日本小児循環器学会雑誌 Pediatric Cardiology and Cardiac Surgery

We present excerpts from the “Kawasaki disease research in the data-driven era: From omics analysis to artificial intelligence” symposium held at the 60th Annual Meeting of the Japanese Society of Pediatric Cardiology and Cardiac Surgery. To determine the etiology of Kawasaki disease (KD), machine learning by sparse modeling of the nationwide survey data in Japan was performed and the results were presented. Moreover, the results of a multicenter study on multisystem inflammatory syndrome in children (MIS-C) and KD were also described. To elucidate the pathophysiology of coronary arteritis and to explore new therapeutic agents, the findings of a single-cell analysis of cardiac-derived cells over time in a KD mouse model were reported. Moreover, we provided the results of a previous research that attempted to predict the appearance of coronary artery lesions by machine learning based on the clinical information obtained from the KD patients. The study outcomes of a nationwide survey of transitional and lifelong medical care among adults with coronary sequelae of KD using the JROAD-DPC database as well as information on the training facilities of the Japanese Circulation Society and characteristics of patients at these centers were also detailed. We hope that you will consider this review paper and become interested in the etiopathogenesis of KD and its treatment advances.