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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Machine learning model evaluation

Background:

  • Clinical integration of artificial intelligence (AI) necessitates reliable and robust models.
  • Model repeatability, a key attribute of robustness, is often overlooked during development and evaluation.
  • Lack of focus on repeatability leads to AI models that are unusable in clinical practice.

Purpose of the Study:

  • To evaluate the repeatability of various AI model types (binary, multi-class, ordinal classification, and regression) in medical image analysis.
  • To assess the impact of Monte Carlo dropout predictions on model performance and repeatability across different medical imaging tasks.
  • To determine the optimal number of Monte Carlo iterations for improving repeatability.

Main Methods:

  • Evaluated four model types on four medical image classification tasks (knee osteoarthritis, cervical cancer screening, breast density estimation, retinopathy of prematurity).
  • Measured and compared repeatability on ResNet and DenseNet architectures.
  • Assessed the effect of sampling Monte Carlo dropout predictions at test time on classification performance and repeatability.

Main Results:

  • Leveraging Monte Carlo predictions significantly increased repeatability for all model types, reducing 95% limits of agreement by 16% and class disagreement by 7%.
  • Classification accuracy generally improved alongside repeatability.
  • No further significant gain in repeatability was observed beyond approximately 20 Monte Carlo iterations.
  • Monte Carlo predictions demonstrated better calibration, providing more accurate output probabilities.

Conclusions:

  • Monte Carlo dropout predictions are a valuable technique for enhancing the repeatability and reliability of AI models in medical imaging.
  • The findings suggest that AI models with improved repeatability and calibration are more suitable for clinical workflows.
  • The study provides practical insights into optimizing AI model evaluation for clinical deployment.