Accurate Differential Diagnosis of Cardiomyopathy Phenotype Based on Multimodal AI Technology

Abstract

Cardiomyopathy is a group of diseases involving the heart muscle, mainly manifested by the abnormality of the structure and function of the heart muscle. According to etiology and pathological characteristics, cardiomyopathy can be divided into various types, including hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), non-dilated left ventricular cardiomyopathy (NDLVC) and restrictive cardiomyopathy (RCM). These different types of cardiomyopathy differ significantly in clinical presentation, pathophysiological mechanisms, and treatment strategies, so accurately differentiating the different phenotypes of cardiomyopathy is critical to developing a personalized treatment plan. However, traditional diagnostic methods such as electrocardiogram (ECG), echocardiography, magnetic resonance imaging (MRI) and genetic testing, while able to provide useful information to a certain extent, still have limitations in complex cases and can lead to misdiagnosis or missed diagnosis. In order to overcome the limitations of traditional diagnostic methods and improve the ability of accurate diagnosis of different phenotypes of cardiomyopathy, this study developed a cardiomyopathy phenotypic differential diagnosis system based on multimodal artificial intelligence (AI) technology. We integrate patient data from multiple centers, including clinical data, electrocardiograms (ECG), echocardiograms, magnetic resonance imaging (MRI), and genomic data, standardized and pre-processed. Through machine learning and deep learning algorithms, multimodal AI models are constructed and trained using large-scale training data sets. The accuracy and stability of the model in the differentiation of different cardiomyopathy phenotypes were verified on an independent data set, and the model parameters were optimized according to the verification results to improve the generalization ability and clinical applicability of the model.