Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases - Takeaways - MDSpire

Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases

  • By

  • Valentine Ojonugwa Idakwo

  • Caren Strote

  • Christian Goelz

  • Qasrina Shafei

  • Thomas J. Stocker

  • Jörg Hausleiter

  • Solveig Vieluf

  • July 1, 2026

  • 0 min

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  • 1

    The systematic review identified 195 studies on predictive machine learning models for valvular heart disease from 2014 to 2025.

  • 2

    Seventy-five studies focused on single-lesion models for aortic stenosis, while 16 studies developed multimodal models.

  • 3

    Multimodal models showed a 6.3 percentage point increase in average performance compared to unimodal models within the same cohort.

  • 4

    Retrospective datasets were used in 86% of the studies, with 79% relying on internal validation for their findings.

  • 5

    The review highlights the need for large, multicenter datasets to validate and standardize data-driven management of valvular heart disease.

Original Source(s)

Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases

  • by Valentine Ojonugwa Idakwo, Caren Strote, Christian Goelz, Qasrina Shafei, Thomas J. Stocker, Jörg Hausleiter, Solveig Vieluf

  • July 1, 2026

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