator Skill supports validation of medical image processing algorithms, including segmentation, detection, and analysis algorithms, ensuring performance meets clinical requirements. ## Capabilities - Ground truth dataset curation guidance - Performance metric calculation (Dice, IoU, sensitivity, specificity) - Inter-observer variability analysis - Statistical comparison methods - Validation dataset stratification - Multi-reader multi-case study design - FDA AI/ML guidance alignment - Failure case analysis - Edge case identification - Performance boundary testing - Cross-validation methodology ## Usage Guidelines ### When to Use - Validating image analysis algorithms - Curating validation datasets - Designing reader studies - Preparing regulatory submissions ### Prerequisites - Algorithm development complete - Ground truth established - Validation dataset available - Performance criteria defined ### Best Practices - Use representative, diverse datasets - Establish robust ground truth methodology - Assess performance across subgroups - Document failure modes ## Process Integration This skill integrates with the following processes: - Medical Image Processing Algorithm Development - AI/ML Medical Device Development - Clinical Evaluation Report Development - Software Verification and Validation ## Dependencies - SimpleITK library - scikit-image - MONAI framework - Evaluation frameworks - Statistical ...