{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T07:03:57Z","timestamp":1789715037869,"version":"4.0.1"},"reference-count":51,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Nursing in Critical Care"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Pain is a multifaceted and subjective phenomenon frequently experienced by patients in intensive care units. In non\u2010communicating populations, conventional assessment tools are often inadequate and susceptible to observer bias. Deep learning\u2010based facial analysis has emerged as a promising approach for the objective quantification of observable pain\u2010related behavioural indicators.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To evaluate the feasibility and diagnostic accuracy of deep\u2010learning models in categorising pain severity in non\u2010communicative adult intensive care patients, using expert\u2010annotated facial images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>Features were extracted via the DenseNet\u2010169 architecture, dimensionally reduced with principal component analysis and classified using support vector machine, random forest and K\u2010nearest neighbours. Data sets were independently annotated by a multidisciplinary team comprising an intensivist, intensive care nurses and a pain specialist. Model performance was comprehensively assessed through accuracy, precision, sensitivity, the F1 score, the area under the receiver operating characteristic curve and Fleiss' kappa coefficient to ensure robust inter\u2010rater reliability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 636 facial images obtained from 120 adult intensive care unit patients were analysed. The support vector machine model achieved the highest overall performance, with an accuracy of 96.9% and an area under the receiver operating characteristic curve of 0.994, demonstrating exceptional sensitivity in severe pain classification. While K\u2010nearest neighbours showed superior performance for moderate pain detection, random forest yielded the lowest accuracy across all data sets. Notably, inter\u2010rater agreement was low (\n                      <jats:italic>k<\/jats:italic>\n                      \u2009=\u20090.16), highlighting the significant variability in expert human judgements and the subjective nature of manual pain assessment.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Deep learning\u2010based facial analysis provides a valid, reproducible and standardised method for pain assessment in non\u2010verbal intensive care patients. The creation of a multi\u2010expert annotated data set and the systematic comparison of classifiers across diverse clinical perspectives represent the original contributions of this study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Relevance to Clinical Practice<\/jats:title>\n                    <jats:p>Automated facial expression analysis minimises inter\u2010observer variability by providing an objective decision support mechanism for critical care nurses. This technology facilitates the standardisation of pain management protocols and bolsters patient safety by reducing the inherent risks of subjective assessment bias.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/nicc.70478","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T09:54:38Z","timestamp":1776246878000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessment of Pain Intensity Using Deep Learning Models in Non\u2010Communicative Intensive Care Patients"],"prefix":"10.1111","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8015-7870","authenticated-orcid":false,"given":"Suzan","family":"Guven","sequence":"first","affiliation":[{"name":"Department of Nursing, Faculty of Health Sciences Van Yuzuncu Yil University  Van Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0965-1443","authenticated-orcid":false,"given":"Fatma Eti","family":"Aslan","sequence":"additional","affiliation":[{"name":"Department of Nursing, Faculty of Health Sciences Bah\u00e7e\u015fehir University  \u0130stanbul Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8120-5101","authenticated-orcid":false,"given":"Murat","family":"Canayaz","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering Van Yuzuncu Yil University  Van Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"e_1_2_15_2_1","doi-asserted-by":"publisher","DOI":"10.2174\/1874434602115010170"},{"key":"e_1_2_15_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-022-06807-w"},{"issue":"2","key":"e_1_2_15_4_1","first-page":"50","article-title":"Adaptation of Two Measurement Tools Used in Sedation\u2010Agitation and Pain Assessment in Intensive Care Units Into Turkish: Validity and Reliability Study","volume":"22","author":"S\u0131lay F.","year":"2018","journal-title":"Yo\u011fun Bak\u0131m Hem\u015fireli\u011fi Dergisi"},{"key":"e_1_2_15_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.iccn.2007.03.002"},{"key":"e_1_2_15_6_1","volume-title":"Nursing Practice Theories Related to Cognition, Bodily Pain, and Man\u2010Environment Interactions","author":"McCaffery M.","year":"1968"},{"key":"e_1_2_15_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113305"},{"key":"e_1_2_15_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmn.2023.08.003"},{"key":"e_1_2_15_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmn.2024.09.010"},{"key":"e_1_2_15_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3067611"},{"key":"e_1_2_15_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10050548"},{"key":"e_1_2_15_12_1","first-page":"67","volume-title":"Pain Nature and Control","author":"Eti Aslan F.","year":"2014"},{"key":"e_1_2_15_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117006"},{"key":"e_1_2_15_14_1","doi-asserted-by":"publisher","DOI":"10.1097\/PR9.0000000000001044"},{"key":"e_1_2_15_15_1","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.58963"},{"key":"e_1_2_15_16_1","doi-asserted-by":"publisher","DOI":"10.4103\/ija.ija_65_23"},{"key":"e_1_2_15_17_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocac231"},{"key":"e_1_2_15_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW51313.2020.00092"},{"key":"e_1_2_15_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/jonm.13425"},{"key":"e_1_2_15_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3101396"},{"key":"e_1_2_15_21_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12912\u2010024\u201002170\u2010x"},{"key":"e_1_2_15_22_1","doi-asserted-by":"publisher","DOI":"10.1097\/CNQ.0000000000000424"},{"key":"e_1_2_15_23_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2024.1309720"},{"key":"e_1_2_15_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSAC51774.2021.00094"},{"key":"e_1_2_15_25_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.851690"},{"key":"e_1_2_15_26_1","doi-asserted-by":"publisher","DOI":"10.1177\/20552076241259664"},{"key":"e_1_2_15_27_1","first-page":"1","volume-title":"The Third International Conference on Biological Information and Biomedical Engineering","author":"Lee J. 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Bursa Uluda\u011f \u00dcniversitesi Sa\u011fl\u0131k Bilimleri Enstit\u00fcs\u00fc (2022)."},{"key":"e_1_2_15_40_1","doi-asserted-by":"publisher","DOI":"10.1542\/peds.2015\u20100029"},{"issue":"1","key":"e_1_2_15_41_1","first-page":"21","article-title":"Yapay zek\u00e2 ve hem\u015firelik","volume":"2","author":"Akgerman A.","year":"2022","journal-title":"Sa\u011fl\u0131k Bilimlerinde Yapay Zek\u00e2 Dergisi"},{"key":"e_1_2_15_42_1","doi-asserted-by":"publisher","DOI":"10.1111\/nicc.12624"},{"key":"e_1_2_15_43_1","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.51150"},{"key":"e_1_2_15_44_1","doi-asserted-by":"publisher","DOI":"10.33715\/inonusaglik.818816"},{"key":"e_1_2_15_45_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010021\u201084714\u20108"},{"key":"e_1_2_15_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.actpsy.2023.103893"},{"key":"e_1_2_15_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11517\u2010017\u20101772\u20101"},{"key":"e_1_2_15_48_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21093273"},{"key":"e_1_2_15_49_1","volume-title":"4th International Eurasian Conference on Biological and Chemical Sciences","author":"Merih Y. 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