Abstract
Objective
We propose a new multimodal artificial intelligence model that facilitate the differentiation of Kawasaki disease through the integration of clinical symptom photographs and laboratory examination indices.
Methods
This study is a retrospective investigation based on laboratory examination data, palm images, and conjunctival image databases of 620 children (comprising those with both healthy physical examinations and Kawasaki disease) who visited our hospital between January 2022 and January 2024. The multimodal model was trained and evaluated using this database. GradCAM was incorporated to analyze the attention mechanisms of the multimodal model. A human-machine double-blind controlled trial was designed to evaluate the diagnostic accuracy of the obtained multimodal model and senior clinical physicians with advanced qualifications on external dataset.
Results
The performance evaluation of the multimodal model on the validation set yielded an area under the curve of 0.97 and an accuracy of 0.96.The GradCAM analysis reveals that the model's attention is concentrated on areas such as palm swelling and peeling, as well as conjunctivitis, which aligns with clinical reasoning.The human-machine double-blind trial validated that the multimodal model and senior pediatric physicians with advanced qualifications achieved comparable accuracy rates in identifying cases within an independent external cohort.
Conclusion
The multimodal model we developed can assist junior doctors in diagnosing Kawasaki disease, providing a new approach for the auxiliary diagnosis of Kawasaki disease in medically underserved areas.