---
name: image-algorithm-validator
description: Medical image processing algorithm validation skill for segmentation, detection, and analysis algorithms
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Edit
  - Bash
metadata:
  specialization: biomedical-engineering
  domain: science
  category: Medical Imaging
  skill-id: BME-SK-031
graph:
  domains: [domain:biomedical-engineering]
  skillAreas: [skill-area:machine-learning-frameworks, skill-area:statistical-analysis, skill-area:data-analysis]
  workflows: [workflow:experiment-design, workflow:peer-review-cycle]
  roles: [role:biomedical-engineer, role:research-engineer]
---

# 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 analysis tools

## Configuration

```yaml
image-algorithm-validator:
  algorithm-types:
    - segmentation
    - detection
    - classification
    - registration
    - quantification
  metrics:
    - Dice
    - IoU
    - sensitivity
    - specificity
    - AUC
    - Hausdorff-distance
  validation-methods:
    - holdout
    - cross-validation
    - external-validation
```

## Output Artifacts

- Dataset curation protocols
- Ground truth documentation
- Performance reports
- Statistical analyses
- Reader study results
- Failure mode catalogs
- Regulatory submission sections
- Validation summaries

## Quality Criteria

- Ground truth methodology validated
- Metrics appropriate for algorithm type
- Dataset representative of intended use
- Statistical analysis rigorous
- Subgroup performance assessed
- Documentation supports regulatory review


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Healthcare & Life Sciences
Roles: Clinical Research, Medical Writer
Specialties: Specialty not specified
Tools: Monai, Simpleitk, Scikit Image
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 480ff87acc2350f8ac688d92ffe867cce6ea74f9788209344370789544b7e333