Learning-Based Models for Predicting IVIG Resistance and Coronary Artery Lesions in Kawasaki Disease: A Review of Technical Aspects and Study Features.

Abstract

Kawasaki disease (KD) is a common pediatric vasculitis, with coronary artery lesions (CALs) representing its most severe complication. Early identification of high-risk patients, including those with disease resistant to first-line treatments, is essential to guide personalized therapeutic approaches. Given the limited reliability of current scoring systems, there has been growing interest in the development of new prognostic models based on machine learning algorithms and artificial intelligence (AI). AI has the potential to revolutionize the management of KD by improving patient stratification and supporting more targeted treatment strategies. This narrative review examines recent applications of AI in stratifying patients with KD, with a particular focus on the ability of models to predict intravenous immunoglobulin resistance and the risk of CALs. We analyzed studies published between January 2019 and April 2024 that incorporated AI-based predictive models. In total, 21 papers met the inclusion criteria and were subject to technical and statistical review; 90% of these were conducted in patients from Asian hospitals. Most of the studies (18/21; 85.7%) were retrospective, and two-thirds included fewer than 1000 patients. Significant heterogeneity in study design and parameter selection was observed across the studies. Resistance to intravenous immunoglobulin emerged as a key factor in AI-based models for predicting CALs. Only five models demonstrated a sensitivity > 80%, and four studies provided access to the underlying algorithms and datasets. Challenges such as small sample sizes, class imbalance, and the need for multicenter validation currently limit the clinical applicability of machine-learning-based predictive models. The effectiveness of AI models is heavily influenced by the quantity and quality of data, labeling accuracy, and the completeness of the training datasets. Additionally, issues such as noise and missing data can negatively affect model performance and generalizability. These limitations highlight the need for rigorous validation and open access to model code to ensure transparency and reproducibility. Collaboration and data sharing will be essential for refining AI algorithms, improving patient stratification, and optimizing treatment strategies.