Reliability of Handheld Ultrasound Assessment of Brachial Artery Flow-Mediated Dilation Using AI-Assisted Automated Analysis in Postmenopausal Women

<i>Background and Objectives:</i> Endothelial dysfunction is an early indicator of cardiovascular disease and is commonly assessed using flow-mediated dilation (FMD). Although handheld ultrasound (HHUS) devices improve measurement accessibility, image analysis for conventional flow-mediated dilation (FMD) assessment remains time-consuming and highly operator-dependent. This study aimed to evaluate the between-day test-retest reliability of an AI-assisted brachial artery image analysis workflow integrating HHUS imaging with a YOLO<sub>v12</sub> deep learning model in postmenopausal women. <i>Materials and Methods:</i> Seventeen postmenopausal women aged 55-70 years completed two flow-mediated dilation assessments conducted seven days apart. Brachial artery images were acquired using a standardized FMD protocol with a handheld ultrasound system. An AI-assisted image analysis workflow based on a YOLO<sub>v12</sub> deep learning model was used to automatically measure baseline diameter (D<sub>base</sub>), peak diameter (D<sub>peak</sub>), absolute FMD (FMD<sub>abs</sub>), and relative FMD (FMD%). Between-day reliability was evaluated using intraclass correlation coefficients (ICCs), coefficients of variation (CVs), and Bland-Altman analysis. <i>Results:</i> Good between-day repeatability was observed for baseline and peak diameters, with ICCs of 0.81 and 0.76 and low CVs (3.26% and 3.22%), respectively. Functional vascular outcomes also demonstrated good reliability, with ICCs of 0.81 for FMD<sub>abs</sub> and 0.87 for FMD%. However, higher CVs were observed for FMD<sub>abs</sub> (17.15%) and FMD% (19.09%), indicating substantial inter-individual variability. Bland-Altman analysis showed a small mean difference for FMD% (0.34%), with no evidence of systematic bias. <i>Conclusions:</i> An AI-assisted HHUS image analysis workflow integrating a YOLO<sub>v12</sub> deep learning model demonstrates acceptable between-day reliability for diameter-based and dilation-based measures of flow-mediated dilation in postmenopausal women. While variability in functional responses exists, the proposed system is feasible for research-oriented vascular assessment, providing a methodological foundation for future validation and clinical translation studies.

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Journal
Medicina
Published
2026-01-15
DOI
https://doi.org/10.3390/medicina62010181
Primary Topic
Cardiovascular Health and Disease Prevention
Type
article
Field-Weighted Citation Impact
0.00

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article

Reliability of Handheld Ultrasound Assessment of Brachial Artery Flow-Mediated Dilation Using AI-Assisted Automated Analysis in Postmenopausal Women

W. M. Chen, Mei-Wun Tsai, Chun-Hsien Chiu, Yung-Chia Kao
Medicina
Cardiovascular Health and Disease Prevention
article

Reliability of Handheld Ultrasound Assessment of Brachial Artery Flow-Mediated Dilation Using AI-Assisted Automated Analysis in Postmenopausal Women

W. M. Chen, Mei-Wun Tsai, Chun-Hsien Chiu, Yung-Chia Kao
article en

Abstract

<i>Background and Objectives:</i> Endothelial dysfunction is an early indicator of cardiovascular disease and is commonly assessed using flow-mediated dilation (FMD). Although handheld ultrasound (HHUS) devices improve measurement accessibility, image analysis for conventional flow-mediated dilation (FMD) assessment remains time-consuming and highly operator-dependent. This study aimed to evaluate the between-day test-retest reliability of an AI-assisted brachial artery image analysis workflow integrating HHUS imaging with a YOLO<sub>v12</sub> deep learning model in postmenopausal women. <i>Materials and Methods:</i> Seventeen postmenopausal women aged 55-70 years completed two flow-mediated dilation assessments conducted seven days apart. Brachial artery images were acquired using a standardized FMD protocol with a handheld ultrasound system. An AI-assisted image analysis workflow based on a YOLO<sub>v12</sub> deep learning model was used to automatically measure baseline diameter (D<sub>base</sub>), peak diameter (D<sub>peak</sub>), absolute FMD (FMD<sub>abs</sub>), and relative FMD (FMD%). Between-day reliability was evaluated using intraclass correlation coefficients (ICCs), coefficients of variation (CVs), and Bland-Altman analysis. <i>Results:</i> Good between-day repeatability was observed for baseline and peak diameters, with ICCs of 0.81 and 0.76 and low CVs (3.26% and 3.22%), respectively. Functional vascular outcomes also demonstrated good reliability, with ICCs of 0.81 for FMD<sub>abs</sub> and 0.87 for FMD%. However, higher CVs were observed for FMD<sub>abs</sub> (17.15%) and FMD% (19.09%), indicating substantial inter-individual variability. Bland-Altman analysis showed a small mean difference for FMD% (0.34%), with no evidence of systematic bias. <i>Conclusions:</i> An AI-assisted HHUS image analysis workflow integrating a YOLO<sub>v12</sub> deep learning model demonstrates acceptable between-day reliability for diameter-based and dilation-based measures of flow-mediated dilation in postmenopausal women. While variability in functional responses exists, the proposed system is feasible for research-oriented vascular assessment, providing a methodological foundation for future validation and clinical translation studies.

MedicinaVol. 62(1)
National Yang Ming Chiao Tung University (TW), Far Eastern Memorial Hospital (TW)
Shanghai Educational Development Foundation
Openalex Percentile: Top 87%
Cardiovascular Health and Disease Prevention
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