ArticleComputer methods and programs in biomedicine2022
Explainable deep learning algorithm for distinguishing incomplete Kawasaki disease by coronary artery lesions on echocardiographic imaging.
Article in Computer methods and programs in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 32 citations in OpenAlex.
- Multimodal Echocardiographic Assessment in Intravenous Immunoglobulin-Resistant Kawasaki Disease: From Prediction to Long-Term Surveillance.Echocardiography (Mount Kisco, N.Y.) · 2026Review
- From Automated ECG Interpretation to Multimodal Cardiovascular Intelligence: The Evolution of Artificial Intelligence in Cardiovascular Medicine.Medical sciences (Basel, Switzerland) · 2026Review
- Explainable Artificial Intelligence in Radiological Cardiovascular Imaging-A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Limited Performance of Machine Learning Models Developed Based on Demographic and Laboratory Data Obtained Before Primary Treatment to Predict Coronary Aneurysms.Biomedicines · 2025Article
- Applications of Artificial Intelligence in Vasculitides: A Systematic Review.ACR open rheumatology · 2025Review
- Identifying the risk of Kawasaki disease based solely on routine blood test features through novel construction of machine learning models.Computational and structural biotechnology journal · 2025Article
- Progression prediction of coronary artery lesions by echocardiography-based ultrasomics analysis in Kawasaki disease.Italian journal of pediatrics · 2024Article
- Application of artificial intelligence in the diagnosis and treatment of Kawasaki disease.World journal of clinical cases · 2024Article
- Real-time and accurate estimation of surgical hemoglobin loss using deep learning-based medical sponges image analysis.Scientific reports · 2023Article
- Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2023Review
- Precision diagnostics in children.Cambridge prisms. Precision medicine · 2023Review
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveIncomplete 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.
methodsWe 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.
resultsSE-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%.
conclusionsThe 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.
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