{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T06:13:46Z","timestamp":1788243226877,"version":"build-2803163510"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":242,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["23K11954"],"award-info":[{"award-number":["23K11954"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Biomedical Imaging"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>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\u2013related 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\u2009\n                      <jats:sup>\u2217<\/jats:sup>\n                      and b\u2009\n                      <jats:sup>\u2217<\/jats:sup>\n                      values were used as explanatory variables contributing to blood pressure estimation in all subjects, but there were individual differences in the location of ROIs selected as explanatory variables.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/ijbi\/8387784","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T05:19:18Z","timestamp":1788239958000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of the Relationship Between Facial Hue and Blood Pressure Fluctuations in Hemodialysis Patients"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7508-171X","authenticated-orcid":false,"given":"Kosuke","family":"Oiwa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takehiro","family":"Okama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4178-9412","authenticated-orcid":false,"given":"Satoshi","family":"Suzuki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4895-2820","authenticated-orcid":false,"given":"Yoshitaka","family":"Maeda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8461-6432","authenticated-orcid":false,"given":"Akio","family":"Nozawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1080\/08037051.2025.2469260"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105538"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1093\/ndt\/gfad064"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40620-023-01741-6"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.878858"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14232664"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3111628"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106570"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.1541\/ieejeiss.144.672"},{"key":"e_1_2_12_10_2","doi-asserted-by":"publisher","DOI":"10.1002\/tee.24011"},{"key":"e_1_2_12_11_2","doi-asserted-by":"publisher","DOI":"10.1186\/s41100-023-00518-8"},{"key":"e_1_2_12_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3123203"},{"key":"e_1_2_12_13_2","unstructured":"LugaresiC. TangJ. NashH. McClanahanC. UbowejaE. HaysM. ZhangF. ChangC. L. YongM. G. LeeJ. andChangW. T. Media Pipe: A Framework for Building Perception Pipelines 2021 https:\/\/arxiv.org\/abs\/1906.08172."},{"key":"e_1_2_12_14_2","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa F.","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_12_15_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq134"},{"key":"e_1_2_12_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.07.017"},{"key":"e_1_2_12_17_2","doi-asserted-by":"publisher","DOI":"10.1198\/016214505000001230"},{"key":"e_1_2_12_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2019.2931775"},{"key":"e_1_2_12_19_2","unstructured":"ZhuangJ. LiB. ZhangY. ChenY. andZhengX. 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