Virtual imaging trials improved the transparency and reliability of AI systems in COVID-19 imaging

• URL: http://arxiv.org/abs/2308.09730v3
• Date: Sun, 27 Oct 2024 06:03:16 GMT
• Title: Virtual imaging trials improved the transparency and reliability of AI systems in COVID-19 imaging
• Authors: Fakrul Islam Tushar, Lavsen Dahal, Saman Sotoudeh-Paima, Ehsan Abadi, W. Paul Segars, Ehsan Samei, Joseph Y. Lo,
• Abstract summary: This study focuses on using convolutional neural networks (CNNs) for COVID-19 diagnosis using computed tomography (CT) and chest radiography (CXR) We developed and tested multiple AI models, 3D ResNet-like and 2D EfficientNetv2 architectures, across diverse datasets. Models trained on the most diverse datasets showed the highest external testing performance, with AUC values ranging from 0.73 to 0.76 for CT and 0.70 to 0.73 for CXR.
• Score: 1.6040478776985583
• License:
• Abstract: The credibility of Artificial Intelligence (AI) models in medical imaging, particularly during the COVID-19 pandemic, has been challenged by reproducibility issues and obscured clinical insights. To address these concerns, we propose a Virtual Imaging Trials (VIT) framework, utilizing both clinical and simulated datasets to evaluate AI systems. This study focuses on using convolutional neural networks (CNNs) for COVID-19 diagnosis using computed tomography (CT) and chest radiography (CXR). We developed and tested multiple AI models, 3D ResNet-like and 2D EfficientNetv2 architectures, across diverse datasets. Our evaluation metrics included the area under the curve (AUC). Statistical analyses, such as the DeLong method for AUC confidence intervals, were employed to assess performance differences. Our findings demonstrate that VIT provides a robust platform for objective assessment, revealing significant influences of dataset characteristics, patient factors, and imaging physics on AI efficacy. Notably, models trained on the most diverse datasets showed the highest external testing performance, with AUC values ranging from 0.73 to 0.76 for CT and 0.70 to 0.73 for CXR. Internal testing yielded higher AUC values (0.77 to 0.85 for CT and 0.77 to 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. This approach enhances model transparency and reliability, offering nuanced insights into the factors driving AI performance and bridging the gap between experimental and clinical settings. The study underscores the potential of VIT to improve the reproducibility and clinical relevance of AI systems in medical imaging.

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