Journal of the American College of Radiology : JACR · 2023
Applying Artificial Intelligence to Pediatric Chest Imaging: Reliability of Leveraging Adult-Based Artificial Intelligence Models.
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Morcos George, Yi Paul H, Jeudy Jean
Аннотация
OBJECTIVE: The scarcity of artificial intelligence (AI) applications designed for use in pediatric patients can cause a significant health disparity in this vulnerable population. We investigated the performance of an adult-trained algorithm in detecting pneumonia in a pediatric population to explore the viability of leveraging adult-trained algorithms to accelerate pediatric AI research.
METHODS: We analyzed a publicly available pediatric chest x-ray dataset using an AI algorithm from TorchXRayVision. A 60% threshold was used to make binary predictions for pneumonia presence. Predictions were compared with dataset labels. Performance measures including true-positive rate, false-positive rate, true-negative rate, false-negative rate, sensitivity, specificity, positive predictive value (PPV), negative predictive value, accuracy, and F1-score were calculated for the complete dataset and bacterial and viral pneumonia subsets.
RESULTS: Overall (n = 5,856), the algorithm identified 3,923 cases with pneumonia (67.00%) and 1,933 (33.00%) normal cases. In comparison with the actual image labels, there were 3,411 (58.25%) true-positive cases, 512 (8.74%) false-positive cases, 1,071 (18.29%) true-negative cases, and 862 (14.72%) false-negative cases resulting in 79.83% sensitivity, 67.66% specificity, 86.95% PPV, 55.41% negative predictive value, and 76.54% accuracy and an F1-score of 0.83. Although the performance remained consistent in the bacterial pneumonia group, there was a significant decrease in PPV (69.9%) and F1-score (0.74) in the viral pneumonia group.
CONCLUSION: An adult-trained model adequately detected pneumonia in pediatric patients aged 1 to 5 years. Though models trained exclusively on pediatric images performed better, leveraging adult-based algorithms and datasets can expedite pediatric AI research.
METHODS: We analyzed a publicly available pediatric chest x-ray dataset using an AI algorithm from TorchXRayVision. A 60% threshold was used to make binary predictions for pneumonia presence. Predictions were compared with dataset labels. Performance measures including true-positive rate, false-positive rate, true-negative rate, false-negative rate, sensitivity, specificity, positive predictive value (PPV), negative predictive value, accuracy, and F1-score were calculated for the complete dataset and bacterial and viral pneumonia subsets.
RESULTS: Overall (n = 5,856), the algorithm identified 3,923 cases with pneumonia (67.00%) and 1,933 (33.00%) normal cases. In comparison with the actual image labels, there were 3,411 (58.25%) true-positive cases, 512 (8.74%) false-positive cases, 1,071 (18.29%) true-negative cases, and 862 (14.72%) false-negative cases resulting in 79.83% sensitivity, 67.66% specificity, 86.95% PPV, 55.41% negative predictive value, and 76.54% accuracy and an F1-score of 0.83. Although the performance remained consistent in the bacterial pneumonia group, there was a significant decrease in PPV (69.9%) and F1-score (0.74) in the viral pneumonia group.
CONCLUSION: An adult-trained model adequately detected pneumonia in pediatric patients aged 1 to 5 years. Though models trained exclusively on pediatric images performed better, leveraging adult-based algorithms and datasets can expedite pediatric AI research.