{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T08:14:24Z","timestamp":1771316064731,"version":"3.50.1"},"reference-count":20,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T00:00:00Z","timestamp":1751068800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T00:00:00Z","timestamp":1751068800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intl J Gynecology &amp;amp; Obste"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To identify factors influencing re\u2010suturing decisions for perineal wound dehiscence, and develop predictive models to assist clinical decision making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Five machine learning models; Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), analyzed 129 cases of postpartum perineal wound dehiscence (October 2020\u2013September 2023). Variables included clinical symptoms, wound characteristics, degree of perineal trauma, and clinician seniority. Models were validated on 20% of the data set as a hold\u2010out set.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Re\u2010sutured wounds were significantly deeper, longer, and more disrupted, with higher rates of rectal mucosal involvement (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.031), subspecialist involvement (50% versus 0%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), and follow\u2010up needs. The SVM model showed the best performance with area under the receiver operating characteristic curve (AUC) 0.86 (95% confidence interval 0.81\u20130.91) and accuracy 0.86, identifying wound depth, wound length, and pain score as top predictors. Decision Tree and XGBoost models achieved 0.82 accuracy (AUC 0.83 and 0.85, respectively), with subspecialist involvement, wound depth, and discharge identified as importance features. Random Forest achieved 0.80 accuracy (AUC 0.81). Across models, seniority and wound depth emerged as critical predictors of re\u2010suturing decisions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Machine learning models can reliably predict re\u2010suturing decisions in perineal wound dehiscence. Wound severity and clinician seniority were the strongest predictors supporting data\u2010driven standardization of perineal wound management. Future work should focus on prospective validation, improving interpretability, and clinical integration.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ijgo.70339","type":"journal-article","created":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T05:10:15Z","timestamp":1751087415000},"page":"451-458","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning insights on optimal timing for re\u2010suturing postpartum perineal wound dehiscence"],"prefix":"10.1002","volume":"172","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7745-2926","authenticated-orcid":false,"given":"Peter T.","family":"Khoo","sequence":"first","affiliation":[{"name":"Obstetrics and Gynecology Joan Kirner Women's and Children's Hospital (Western Health)  Melbourne Victoria Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rebecca","family":"McDonald","sequence":"additional","affiliation":[{"name":"Obstetrics and Gynecology Joan Kirner Women's and Children's Hospital (Western Health)  Melbourne Victoria Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2462-7492","authenticated-orcid":false,"given":"J. Oliver","family":"Daly","sequence":"additional","affiliation":[{"name":"Obstetrics and Gynecology Sandringham Hospital (Monash Health)  Melbourne Victoria Australia"},{"name":"Obstetrics and Gynecology Peninsula Health  Melbourne Victoria Australia"},{"name":"Consultative Council on Obstetric and Pediatric Mortality and Morbidity  Melbourne Victoria Australia"},{"name":"Centre of Digital Transformation of Health University of Melbourne  Melbourne Victoria Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1754-3602","authenticated-orcid":false,"given":"Debjyoti","family":"Karmakar","sequence":"additional","affiliation":[{"name":"Department of Obstetrics,Gynaecology and Newborn Health University of Melbourne  Melbourne Victoria Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,6,28]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejogrb.2019.05.038"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2016-013008"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.3109\/00016348709083040"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1013\/j.ejogrb.2020.09.013"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/s0002-9378(12)80047-0"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1097\/SPV.0000000000000921"},{"issue":"34","key":"e_1_2_9_8_1","first-page":"4829","article-title":"Treatment of episiotomy wound infections. 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