The Utility of the Virtual Imaging Trials Methodology for Objective Characterization of AI Systems and Training Data

TL;DR

This study demonstrates that Virtual Imaging Trials (VIT) can objectively evaluate AI models for medical imaging, revealing how dataset diversity and imaging factors influence model performance and reliability.

Contribution

The paper introduces VIT methodology as an objective tool to assess AI systems and training data in medical imaging, improving transparency and reproducibility.

Findings

Diverse training datasets improve external model performance.

VIT provides insights into dataset and imaging physics effects.

External validation shows performance drop compared to internal testing.

Abstract

Purpose: The credibility of Artificial Intelligence (AI) models for medical imaging continues to be a challenge, affected by the diversity of models, the data used to train the models, and applicability of their combination to produce reproducible results for new data. In this work, we aimed to explore whether emerging Virtual Imaging Trials (VIT) methodologies can provide an objective resource to approach this challenge. Approach: The study was conducted for the case example of COVID-19 diagnosis using clinical and virtual computed tomography (CT) and chest radiography (CXR) processed with convolutional neural networks. Multiple AI models were developed and tested using 3D ResNet-like and 2D EfficientNetv2 architectures across diverse datasets. Results: Model performance was evaluated using the area under the curve (AUC) and the DeLong method for AUC confidence intervals. The models trained on the most diverse datasets showed the highest external testing performance, with AUC values ranging from 0.73-0.76 for CT and 0.70-0.73 for CXR. Internal testing yielded higher AUC values (0.77-0.85 for CT and 0.77-1.0 for CXR), highlighting a substantial drop in performance during external validation, which underscores the importance of diverse and comprehensive training and testing data. Most notably, the VIT approach provided objective assessment of the utility of diverse models and datasets, while offering insight into the influence of dataset characteristics, patient factors, and imaging physics on AI efficacy. Conclusions: The VIT approach enhances model transparency and reliability, offering nuanced insights into the factors driving AI performance and bridging the gap between experimental and clinical settings.