Evidence map›Paper›PMID 42682880›Full record

ArticleInternational journal of biomedical imaging2026

Evaluation of the Relationship Between Facial Hue and Blood Pressure Fluctuations in Hemodialysis Patients.

Kosuke Oiwa, Takehiro Okama, Satoshi Suzuki, Yoshitaka Maeda, Akio Nozawa

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Article in International journal of biomedical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Kosuke OiwaDepartment of Information and Management Systems Engineering, Nagaoka University of Technology, Nagaoka, Niigata, Japan, nagaokaut.ac.jp.ORCID https://orcid.org/0000-0002-7508-171X
Takehiro OkamaMedical Corporation Credo, Sato Clinic, Chiba, Chiba, Japan.
Satoshi SuzukiDepartment of Clinical Engineering, Kanagawa Institute of Technology, Atsugi, Kanagawa, Japan, kait.jp.ORCID https://orcid.org/0009-0004-4178-9412
Yoshitaka MaedaMedical Simulation Center, Jichi Medical University, Shimotsuke, Tochigi, Japan, jichi.ac.jp.ORCID https://orcid.org/0000-0003-4895-2820
Akio NozawaCollege of Science and Engineering, Aoyama Gakuin University, Sagamihara, Kanagawa, Japan, aoyama.ac.jp.ORCID https://orcid.org/0000-0001-8461-6432

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: In hemodialysis, hypotension may occur due to fluid removal, but traditional intermittent blood pressure measurements risk missing its onset. We are aimed to enabling noninvasive and continuous blood pressure monitoring in patients undergoing hemodialysis based on the spatial features of facial visible images. Facial visible images contain not only skin chromaticity information but also facial expression-related variations. To improve estimation, facial color components are separated from expressions, analyzed for their relationship with blood pressure fluctuations, and used to build an individual estimation model. Methods: Facial landmark coordinates were extracted, and eight facial regions of interest (ROIs) were defined on facial images of hemodialysis patients. Mean and differential color values between ROIs served as explanatory variables, whereas mean blood pressure was the dependent variable. Stepwise selection identified key variables for blood pressure estimation, followed by regression modeling. Results: Applying a linear regression model to the mean color values in each ROI resulted in a higher generalization performance across all subjects. The facial hue components of the a Conclusion: Applying a linear regression model to the absolute values of facial hue improved generalization performance, and there are individual differences in the relationship between facial hue components and blood pressure fluctuations. This study is significant because these findings could contribute to the realization of remote blood pressure monitoring during hemodialysis.

Indexed as

blood pressure estimationfacial hue informationhemodialysisL*a*b* color spacenoncontact sensingRGB color space

Identifiers

PMID42682880
PMCPMC13530390

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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.