Diagnosis of Incomplete Kawasaki Disease using Deep learning Techniques with Ultrasound Images of Coronary Artery Lesions

Kawasaki disease (KD) is the most common cause of acquired heart disease in young children and can lead to sudden death. Incomplete KD lacks clinical characteristics of KD and is thus difficult to distinguish from other diseases presenting similar symptoms. Although ultrasound imaging is useful to identify one of the most fatal complications, coronary aneurysms, the diagnosis of incomplete KD is still difficult due to its similar symptoms to other diseases. We here demonstrated the feasibility of the deep learning algorithms for the diagnosis of incomplete KD. Various deep learning networks were trained, and their accuracy was compared. Although the accuracy is lower than the experienced specialist, the experimental results suggest that deep learning algorithms may assist clinicians to diagnose KD.