AI-assisted diagnosis of myocardial hypertrophy based on cardiac MRI: A systemic review-ZENTIME PUBLISHING CORPORATION LIMITED

Home Progress in Medical Devices All issues Issue 1

AI-assisted diagnosis of myocardial hypertrophy based on cardiac MRI: A systemic review

Shimin Zhou¹, Xudong Guo^1,2, Yunli Shen², Qinfen Jiang², Xin Gong², Jie Ding², Yihong Yang³, Guojie Xu¹, Jican Wen¹, Jingyang Niu¹

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

²State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200093, China.

³Department of Nuclear Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, China.

Address correspondence to: Xudong Guo, School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Yangpu District, Shang hai 200093, China. E-mail: guoxd@usst.edu.cn.

DOI: https://doi.org/10.61189/569607adnpiw

Received October 25, 2025; Accepted February 12, 2026; Published March 31, 2026

Highlights

● This review systematically summarizes the research progress of artificial intelligence technologies in the diagnosis of cardiac hypertrophy based on cardiac MRI, with a focus on AI diagnostic methods utilizing Cine-MRI, T1/T2 Mapping, late gadolinium enhancement (LGE), and multi-sequence fusion strategies.

● This review highlights the application potential and current limitations of natural language processing-based automated MRI report parsing technology for large-scale case screening and phenotypic stratification.

● This review analyzes existing challenges in AI diagnosis, including data quality, annotation consistency, and model generalization, and discusses future directions such as multicenter collaboration, multimodal data fusion, and clinical translation.

Abstract

Cardiac Hypertrophy represents a complex pathological condition characterized by ventricular wall thickening, with diverse etiologies and substantial challenges in clinical differential diagnosis. In recent years, rapid advances in Artificial Intelligence (AI) techniques for CMR image analysis have provided novel technical approaches for the precise diagnosis of cardiac hypertrophy. This paper systematically reviews the research progress of CMR-based AI technologies in the diagnosis of cardiac hypertrophy, including AI diagnostic methods based on Cine-MRI sequences, T1/T2 Mapping sequences, late gadolinium enhancement (LGE) sequences, and multi-sequence fusion strategies. The review further explores the technological evolution from traditional machine learning to deep learning and their applications in differentiating normal from hypertrophic hearts, as well as in the fine classification of cardiac hypertrophy with different etiologies. Furthermore, this paper elucidates the application value of natural language processing (NLP)-based MRI report automatic parsing technology in large-scale case screening and discusses the existing challenges and potential future directions of AI in this field.

Keywords: Cardiac hypertrophy, Cardiac magnetic resonance, Artificial intelligence, Deep learning, Multi-sequence fusion

Download

Cite

Views

Downloads

Lastest Issue

Development of an automated cytological smear staining device for rapid on-site evaluation Review of key technologies in ankle rehabilitation robots Design and analysis of a tissue retraction manipulator for neuroendoscopic surgery Heart sound classification based on the fusion of dynamic features and images of mel-frequency cepstral coefficients Research progress on hemostatic techniques for combat trauma AI-assisted diagnosis of myocardial hypertrophy based on cardiac MRI: A systemic review Slim exquisite easy-exposing video laryngoscope: A novel video laryngoscope A metallic foreign object detection algorithm in pharmaceuticals based on phase rotation and smoothed pseudo-Wigner-Ville distribution