Explainable deep learning algorithm for distinguishing incomplete Kawasaki disease by coronary artery lesions on echocardiographic imaging

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Citation: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, Vol.223 : 106970, 2022-08

MeSH: Algorithms ; COVID-19* / diagnostic imaging ; Child ; Coronary Artery Disease* / diagnostic imaging ; Coronary Vessels / diagnostic imaging ; Coronary Vessels / pathology ; Deep Learning* ; Echocardiography ; Fever / complications ; Fever / diagnosis ; Fever / pathology ; Humans ; Infant ; Mucocutaneous Lymph Node Syndrome* / complications ; Mucocutaneous Lymph Node Syndrome* / diagnostic imaging

Keywords: Coronary artery lesion ; Deep learning ; Explanable AI ; Kawasaki disease ; Ultrasound Image

Abstract: Background and objective: Incomplete Kawasaki disease (KD) has often been misdiagnosed due to a lack of the clinical manifestations of classic KD. However, it is associated with a markedly higher prevalence of coronary artery lesions. Identifying coronary artery lesions by echocardiography is important for the timely diagnosis of and favorable outcomes in KD. Moreover, similar to KD, coronavirus disease 2019, currently causing a worldwide pandemic, also manifests with fever; therefore, it is crucial at this moment that KD should be distinguished clearly among the febrile diseases in children. In this study, we aimed to validate a deep learning algorithm for classification of KD and other acute febrile diseases.

Methods: We obtained coronary artery images by echocardiography of children (n = 138 for KD; n = 65 for pneumonia). We trained six deep learning networks (VGG19, Xception, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) using the collected data.

Results: SE-ResNext50 showed the best performance in terms of accuracy, specificity, and precision in the classification. SE-ResNext50 offered a precision of 81.12%, a sensitivity of 84.06%, and a specificity of 58.46%.

Conclusions: The results of our study suggested that deep learning algorithms have similar performance to an experienced cardiologist in detecting coronary artery lesions to facilitate the diagnosis of KD.