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Multi-Institutional Evaluation and Training of Breast Density Classification AI Algorithm Using ACR Connect and

Laura Brink1, Ricardo Amaya Romero1, Laura Coombs1

  • 1American College of Radiology, Reston, Virginia.

Journal of the American College of Radiology : JACR
|November 17, 2024
PubMed
Summary
This summary is machine-generated.

This study tested AI breast density classification using ACR Connect and AI-LAB across multiple hospitals. Results showed the AI model had poor generalizability, highlighting the need for multi-institutional training and validation.

Keywords:
AIclassificationradiologysoftware

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Breast cancer screening

Background:

  • ACR Connect and AI-LAB are software platforms for AI implementation.
  • Multi-institutional validation is crucial for robust AI performance.

Purpose of the Study:

  • To demonstrate and test the ACR Connect and AI-LAB platform capabilities.
  • To implement multi-institutional AI training and validation for breast density classification.

Main Methods:

  • Proof-of-concept study involving six US hospitals.
  • Installation of Connect and AI-LAB software.
  • Training and testing a breast density algorithm on retrospective mammograms.
  • Recording implementation timelines (IRB approval, software installation, training/testing).
  • Comparing algorithm performance across hospitals and against multi-institutional datasets.

Main Results:

  • Median IRB approval time: 66 days; median installation time: 157 days; median training/testing time: 216 days.
  • The breast density algorithm performed worse at individual hospitals than on the holdout test dataset, indicating poor generalizability.
  • Locally fine-tuned models showed mixed performance and poor results on the test dataset.

Conclusions:

  • Successful installation and implementation of Connect and AI-LAB platforms were demonstrated.
  • The study highlights the poor generalizability of single-dataset trained algorithms and institution-specific fine-tuned models.
  • Emphasizes the importance of multi-institutional testing and training for AI in medical imaging.