T2D-LVDD: neural network-based predictive models for left ventricular diastolic dysfunction in type 2 diabetes.

Journal: Diabetology & metabolic syndrome
Published Date: May 17, 2025

Abstract

Cardiovascular disease complications are the leading cause of morbidity and mortality in patients with Type 2 diabetes (T2DM). Left ventricular diastolic dysfunction (LVDD) is one of the earliest myocardial characteristics of diabetic cardiac dysfunction. Therefore, we aimed to develop an LVDD-risk predictive model to diagnose cardiac dysfunction before severe cardiovascular complications arise. We trained an artificial neural network model to predict LVDD risk with patients' clinical information. The model showed better performance than classical machine learning methods such as logistic regression, random forest and support vector machine. We further explored LVDD-risk/protective features with interpretability methods in neural network. Finally, we provided a freely accessible web server called LVDD-risk, where users can submit their clinical information to obtain their LVDD-risk probability and the most noteworthy risk indicators.

Authors

  • Yu Rong
    Department of Radiology, Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis in Guizhou Province, Guizhou Provincial People's Hospital, China.
  • Wei Liu
    Department of Radiation Oncology, Mayo Clinic, Scottsdale, AZ, United States.
  • Ke Li
    School of Ideological and Political Education, Shanghai Maritime University, Shanghai, China.
  • Jian Guo
    Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1 Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China; Clinical Center for Eye Tumors, Capital Medical University, Beijing, 100730, China.
  • Xue-Ping Li
    Xi'an Key Laboratory for Prevention and Treatment of Common Aging Diseases, Translational and Research Centre for Prevention and Therapy of Chronic Disease, Institute of Basic and Translational Medicine, Xi'an Medical University, Xi'an, 710021, China. 1476117275@qq.com.

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