{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T20:31:31Z","timestamp":1769805091640,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100010628","name":"University of La Sabana","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100010628","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"DOI":"10.1007\/s10916-025-02332-7","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T07:27:50Z","timestamp":1769758070000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning and Noninvasive Sensors for Detecting Physiological Dysregulation: A Scoping Review"],"prefix":"10.1007","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2155-5657","authenticated-orcid":false,"given":"Mariana Gonz\u00e1lez","family":"Garc\u00e9s","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6827-3752","authenticated-orcid":false,"given":"Jer\u00f3nimo C\u00e1rdenas","family":"Montoya","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1179-312X","authenticated-orcid":false,"given":"Mar\u00eda Isabel Pe\u00f1a","family":"Mart\u00ednez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4977-4600","authenticated-orcid":false,"given":"Juanita Valencia","family":"Garc\u00eda","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7189-5863","authenticated-orcid":false,"given":"Erwin Hernando Hern\u00e1ndez","family":"Rinc\u00f3n","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"2332_CR1","doi-asserted-by":"publisher","first-page":"105544","DOI":"10.1016\/j.ijmedinf.2024.105544","volume":"190","author":"JA Hughes","year":"2024","unstructured":"Hughes JA, Wu Y, Jones L, Douglas C, Brown N, Hazelwood S, et al. Analyzing pain patterns in the emergency department: Leveraging clinical text deep learning models for real-world insights. Int J Med Inform. 2024;190:105544. Available in: https:\/\/doi.org\/10.1016\/j.ijmedinf.2024.105544","journal-title":"Int J Med Inform"},{"issue":"6","key":"2332_CR2","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1007\/s10877-024-01206-6","volume":"38","author":"H Jeong","year":"2024","unstructured":"Jeong H, Kim D, Kim DW, Baek S, Lee HC, Kim Y, et al. Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices. J Clin Monit Comput. 2024;38(6):1357\u201365. Available in: https:\/\/doi.org\/10.1007\/s10877-024-01206-6","journal-title":"J Clin Monit Comput"},{"key":"2332_CR3","doi-asserted-by":"publisher","first-page":"851690","DOI":"10.3389\/fmed.2022.851690","volume":"9","author":"CL Wu","year":"2022","unstructured":"Wu CL, Liu SF, Yu TL, Shih SJ, Chang CH, Yang Mao SF, et al. Deep learning-based pain classifier based on the facial expression in critically ill patients. Front Med (Lausanne). 2022;9:851690. Available in: https:\/\/doi.org\/10.3389\/fmed.2022.851690","journal-title":"Front Med (Lausanne)"},{"issue":"3","key":"2332_CR4","doi-asserted-by":"publisher","first-page":"241","DOI":"10.4258\/hir.2021.27.3.241","volume":"27","author":"D Jeddah","year":"2021","unstructured":"Jeddah D, Chen O, Lipsky AM, Forgacs A, Celniker G, Lilly CM, et al. Validation of an automatic tagging system for identifying respiratory and hemodynamic deterioration events in the intensive care unit. Healthc Inform Res. 2021;27(3):241\u20138. Available in: https:\/\/doi.org\/10.4258\/hir.2021.27.3.241","journal-title":"Healthc Inform Res"},{"key":"2332_CR5","doi-asserted-by":"publisher","first-page":"e51250","DOI":"10.2196\/51250","volume":"26","author":"J Huo","year":"2024","unstructured":"Huo J, Yu Y, Lin W, Hu A, Wu C. Application of AI in multilevel pain assessment using facial images: systematic review and meta-analysis. J Med Internet Res. 2024;26:e51250. Available in: https:\/\/doi.org\/10.2196\/51250","journal-title":"J Med Internet Res"},{"key":"2332_CR6","doi-asserted-by":"publisher","first-page":"107365","DOI":"10.1016\/j.cmpb.2023.107365","volume":"231","author":"S Gkikas","year":"2023","unstructured":"Gkikas S, Tsiknakis M. Automatic assessment of pain based on deep learning methods: a systematic review. Comput Methods Programs Biomed. 2023;231:107365. Available in: https:\/\/doi.org\/10.1016\/j.cmpb.2023.107365","journal-title":"Comput Methods Programs Biomed"},{"key":"2332_CR7","doi-asserted-by":"publisher","first-page":"113305","DOI":"10.1016\/j.eswa.2020.113305","volume":"149","author":"G Bargshady","year":"2020","unstructured":"Bargshady G, Zhou X, Deo RC, Soar J, Whittaker F, Wang H. Enhanced deep learning algorithm development to detect pain intensity from facial expression images. Expert Syst Appl. 2020;149:113305. Available in: https:\/\/doi.org\/10.1016\/j.eswa.2020.113305","journal-title":"Expert Syst Appl"},{"issue":"1","key":"2332_CR8","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s10877-022-00881-7","volume":"37","author":"GJ Solares","year":"2023","unstructured":"Solares GJ, Garcia D, Monge Garcia MI, Crespo C, Rabago JL, Iglesias F, et al. Real-world outcomes of the hypotension prediction index in the management of intraoperative hypotension during non-cardiac surgery: a retrospective clinical study. J Clin Monit Comput. 2023;37(1):211\u201320.Available in: https:\/\/doi.org\/10.1007\/s10877-022-00881-7","journal-title":"J Clin Monit Comput"},{"issue":"2","key":"2332_CR9","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1016\/j.bja.2024.10.048","volume":"134","author":"Z Jian","year":"2025","unstructured":"Jian Z, Liu X, Kouz K, Settels JJ, Davies S, Scheeren TWL, et al. Deep learning model to identify and validate hypotension endotypes in surgical and critically ill patients. Br J Anaesth. 2025;134(2):308\u201316. Available in: https:\/\/doi.org\/10.1016\/j.bja.2024.10.048","journal-title":"Br J Anaesth"},{"key":"2332_CR10","doi-asserted-by":"publisher","first-page":"1424935","DOI":"10.3389\/fcomp.2024.1424935","volume":"6","author":"R Gutierrez","year":"2024","unstructured":"Gutierrez R, Garcia-Ortiz J, Villegas-Ch W. Multimodal AI techniques for pain detection: integrating facial gesture and paralanguage analysis. Front Comput Sci. 2024;6:1424935. Available in: https:\/\/doi.org\/10.3389\/fcomp.2024.1424935","journal-title":"Front Comput Sci"},{"issue":"9","key":"2332_CR11","doi-asserted-by":"publisher","first-page":"4804","DOI":"10.3390\/app15094804","volume":"15","author":"S Buitrago-Osorio","year":"2025","unstructured":"Buitrago-Osorio S, Gil-Gonz\u00e1lez J, \u00c1lvarez-Meza AM, Cardenas-Pe\u00f1a D, Orozco-Gutierrez A. Electroencephalography-based pain detection using kernel spectral connectivity network with preserved spatio-frequency interpretability. Appl Sci (Basel). 2025;15(9):4804. Available in: https:\/\/doi.org\/10.3390\/app15094804","journal-title":"Appl Sci (Basel)"},{"key":"2332_CR12","doi-asserted-by":"publisher","first-page":"107780","DOI":"10.1016\/j.asoc.2021.107780","volume":"112","author":"A Semwal","year":"2021","unstructured":"Semwal A, Londhe ND. Computer aided pain detection and intensity estimation using compact CNN based fusion network. Appl Soft Comput. 2021;112:107780. Available in: https:\/\/doi.org\/10.1016\/j.asoc.2021.107780","journal-title":"Appl Soft Comput"},{"key":"2332_CR13","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.neuroscience.2021.11.034","volume":"481","author":"F Wu","year":"2022","unstructured":"Wu F, Mai W, Tang Y, Liu Q, Chen J, Guo Z. Learning spatial-spectral-temporal EEG representations with deep attentive-recurrent-convolutional neural networks for pain intensity assessment. Neuroscience. 2022;481:144\u201355.Available in: https:\/\/doi.org\/10.1016\/j.neuroscience.2021.11.034","journal-title":"Neuroscience"},{"issue":"2","key":"2332_CR14","doi-asserted-by":"publisher","first-page":"195","DOI":"10.3390\/bioengineering12020195","volume":"12","author":"M Bouazizi","year":"2025","unstructured":"Bouazizi M, Feghoul K, Wang S, Yin Y, Ohtsuki T. A non-invasive approach for facial action unit extraction and its application in pain detection. Bioengineering (Basel). 2025;12(2):195. Available in: https:\/\/doi.org\/10.3390\/bioengineering12020195","journal-title":"Bioengineering (Basel)"},{"issue":"5","key":"2332_CR15","doi-asserted-by":"publisher","first-page":"e0693","DOI":"10.1097\/CCE.0000000000000693","volume":"4","author":"FF Schmitzberger","year":"2022","unstructured":"Schmitzberger FF, Hall AE, Hughes ME, Belle A, Benson B, Ward KR, et al. Detection of hemodynamic status using an analytic based on an electrocardiogram lead waveform. Crit Care Explor. 2022;4(5):e0693.Available in: https:\/\/doi.org\/10.1097\/CCE.0000000000000693","journal-title":"Crit Care Explor"},{"issue":"12","key":"2332_CR16","doi-asserted-by":"publisher","first-page":"e50169","DOI":"10.7759\/cureus.50169","volume":"15","author":"AL Holder","year":"2023","unstructured":"Holder AL, Khanna AK, Scott MJ, Rossetti SC, Rinehart JB, Linn DD, et al. A Delphi process to identify relevant outcomes that may be associated with a predictive analytic tool to detect hemodynamic deterioration in the intensive care unit. Cureus. 2023;15(12):e50169.Available in: https:\/\/doi.org\/10.7759\/cureus.50169","journal-title":"Cureus"},{"issue":"9","key":"2332_CR17","doi-asserted-by":"publisher","first-page":"1336","DOI":"10.3390\/jcm8091336","volume":"8","author":"J Kim","year":"2019","unstructured":"Kim J, Chae M, Chang HJ, Kim YA, Park E. Predicting cardiac arrest and respiratory failure using feasible artificial intelligence with simple trajectories of patient data. J Clin Med. 2019;8(9):1336.Available in: https:\/\/doi.org\/10.3390\/jcm8091336","journal-title":"J Clin Med"},{"key":"2332_CR18","doi-asserted-by":"publisher","first-page":"1372814","DOI":"10.3389\/fpain.2024.1372814","volume":"5","author":"S Gkikas","year":"2024","unstructured":"Gkikas S, Tachos NS, Andreadis S, Pezoulas VC, Zaridis D, Gkois G, et al. Multimodal automatic assessment of acute pain through facial videos and heart rate signals utilizing transformer-based architectures. Front Pain Res (Lausanne). 2024;5:1372814. Available in: https:\/\/doi.org\/10.3389\/fpain.2024.1372814","journal-title":"Front Pain Res (Lausanne)"},{"key":"2332_CR19","doi-asserted-by":"publisher","first-page":"102581","DOI":"10.1016\/j.mex.2024.102581","volume":"12","author":"M Nazeer","year":"2024","unstructured":"Nazeer M, Salagrama S, Kumar P, Sharma K, Parashar D, Qayyum M, et al. Improved method for stress detection using bio-sensor technology and machine learning algorithms. MethodsX. 2024;12:102581. Available in: https:\/\/doi.org\/10.1016\/j.mex.2024.102581","journal-title":"MethodsX"},{"issue":"3","key":"2332_CR20","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/s40122-024-00584-8","volume":"13","author":"SN El-Tallawy","year":"2024","unstructured":"El-Tallawy SN, Pergolizzi JV, Vasiliu-Feltes I, Ahmed RS, LeQuang JK, El-Tallawy HN, et al. Incorporation of \u201cartificial intelligence\u201d for objective pain assessment: a comprehensive review. Pain Ther. 2024;13(3):293\u2013317. Available in: https:\/\/doi.org\/10.1007\/s40122-024-00584-8","journal-title":"Pain Ther"},{"issue":"15","key":"2332_CR21","doi-asserted-by":"publisher","first-page":"5496","DOI":"10.3390\/s22155496","volume":"22","author":"WH Jean","year":"2022","unstructured":"Jean WH, Sutikno P, Fan SZ, Abbod MF, Shieh JS. Comparison of deep learning algorithms in predicting expert assessments of pain scores during surgical operations using analgesia nociception index. Sensors (Basel). 2022;22(15):5496. Available in: https:\/\/doi.org\/10.3390\/s22155496","journal-title":"Sensors (Basel)"},{"key":"2332_CR22","doi-asserted-by":"publisher","first-page":"1150264","DOI":"10.3389\/fpain.2023.1150264","volume":"4","author":"R Fernandez Rojas","year":"2023","unstructured":"Fernandez Rojas R, Hirachan N, Brown N, Waddington G, Murtagh L, Seymour B, et al. Multimodal physiological sensing for the assessment of acute pain. Front Pain Res (Lausanne). 2023;4:1150264. Available in: https:\/\/doi.org\/10.3389\/fpain.2023.1150264","journal-title":"Front Pain Res (Lausanne)"},{"issue":"1","key":"2332_CR23","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s12911-022-01772-2","volume":"22","author":"D Jaber","year":"2022","unstructured":"Jaber D, Hajj H, Maalouf F, El-Hajj W. Medically-oriented design for explainable AI for stress prediction from physiological measurements. BMC Med Inform Decis Mak. 2022;22(1):38. Available in: https:\/\/doi.org\/10.1186\/s12911-022-01772-2","journal-title":"BMC Med Inform Decis Mak"},{"key":"2332_CR24","doi-asserted-by":"publisher","first-page":"105408","DOI":"10.1016\/j.compbiomed.2022.105408","volume":"145","author":"Q Yin","year":"2022","unstructured":"Yin Q, Shen D, Tang Y, Ding Q. Intelligent monitoring of noxious stimulation during anaesthesia based on heart rate variability analysis. Comput Biol Med. 2022;145:105408. Available in: https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105408","journal-title":"Comput Biol Med"},{"issue":"4","key":"2332_CR25","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.3390\/s25041150","volume":"25","author":"OMT Abdel Deen","year":"2025","unstructured":"Abdel Deen OMT, Fan SZ, Shieh JS. A multimodal deep learning approach to intraoperative nociception monitoring: integrating electroencephalogram, photoplethysmography, and electrocardiogram. Sensors (Basel). 2025;25(4):1150. Available in: https:\/\/doi.org\/10.3390\/s25041150","journal-title":"Sensors (Basel)"},{"key":"2332_CR26","doi-asserted-by":"publisher","first-page":"e59520","DOI":"10.2196\/59520","volume":"27","author":"C Park","year":"2025","unstructured":"Park C, Han C, Jang SK, Kim H, Kim S, Kang BH, et al. Development and validation of a machine learning model for early prediction of delirium in intensive care units using continuous physiological data: retrospective study. J Med Internet Res. 2025;27:e59520. Available in: https:\/\/doi.org\/10.2196\/59520","journal-title":"J Med Internet Res"},{"issue":"2","key":"2332_CR27","doi-asserted-by":"publisher","first-page":"195","DOI":"10.3390\/bioengineering12020195","volume":"12","author":"S Nerella","year":"2025","unstructured":"Nerella S, Khezeli K, Davidson A, Tighe P, Bihorac A, Rashidi P. End-to-end machine learning framework for facial AU detection in intensive care units. Bioengineering. 2025;12(2):195. Available in: https:\/\/doi.org\/10.3390\/bioengineering12020195","journal-title":"Bioengineering"},{"issue":"3","key":"2332_CR28","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.pain.2008.04.025","volume":"137","author":"JD Loeser","year":"2008","unstructured":"Loeser JD, Treede RD. The Kyoto protocol of IASP basic pain terminology. Pain. 2008;137(3):473\u20137.","journal-title":"Pain"},{"issue":"9","key":"2332_CR29","doi-asserted-by":"publisher","first-page":"1976","DOI":"10.1097\/j.pain.0000000000001939","volume":"161","author":"SN Raja","year":"2020","unstructured":"Raja SN, Carr DB, Cohen M, Finnerup NB, Flor H, Gibson S, et al. The revised International Association for the Study of Pain definition of pain: concepts, challenges, and compromises. Pain. 2020;161(9):1976\u201382. doi: https:\/\/doi.org\/10.1097\/j.pain.0000000000001939.","journal-title":"Pain"},{"key":"2332_CR30","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2401.12783","author":"G Nie","year":"2024","unstructured":"Nie G, Zhu J, Tang G, Zhang D, Geng S, Zhao Q, Hong S. A review of deep learning methods for photoplethysmography data. IEEE J Biomed Health Inform. 2024. Available in: https:\/\/doi.org\/10.48550\/arXiv.2401.12783","journal-title":"IEEE J Biomed Health Inform"},{"issue":"3","key":"2332_CR31","doi-asserted-by":"publisher","first-page":"e1065","DOI":"10.1097\/CCE.0000000000001065","volume":"6","author":"CM Lilly","year":"2024","unstructured":"Lilly CM, Kirk D, Pessach IM, Celniker G, Cucchi EW, Blum JM, et al. Application of machine learning models to biomedical and information system signals from critically ill adults. Crit Care Explor. 2024;6(3):e1065. Available in: https:\/\/doi.org\/10.1097\/CCE.0000000000001065","journal-title":"Crit Care Explor"},{"issue":"3","key":"2332_CR32","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1097\/CCM.0000000000005800","volume":"51","author":"KJ Cho","year":"2023","unstructured":"Cho KJ, Kwon O, Kwon JM, Lee Y, Park H, Jeon KH, et al. Detecting patient deterioration using artificial intelligence in a rapid response system. Crit Care Med. 2023;51(3):384\u201393.Available in: https:\/\/doi.org\/10.1097\/CCM.0000000000005800","journal-title":"Crit Care Med"},{"key":"2332_CR33","doi-asserted-by":"publisher","first-page":"3178","DOI":"10.1016\/j.procs.2024.09.355","volume":"246","author":"MA Al-Alim","year":"2024","unstructured":"Al-Alim MA, Mubarak R, Salem NM, Sadek I. Deep neural network based for stress detection. Procedia Comput Sci. 2024;246:3178\u201387. Available in: https:\/\/doi.org\/10.1016\/j.procs.2024.09.355","journal-title":"Procedia Comput Sci"},{"key":"2332_CR34","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s10916-022-01869-1","volume":"46","author":"KJ Burdick","year":"2022","unstructured":"Burdick KJ, Gupta M, Sangari A, Schlesinger JJ. Improved Patient Monitoring with a Novel Multisensory Smartwatch Application. J Med Syst. 2022; 46: 83. doi:https:\/\/doi.org\/10.1007\/s10916-022-01869-1","journal-title":"J Med Syst"},{"key":"2332_CR35","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/s10916-023-01997-2","volume":"47","author":"A Sangari","year":"2023","unstructured":"Sangari A, Bingham MA, Cummins M, Sood A, Tong A, Purcell P, Schlesinger JJ. A Spatiotemporal and Multisensory Approach to Designing Wearable Clinical ICU Alarms. J Med Syst. 2023; 47: 105. doi:https:\/\/doi.org\/10.1007\/s10916-023-01997-2","journal-title":"J Med Syst"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02332-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-025-02332-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02332-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T07:27:51Z","timestamp":1769758071000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-025-02332-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,30]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2332"],"URL":"https:\/\/doi.org\/10.1007\/s10916-025-02332-7","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,30]]},"assertion":[{"value":"11 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable. This study is a scoping review based on previously published literature and did not involve human participants or personal data.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical Trial Number"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"14"}}