{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T14:40:36Z","timestamp":1793716836412,"version":"4.6.1"},"reference-count":60,"publisher":"SAGE Publications","issue":"12","license":[{"start":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:00:00Z","timestamp":1777852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:00:00Z","timestamp":1777852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The Cleft Palate Craniofacial Journal"],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To systematically review literature on the use of artificial intelligence (AI) and machine learning (ML) models for detecting velopharyngeal dysfunction (VPD) in patients with cleft palate.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Systematic review conducted in accordance with PRISMA guidelines (PROSPERO CRD420251034524).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>Studies published were identified through EMBASE, ProQuest, Google Scholar, and PubMed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p>A total of 3967 participants contributed 92,323 training samples. Internal validation included 2331 controls and 2449 VPD cases, generating 81,143 validation samples. Ages ranged from 1 to 93 years.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interventions<\/jats:title>\n                    <jats:p>ML models were trained on speech features such as mel frequency cepstral coefficients (MFCCs) and constant Q cepstral coefficients (CQCCs) to classify or validate VPD-related speech outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main Outcome Measure(s)<\/jats:title>\n                    <jats:p>Reported performance metrics included accuracy, precision, recall, F1-score, sensitivity, specificity, and Pearson correlation coefficient (PCC). External validation was assessed when reported.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of 455 screened articles, 34 met the inclusion criteria. Support vector machines were the most commonly used models (16\/34, 47.1%), followed by convolutional neural networks (6\/34, 17.6%) and deep neural networks (2\/34, 5.9%). Across studies reporting performance metrics, midpoint estimates yielded a mean accuracy of 82.9%, precision of 86.7%, F1-score of 0.88, sensitivity of 80.5%, specificity of 82.2%, and PCC of 0.58. Only 3 studies (3\/34, 8.8%) performed external validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI\/ML models demonstrate promise for VPD detection with encouraging performance. Inconsistent reporting, reliance on engineered features, and limited external validation restrict generalizability. No clinically deployable model has yet been achieved.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10556656261445320","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T15:00:04Z","timestamp":1777906804000},"page":"4110-4130","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["The Rise in Artificial Intelligence and Machine Learning Models to Screen for Cleft-Related Velopharyngeal Dysfunction: A Systematic Review"],"prefix":"10.1177","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8529-9694","authenticated-orcid":false,"given":"Julia","family":"Isber","sequence":"first","affiliation":[{"name":"University of Tennessee Health Science Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9082-1000","authenticated-orcid":false,"given":"Weixin","family":"Liu","sequence":"additional","affiliation":[{"name":"Vanderbilt University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bowen","family":"Qu","sequence":"additional","affiliation":[{"name":"Vanderbilt University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shama","family":"Dufresne","sequence":"additional","affiliation":[{"name":"Vanderbilt University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy","family":"Stone","sequence":"additional","affiliation":[{"name":"Vanderbilt University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria E.","family":"Powell","sequence":"additional","affiliation":[{"name":"Vanderbilt University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8560-2646","authenticated-orcid":false,"given":"Stephane A.","family":"Braun","sequence":"additional","affiliation":[{"name":"Vanderbilt University Medical Center"},{"name":"Monroe Carell Jr. Children's Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Izabela A.","family":"Galdyn","sequence":"additional","affiliation":[{"name":"Vanderbilt University Medical Center"},{"name":"Monroe Carell Jr. Children's Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael S.","family":"Golinko","sequence":"additional","affiliation":[{"name":"Vanderbilt University Medical Center"},{"name":"Monroe Carell Jr. Children's Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijun","family":"Yin","sequence":"additional","affiliation":[{"name":"Vanderbilt University"},{"name":"Vanderbilt University"},{"name":"Vanderbilt University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2329-9899","authenticated-orcid":false,"given":"Matthew E.","family":"Pontell","sequence":"additional","affiliation":[{"name":"Vanderbilt University Medical Center"},{"name":"Monroe Carell Jr. Children's Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,5,4]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0094-1298(20)32409-3"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamafacial.2017.0639"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1097\/SCS.0000000000010606"},{"issue":"4","key":"e_1_3_2_5_2","first-page":"500","article-title":"Development of community-based speech therapy model for children with cleft lip\/palate in northeast Thailand","volume":"89","author":"Prathanee B","year":"2006","unstructured":"Prathanee B, Dechongkit S, Manochiopinig S. 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