Artificial Intelligence in Breast Imaging
Data Concepts
| Term | Definition |
|---|
| Training data | Dataset used to develop/train the algorithm |
| Validation data | Smaller training set used for cross-validation of model to test performance robustness and/or boost model performance during training stage |
| Test data | Unseen data withheld from training — used to assess real-world performance |
| External validation | Testing the algorithm using data sets from a different population (different from the one used for initial development) |
Key Principles
- AI algorithms should perform equally well on diverse populations (age, race, density, risk, etc.)
- Patient acceptance: a recent study shows 77% of patients do NOT support being solely screened by AI
- AI is promising for:
- Screening accuracy
- Breast cancer risk prediction
- Diagnostic radiology workflows
- Clinical efficiencies
- AI has been found to be superior or not inferior to human double reading in some studies
- Limitations: performance may vary across populations different from the training population
Clinical Applications in Breast Imaging
- Screening mammography: detection of masses, calcifications, asymmetries
- BC risk prediction: using imaging features + patient data
- Workflow optimization: triage, prioritization of worklists
- Supplemental screening: decision support for supplemental US or MRI