Evidence map›Paper›PMID 41743963›Full record

ArticleFrontiers in cardiovascular medicine2026

Assessing arterial stiffness using characteristics of Korotkoff sounds.

Shuqi Ren, Wei Zhao, Changcheng Yi, Xiaoyan Deng, Zengsheng Chen, Li Wang, Ling Xu, Yuheng Yang, Yubo Fan, Anqiang Sun

Abstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Shuqi Ren *Key Laboratory of Biomechanics and Mechanobiology (Ministry of Education), Key Laboratory of Innovation and Transformation of Advanced Medical Devices (Ministry of Industry and Information Technology), National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Wei Zhao *Department of Cardiology, Institute of Vascular Medicine, State Key Laboratory of Vascular Homeostasis and Remodeling, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Peking University Third Hospital, Peking University, Beijing, China.
Changcheng YiKey Laboratory of Biomechanics and Mechanobiology (Ministry of Education), Key Laboratory of Innovation and Transformation of Advanced Medical Devices (Ministry of Industry and Information Technology), National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Xiaoyan DengKey Laboratory of Biomechanics and Mechanobiology (Ministry of Education), Key Laboratory of Innovation and Transformation of Advanced Medical Devices (Ministry of Industry and Information Technology), National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Zengsheng ChenKey Laboratory of Biomechanics and Mechanobiology (Ministry of Education), Key Laboratory of Innovation and Transformation of Advanced Medical Devices (Ministry of Industry and Information Technology), National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Li WangKey Laboratory of Biomechanics and Mechanobiology (Ministry of Education), Key Laboratory of Innovation and Transformation of Advanced Medical Devices (Ministry of Industry and Information Technology), National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Ling XuDepartment of Cardiology, Institute of Vascular Medicine, State Key Laboratory of Vascular Homeostasis and Remodeling, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Peking University Third Hospital, Peking University, Beijing, China.
Yuheng YangKey Laboratory of Biomechanics and Mechanobiology (Ministry of Education), Key Laboratory of Innovation and Transformation of Advanced Medical Devices (Ministry of Industry and Information Technology), National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Yubo FanKey Laboratory of Biomechanics and Mechanobiology (Ministry of Education), Key Laboratory of Innovation and Transformation of Advanced Medical Devices (Ministry of Industry and Information Technology), National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Anqiang SunKey Laboratory of Biomechanics and Mechanobiology (Ministry of Education), Key Laboratory of Innovation and Transformation of Advanced Medical Devices (Ministry of Industry and Information Technology), National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Biological Science and Medical Engineering, Beihang University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objectives: Arterial stiffness is a recognized marker of vascular ageing and is associated with adverse cardiovascular outcomes. However, routine assessment of pulse wave velocity (PWV) remains limited in many clinical and home settings. This study investigated the feasibility of extracting arterial stiffness-related information from Korotkoff sounds recorded during cuff-based blood pressure measurement using feature analysis and machine learning. Materials and methods: Korotkoff sounds were collected from 123 young (25.9 ± 2.2 years) participants and 112 older (67.5 ± 6.7 years) participants using a custom-developed device as a proof-of-concept for age-related vascular differences. In addition, 81 hospital participants with measured brachial-ankle PWV (baPWV) were enrolled and grouped according to baPWV to further evaluate clinical feasibility. Time- and frequency-domain features were extracted, and both traditional feature-based models and deep learning approaches were applied for classification. Results: Extracted features including center of mass, skewness, and peak frequency showed significant differences between the age-stratified groups. Two deep learning models achieved classification accuracies of 89.3% and 93.7%, respectively, outperforming traditional feature-based analysis. In the baPWV-defined classification task, model performance was moderate (accuracy 87.5% and 81.3%). In the baPWV-measured cohort, Korotkoff sound-derived features showed a statistically significant but modest association with measured baPWV. Conclusion: Korotkoff sounds contain measurable information related to vascular ageing and arterial stiffness, and machine learning can leverage these signals for group discrimination. Given that the primary comparison used age as a surrogate label and clinical outcomes were not assessed, the present data do not establish incremental value for cardiovascular risk stratification beyond age and blood pressure. Larger studies with standardized PWV measurements, ideally carotid-femoral PWV (cfPWV), and prospective validation are required before prognostic or risk-stratification claims can be made.

Indexed as

arterial stiffnessartificial intelligenceblood pressurecardiovascular diseasemachine learningpulse wave velocity

Identifiers

PMID41743963
PMCPMC12930344

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.