{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:40:33Z","timestamp":1787319633984,"version":"build-2736575974"},"reference-count":30,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T00:00:00Z","timestamp":1733702400000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Science Reports"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>An unanticipated difficult airway is one of the greatest challenges for anesthesiologists. Proper preoperative airway assessment is crucial to reducing complications. However, current screening tests based on anthropometric features are of uncertain benefit. Therefore, our study explores using voice analysis with machine learning algorithms to predict a difficult airway.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Observational, multicenter study with N\u2009=\u2009438 patients initially enrolled at Centro Medico Teknon and Institut Universitari Dexeus (2019\u20132022) for the research study. After excluding 125 patients, N\u2009=\u2009313 were included. Ethics committee approval was obtained. Adults ASA I\u2010III scheduled for elective procedures under general anesthesia with endotracheal intubation were selected. Patient clinical features and traditional predictive tests were collected. Vowels \u201cA, E, I, O, U\u201d were recorded in normal, flexion, and extension positions. Cormack grade was assessed, and data were analyzed using KNIME, resulting in multiple models based on demographics and voice data. ROC curves and other metrics were evaluated for each model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among multiple models evaluated, two yielded the best performance to predict a difficult airway both exclusively analyzing Cormack I and IV cases which showed the most distinct differences. The variables included in each model were the following: Model 1; included demographic data, vowel \u201cA\u201d in all positions and harmonics of the voice achieving an AUC of 0.91. Model 2; Included demographic data, vowel \u201cO\u201d in normal positions and voice parameters (Shimmer, Jitter, HNR); achieving in an AUC of 0.90. In contrast, models which focused on analyzing all Cormack grades (I, II, III, IV) cases performed less effectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Acoustic parameters of the voice together with the demographic data of the patients, when introduced into classification algorithms based on machine learning showed promising signs of predicting a difficult airway.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hsr2.70246","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T02:16:01Z","timestamp":1733796961000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Voice Analysis as a Method for Preoperatively Predicting a Difficult Airway Based on Machine Learning Algorithms: Original Research Report"],"prefix":"10.1002","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7412-5977","authenticated-orcid":false,"given":"Claudia","family":"Rodiera","sequence":"first","affiliation":[{"name":"Department of Anesthesia Anestalia. Centro Medico Teknon, Quironsalud Group Barcelona Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helena","family":"Fortuny","sequence":"additional","affiliation":[{"name":"Department of Anesthesia Anestalia. Centro Medico Teknon, Quironsalud Group Barcelona Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adaia","family":"Valls","sequence":"additional","affiliation":[{"name":"Department of Maxilofacial Instituto Maxilofacial, Centro Medico Teknon, Quironsalud Group Barcelona Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosa","family":"Borras","sequence":"additional","affiliation":[{"name":"Department of Anestesia DARYD, Hospital Universitari Dexeus, Quironsalud Group Barcelona Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1396-8382","authenticated-orcid":false,"given":"Carlos","family":"Ram\u00edrez","sequence":"additional","affiliation":[{"name":"Department of Anesthesia Anestalia. Centro Medico Teknon, Quironsalud Group Barcelona Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bibiana","family":"Ros","sequence":"additional","affiliation":[{"name":"Department of Anesthesia Anestalia. Centro Medico Teknon, Quironsalud Group Barcelona Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josep","family":"Rodiera","sequence":"additional","affiliation":[{"name":"Department of Anesthesia Anestalia. Centro Medico Teknon, Quironsalud Group Barcelona Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jes\u00fas","family":"Santaliestra","sequence":"additional","affiliation":[{"name":"Department of Anesthesia Anestalia. Centro Medico Teknon, Quironsalud Group Barcelona Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miquel","family":"Lanau","sequence":"additional","affiliation":[{"name":"Department of Anesthesia Anestalia. Centro Medico Teknon, Quironsalud Group Barcelona Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nacho","family":"Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Department of Statistics, Women's Institute Hospital Universitari Dexeus, Quironsalud Group Barcelona Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,12,9]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.55730\/1300-0144.5367"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/ALN.0000000000004002"},{"key":"e_1_2_11_4_1","first-page":"818","article-title":"Management of Difficult Tracheal Intubation: A Closed Claims Analysis","volume":"1","author":"Joffe A. 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M.M\u00f8ller S.Charuluxananan J.L'hermite andJ.WetterslevPoor prognostic value of the modified Mallampati score: A Meta\u2010Analysis Involving 177 088 Patients. 2011.","DOI":"10.1093\/bja\/aer292"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bjan.2020.02.007"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.4103\/aer.AER_52_20"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1093\/bja\/80.2.140"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/anae.12955"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1213\/ANE.0000000000005839"},{"issue":"6","key":"e_1_2_11_16_1","article-title":"Usefulness of Ultrasound View of Larynx in Pre\u2010Anesthetic Airway Assessment: A Comparison With Cormack\u2010Lehane Classification During Direct Laryngoscopy","volume":"6","author":"Soltani Mohammadi S.","year":"2016","journal-title":"Anesthesiol Pain Med [Internet]"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.35117"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bjoms.2020.08.045"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104737"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1213\/ANE.0b013e31820098d6"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40560-021-00551-x"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/anae.14732"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.1097\/EJA.0000000000001108"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.21037\/atm-21-6274"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvoice.2016.06.003"},{"key":"e_1_2_11_26_1","unstructured":"KNIME [Internet]. [cited 2023 May 22] https:\/\/www.knime.com\/."},{"key":"e_1_2_11_27_1","doi-asserted-by":"crossref","unstructured":"C. H.Shadle H.Nam andD. H.WhalenComparing measurement errors for formants in synthetic and natural vowels a). [cited 2023 Jun 11] https:\/\/doi.org\/10.1121\/1.4940665.","DOI":"10.1121\/1.4940665"},{"key":"e_1_2_11_28_1","doi-asserted-by":"publisher","DOI":"10.1121\/1.5103195"},{"key":"e_1_2_11_29_1","first-page":"963.e23","article-title":"A Comparison of Healthy and Disordered Voices Using Multi\u2010Dimensional Voice Program, Praat, and TF32","volume":"4","author":"Keung L. 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