An interpretable machine learning risk stratification model for the imaging burden of cerebral small vessel disease: integrating echocardiography, carotid ultrasound, and serological indicators

Huilan Tan; Houwei Li; Chulin Deng; Sisi Liang; Jiafu Lan

doi:10.54254/3049-5458/2026.37658

Article Information

Received: 31 August 2026

Published: 16 October 2026

DOI: https://doi.org/10.54254/3049-5458/2026.37658

Article Type: Research Article

Cite this article

Tan,H.;Li,H.;Deng,C.;Liang,S.;Lan,J. (2026). An interpretable machine learning risk stratification model for the imaging burden of cerebral small vessel disease: integrating echocardiography, carotid ultrasound, and serological indicators. Journal of Clinical Technology and Theory,4(6),1-13.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Journal: Journal of Clinical Technology and Theory

Volume: Vol.4, Issue 6

ISSN: 3049-5458 (Print) / 3049-5466 (Online)

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