Aim: This study proposes a new artificial intelligence model based on cardiovascular computed tomography for more efficient and precise recognition of Tetralogy of Fallot (TOF).
Methods: Our model is a structurally optimized stochastic pooling convolutional neural network (SOSPCNN), which combines stochastic pooling, structural optimization, and convolutional neural network. In addition, multiple-way data augmentation is used to overcome overfitting. Grad-CAM is employed to provide explainability to the proposed SOSPCNN model. Meanwhile, both desktop and web apps are developed based on this SOSPCNN model.
Results: The results on ten runs of 10-fold cross-validation show that our SOSPCNN model yields a sensitivity of 92.25±2.19, a specificity of 92.75±2.49, a precision of 92.79±2.29, an accuracy of 92.50±1.18, an F1 score of 92.48±1.17, an MCC of 85.06±2.38, an FMI of 92.50±1.17, and an AUC of 0.9587.
Conclusion: The SOSPCNN method performed better than three state-of-the-art TOF recognition approaches.
Keywords: Grad-CAM; Tetralogy of Fallot; artificial intelligence; computed tomography; convolutional neural network; cross-validation; deep learning; deep neural network; machine learning; multiple-way data augmentation; stochastic pooling; structural optimization.