[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118276-en":3,"doc-seo-118276-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118276,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine learning perioperative applications in visceral surgery - a narrative review","Machine learning is increasingly used across perioperative stages in visceral surgery to support data-driven clinical decisions. Preoperatively, models help determine surgical indications, optimize timing, estimate risk, forecast prognosis, and improve planning of operative time and resources. Intraoperative work includes visual annotation of the operative field, automated phase classification, and prediction of intraoperative patient decompensation. Postoperative efforts focus on complication and recurrence prediction, and on surgical education, with implementation barriers tied to standardized data, infrastructure, training resources, and ethical and patient-acceptance considerations.","TYPE Mini Review  \nPUBLISHED 30 October 2024 DOI 10.3389/fsurg.2024.1493779  \nEDITED BY  \nMarialuisa Lugaresi,  \nUniversity of Bologna, Italy  \nREVIEWED BY  \nAlberto Posabella,  \nUniversity Hospital of Basel, Switzerland  \n*CORRESPONDENCE  \nIntekhab Hossain  \n [intekhab.hossain@uhn.ca](intekhab.hossain@uhn.ca)  \nRECEIVED 09 September 2024  \nACCEPTED 18 October 2024  \nPUBLISHED 30 October 2024  \nCITATION  \nHossain I, Madani A and Laplante S (2024) Machine learning perioperative applications in visceral surgery: a narrative review.  \nFront. Surg. 11:1493779 .  \ndoi: 10.3389/fsurg.2024.1493779  \nCOPYRIGHT  \n© 2024 Hossain, Madani and Laplante. This isan open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning perioperative applications in visceral surgery: a narrative review  \nIntekhab Hossain1,2*, Amin Madani1,2 and Simon Laplante2,3  \n1Department of Surgery, University of Toronto, Toronto, ON, Canada, 2Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada, 3Department of Surgery, Mayo Clinic, Rochester, MN, United States  \nArtificial intelligence in surgery has seen an expansive rise in research and clinical implementation in recent years, with many of the models being driven by machine learning. In the preoperative setting, machine learning models have been utilized to guide indications for surgery, appropriate timing of operations, calculation of risks and prognostication, along with improving estimations of time and resources required for surgeries. Intraoperative applications that have been demonstrated are visual annotations of the surgical field, automated classification of surgical phases and prediction of intraoperative patient decompensation. Postoperative applications have been studied the most, with most efforts put towards prediction of postoperative complications, recurrence patterns of malignancy, enhanced surgical education and assessment of surgical skill. Challenges to implementation of these models in clinical practice include the need for more quantity and quality of standardized data to improve model performance, sufficient resources and infrastructure to train and use machine learning, along with addressing ethical and patient acceptance considerations.  \nKEYWORDS  \nmachine learning (ML), preoperative, intraoperative, postoperative, applications  \nIntroduction  \nThere has been rapid growth of interest over the past decade in the use of artificial intelligence (AI) in the field of surgery to perform data-driven tasks efficiently and ultimately improve patient care (1) . Machine learning (ML) is a division of AI which learns from large datasets and algorithms to provide personalized analysis and predictions. In visceral surgery, applications of ML in surgery include optimization of patients and resources, intraoperative analysis and feedback, and prediction of postoperative complications.  \nDespite the increase in research and demonstration of application on ML in visceral surgery, there remains a challenge in implementation. Numerous factors play a role in this dilemma including ML training, validating and testing of quality data, modelselection, implementation of appropriate resources and infrastructure for ML, along with ethical and professional acceptance (2) .  \nIn this mini review, we will assess current literature on application of ML in visceral surgery in the preoperative, intraoperative, and postoperative settings (Table 1) .  \nFrontiers in Surgery 01 [frontiersin.org](frontiersin.org)  \nFrontiers in Surgery 02 [frontiersin.org](frontiersin.org)  \nTABLE 1 Summary of stud","cbCaig8p19gwHrVK","https://ap.wps.com/l/cbCaig8p19gwHrVK","pdf",306356,1,"English","en",105,"# Introduction\n## Preoperative applications\n## Intraoperative applications\n## Postoperative applications\n## Challenges and implementation considerations","[{\"question\":\"What perioperative stages does the review cover for machine learning in visceral surgery?\",\"answer\":\"The review assesses applications in the preoperative, intraoperative, and postoperative settings.\"},{\"question\":\"What are key preoperative uses of machine learning models described in the review?\",\"answer\":\"Models are used to guide surgical indications, determine appropriate timing, calculate risks, prognosticate outcomes, and improve estimates of surgical time and required resources.\"},{\"question\":\"What are the main challenges to clinical implementation highlighted by the review?\",\"answer\":\"Implementation challenges include the need for more quantity and quality of standardized data, adequate resources and infrastructure for training and deployment, and addressing ethical and patient acceptance considerations.\"}]","Machine learning perioperative applications in visceral surgery - 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