[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-410608-105":59,"doc-detail-410608-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","antepartum-prediction-of-shoulder-dystocia-using-machine-learning","Antepartum prediction of shoulder dystocia using machine learning","","Shoulder dystocia is a serious delivery complication that can occur without identifiable risk factors, making accurate prediction essential for informed decisions about delivery mode. A retrospective single-center study over 10 years developed and validated a machine learning model using maternal and fetal characteristics from term singleton vaginal deliveries. Multiple algorithms were trained and evaluated with cross-validation and ROC-AUC, incorporating handling of missing values and class imbalance. The CatBoost model achieved the best performance (AUC 0.83) and highlighted ultrasound EFW and maternal BMI as key predictors, supporting clinician decision-making in delivery planning.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/antepartum-prediction-of-shoulder-dystocia-using-machine-learning/410608/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/antepartum-prediction-of-shoulder-dystocia-using-machine-learning/410608.png","ImageObject",300,407,{"name":92,"@type":93},"Aria Callaghan","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is shoulder dystocia difficult to predict, and why does the study focus on risk assessment?","Question",{"text":112,"@type":113},"Shoulder dystocia can occur without clear identifiable risk factors, and accurate risk assessment is needed to support informed counseling about delivery mode. The study addresses this prediction gap by building and validating a machine learning model.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data were used to develop the machine learning model?",{"text":117,"@type":113},"The model used retrospective data from term singleton vaginal deliveries at a single academic hospital over 10 years. Maternal characteristics and fetal parameters included ultrasound- and clinically based estimated fetal weight within 2 weeks of delivery, along with related delivery and birthweight data.",{"name":119,"@type":110,"acceptedAnswer":120},"Which model performed best and what were the main predictive features?",{"text":121,"@type":113},"The CatBoost model achieved the highest performance with an AUC of 0.83. Key predictive features included sonographic estimated fetal weight (EFW) and maternal BMI, with additional contribution from clinical EFW.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},410608,1790765135,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},962084926284,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Archives of Gynecology and Obstetrics (2026) 313:3  \n[https://doi.org/10.1007/s00404-025-08271-5](https://doi.org/10.1007/s00404-025-08271-5)  \nAntepartum prediction of shoulder dystocia using machine learning  \nLior Heresco1 · Noa Levy2 · Omer Todress2 · Hadar Gluska1 · Tal Biron‑Shental1 · Omer Weitzner1  \nReceived: 14 October 2025 / Accepted: 25 November 2025 © The Author(s) 2025  \nAbstract  \nPurpose Shoulder dystocia (SD) is a serious delivery complication, often occurring without identifiable risk factors. Accurate risk assessment is essential for educated decisions regarding the mode of delivery. This study aimed to develop and validate a machine learning-based model for SD prediction.  \nMethods We conducted a retrospective analysis of term singleton vaginal deliveries at a single academic hospital over 10 years. Exclusion criteria included cesarean deliveries and multiple gestations. Maternal and fetal characteristics were compared between SD and non-SD cases. Eleven features were selected for model development. Missing values in normally distributed variables were imputed using mean values. To address class imbalance, repeated random sampling of non-SD cases was performed. Data were split into training and test sets (70:30) and standardized. Multiple machine learning models—including logistic regression, decision tree, random forest, support vector machine, XGBoost, and CatBoost—were evaluated using cross-validation and area under the ROC curve (AUC) .  \nResults Among 51,628 deliveries, 94 (0.18%) involved SD. SD was associated with higher BMI, shorter stature, and increased diabetes rates (all p \u003C 0.05) . Mean birthweight was significantly higher in the SD group (3751 g vs 3287 g, p \u003C 0.01). The CatBoost model achieved the highest performance (AUC = 0.83, 95% CI 0.77–0.89). Key predictive features were sonographic estimated fetal weight (EFW, 55.6% of the model’s decision-making weight), maternal BMI (20.1%), and clinical EFW (7 .9%) .  \nConclusion Our machine learning-based model predicted SD with an AUC of 0.83 and may support clinicians in delivery planning.  \nKeywords Shoulder dystocia · Vaginal delivery · Machine learning  \n* Lior Heresco[lior160@gmail.com](lior160@gmail.com)  \n1 Department of Obstetrics and Gynecology, Meir Medical Center, affiliated with the Gray Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel  \n2 Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel  \nIntroduction  \nShoulder dystocia (SD) is an obstetric emergency that occurs in 0.2–3% of vaginal deliveries, when one or both of the fetal shoulders are obstructed at the level of the pelvic inlet, typically by the symphysis pubis, after the expulsion of the fetal head. SD results from a size discrepancy between the fetal shoulders and the pelvic inlet [1] .  \nThe definition of SD is different in various sources, but a commonly accepted definition is the inability to deliver the fetal shoulders with gentle downward pressure on the fetal head, necessitating additional obstetric maneuvers to facilitate the delivery. Alternatively, a more precise definition involves the duration of more than 60 s between the delivery of the fetal head and the rest of the body [2] .  \nSD is associated with both increased maternal complications, including postpartum hemorrhage (PPH), thirdand fourth-degree tears, and cervical lacerations [3], and increased neonatal morbidity such as low Apgar scores,  \nbrachial plexus injuries, clavicular and brachial fractures, acidosis, and hypoxic neurologic damage [4–6] .  \nAlthough some risk factors for the occurrence of SD, including maternal diabetes mellitus (DM), maternal obesity, and fetal macrosomia, have been previously identified [7, 8], SD remains difficult to predict [9–11] . Previous studies have shown that only 25% of shoulder dystocia cases are associated with at least one significant risk factor. Additionally, most of these cases occur in neonates weighing les","cbCaihljHG6Py7nj","https://ap.wps.com/l/cbCaihljHG6Py7nj","pdf",789974,"English","# Abstract\n## Purpose\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Definition and clinical significance\n## Known risk factors and prediction challenges\n## Rationale for machine learning approaches\n# Materials and methods\n## Study design and setting\n## Inclusion and exclusion criteria\n## Variables and ultrasound-based EFW","[{\"question\":\"Why is shoulder dystocia difficult to predict, and why does the study focus on risk assessment?\",\"answer\":\"Shoulder dystocia can occur without clear identifiable risk factors, and accurate risk assessment is needed to support informed counseling about delivery mode. The study addresses this prediction gap by building and validating a machine learning model.\"},{\"question\":\"What data were used to develop the machine learning model?\",\"answer\":\"The model used retrospective data from term singleton vaginal deliveries at a single academic hospital over 10 years. Maternal characteristics and fetal parameters included ultrasound- and clinically based estimated fetal weight within 2 weeks of delivery, along with related delivery and birthweight data.\"},{\"question\":\"Which model performed best and what were the main predictive features?\",\"answer\":\"The CatBoost model achieved the highest performance with an AUC of 0.83. Key predictive features included sonographic estimated fetal weight (EFW) and maternal BMI, with additional contribution from clinical EFW.\"}]","Antepartum prediction of shoulder dystocia using machine learning | PDF",1790585863,15]