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Table 1: Machine learning performance metrics and limitations
Performance metrics Classification Sensitivity (recall): TP/(TP + FN)
Specificity (true-negative rate): TN/(TN + FP)
Accuracy: number of correct predictions/total predictions
AUC: plot of true positive rate (sensitivity) against false positive rate (1 – specificity)
Segmentation Dice similarity coefficient: overlap of 2 samples
Pearson correlation coefficient: strength of linear relationship between 2 variables
Limitations and ways to address them Requires large datasets: multisite collaboration, open-source datasets
Interpretability: saliency maps
Overfitting: more training data, regularization, and batch normalization
Note:—FP indicates false positive; FN, false-negative; ROC, receiver operating characteristic; TN, true-negative; TP, true positive.

Performance metrics	Classification	Sensitivity (recall): TP/(TP + FN) Specificity (true-negative rate): TN/(TN + FP) Accuracy: number of correct predictions/total predictions AUC: plot of true positive rate (sensitivity) against false positive rate (1 – specificity)
Performance metrics	Segmentation	Dice similarity coefficient: overlap of 2 samples Pearson correlation coefficient: strength of linear relationship between 2 variables
Limitations and ways to address them	Requires large datasets: multisite collaboration, open-source datasets Interpretability: saliency maps Overfitting: more training data, regularization, and batch normalization