Interpretable machine learning for predicting isolated basal septal hypertrophy.

Journal: PloS one
Published Date: Jun 30, 2025

Abstract

BACKGROUND: The basal septal hypertrophy(BSH) is an often under-recognized morphological change in the left ventricle. This is a common echocardiographic finding with a prevalence of approximately 7-20%, which may indicate early structural and functional remodeling of the left ventricle in certain pathologies. It also poses a risk of severe left ventricular outflow tract obstruction and is a significant cause of postoperative complications in patients undergoing transcatheter aortic valve implantation (TAVI). Compared to traditional algorithms, machine learning algorithms are more effective at capturing nonlinear relationships and developing more accurate diagnostic and predictive models. However, no predictive models for BSH have been developed using machine learning algorithms.

Authors

  • Lei Gao
    Microscopy Core Facility, Biomedical Research Core Facilities, Westlake University, Hangzhou, China.
  • Boyan Tian
    Department of Cardiac Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
  • Qiqi Jia
    Department of Cardiac Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
  • Xingyu He
    Department of Pathology, Hebei Medical University, Shijiazhuang, China.
  • Guannan Zhao
    The Third Department of Ultrasound, Baoding First Central Hospital, Baoding, China.
  • Yueheng Wang
    Department of Cardiac Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, China.

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