{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T23:06:46Z","timestamp":1775516806333,"version":"3.50.1"},"reference-count":97,"publisher":"Georg Thieme Verlag KG","issue":"03","funder":[{"name":"Cleveland Clinic Digestive Disease Institute Chief's Innovation and Research Award","award":["DDICIRA033"],"award-info":[{"award-number":["DDICIRA033"]}]},{"name":"Cleveland Clinic Catalyst Grant"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Clin Colon Rectal Surg"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) is rapidly transforming surgical practice with growing applications in colon and rectal surgery. This review explores perioperative AI tools that assist with operative planning, intraoperative guidance, and outcome optimization. Preoperative innovations include machine learning models that outperform traditional risk calculators for predicting complications and readmissions, as well as computer vision and radiomics for analyzing imaging in colorectal cancer and inflammatory bowel disease. The integration of molecular and multiomics data further enhances personalized, precision surgical planning. Intraoperatively, deep learning enables computational identification of critical anatomy, including vascular structures, ureters, and pelvic nerves, and supports the objective analysis of advanced imaging techniques such as indocyanine green fluorescence. In terms of surgical techniques, AI-driven video analysis facilitates surgical phase recognition and automated skill assessment, whereas emerging vision\u2013language models and surgical foundation models promise improved documentation and context-aware guidance. Future directions include generative AI for simulation, AI-based coaching, and progress toward autonomous surgical robotics. Although research remains in the early stages and is not yet ready for widespread clinical implementation, ongoing work within the field of colorectal surgery underscores the potential of AI to augment decision-making and standardize surgical care.<\/jats:p>","DOI":"10.1055\/a-2769-1052","type":"journal-article","created":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:13:43Z","timestamp":1767226423000},"page":"185-193","source":"Crossref","is-referenced-by-count":0,"title":["The Role of Perioperative Artificial Intelligence in Colon and Rectal Surgery"],"prefix":"10.1055","volume":"39","author":[{"given":"Kevin A.","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Colon and Rectal Surgery, Digestive Disease Institute, Cleveland Clinic, Cleveland, Ohio, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shah","family":"Feroz","sequence":"additional","affiliation":[{"name":"Department of Colon and Rectal Surgery, Digestive Disease Institute, Cleveland Clinic, Cleveland, Ohio, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan D.","family":"Holubar","sequence":"additional","affiliation":[{"name":"Department of Colon and Rectal Surgery, Digestive Disease Institute, Cleveland Clinic, Cleveland, Ohio, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2025,12,31]]},"reference":[{"issue":"11","key":"ref1","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1001\/jama.2018.11100","article-title":"Deep learning-A technology with the potential to transform health care","volume":"320","author":"G Hinton","year":"2018","journal-title":"JAMA"},{"issue":"7956","key":"ref2","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","article-title":"Foundation models for generalist medical artificial intelligence","volume":"616","author":"M Moor","year":"2023","journal-title":"Nature"},{"issue":"01","key":"ref3","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.amjsurg.2018.11.017","article-title":"ACS-NSQIP risk calculator predicts cohort but not individual risk of complication following colorectal resection","volume":"218","author":"L Z Hyde","year":"2019","journal-title":"Am J Surg"},{"issue":"05","key":"ref4","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1016\/j.jamcollsurg.2017.07.1069","article-title":"American College of Surgeons NSQIP Risk Calculator accuracy for emergent and elective colorectal operations","volume":"225","author":"A L Lubitz","year":"2017","journal-title":"J Am Coll Surg"},{"issue":"04","key":"ref5","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.jamcollsurg.2015.12.034","article-title":"Failure of colorectal surgical site infection predictive models applied to an independent dataset: Do they add value or just confusion?","volume":"222","author":"J R Bergquist","year":"2016","journal-title":"J Am Coll Surg"},{"issue":"08","key":"ref6","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1007\/s11605-019-04338-2","article-title":"Use of machine learning for prediction of patient risk of postoperative complications after liver, pancreatic, and colorectal surgery","volume":"24","author":"K Merath","year":"2020","journal-title":"J Gastrointest Surg"},{"issue":"08","key":"ref7","doi-asserted-by":"crossref","first-page":"1732","DOI":"10.1007\/s11605-022-05332-x","article-title":"Differential performance of machine learning models in prediction of procedure-specific outcomes","volume":"26","author":"K A Chen","year":"2022","journal-title":"J Gastrointest Surg"},{"issue":"11","key":"ref8","doi-asserted-by":"crossref","first-page":"2342","DOI":"10.1007\/s11605-022-05443-5","article-title":"Development and validation of machine learning models to predict readmission after colorectal surgery","volume":"26","author":"K A Chen","year":"2022","journal-title":"J Gastrointest Surg"},{"issue":"03","key":"ref9","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1097\/DCR.0000000000002559","article-title":"Improved prediction of surgical-site infection after colorectal surgery using machine learning","volume":"66","author":"K A Chen","year":"2023","journal-title":"Dis Colon Rectum"},{"issue":"03","key":"ref10","doi-asserted-by":"crossref","first-page":"921","DOI":"10.21037\/jgo-20-436","article-title":"Machine learning-based random forest predicts anastomotic leakage after anterior resection for rectal cancer","volume":"12","author":"R Wen","year":"2021","journal-title":"J Gastrointest Oncol"},{"issue":"01","key":"ref11","doi-asserted-by":"crossref","first-page":"101772","DOI":"10.4240\/wjgs.v17.i1.101772","article-title":"Machine learning and deep learning to improve prevention of anastomotic leak after rectal cancer surgery","volume":"17","author":"F Celotto","year":"2025","journal-title":"World J Gastrointest Surg"},{"issue":"07","key":"ref12","doi-asserted-by":"crossref","first-page":"3672","DOI":"10.1007\/s00464-024-10926-4","article-title":"Machine learning-based preoperative analytics for the prediction of anastomotic leakage in colorectal surgery: A Swiss pilot study","volume":"38","author":"S Taha-Mehlitz","year":"2024","journal-title":"Surg Endosc"},{"issue":"22","key":"ref13","doi-asserted-by":"crossref","first-page":"921","DOI":"10.2217\/bmm-2023-0355","article-title":"Nomogram for predicting prolonged postoperative ileus in colorectal cancer based on age and inflammatory markers","volume":"17","author":"Z Q Fan","year":"2023","journal-title":"Biomarkers Med"},{"issue":"12","key":"ref14","doi-asserted-by":"crossref","first-page":"5702","DOI":"10.1177\/00031348231173981","article-title":"Prediction of ureteral injury during colorectal surgery using machine learning","volume":"89","author":"K A Chen","year":"2023","journal-title":"Am Surg"},{"issue":"04","key":"ref15","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1097\/SLA.0000000000002956","article-title":"Surgical risk is not linear: Derivation and validation of a novel, user-friendly, and machine-learning-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator","volume":"268","author":"D Bertsimas","year":"2018","journal-title":"Ann Surg"},{"issue":"04","key":"ref16","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1097\/TA.0000000000004030","article-title":"Artificial intelligence versus surgeon gestalt in predicting risk of emergency general surgery","volume":"95","author":"M El Moheb","year":"2023","journal-title":"J Trauma Acute Care Surg"},{"issue":"03","key":"ref17","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1097\/XCS.0000000000001234","article-title":"Validation of artificial intelligence-based POTTER Calculator in emergency general surgery patients undergoing laparotomy: Prospective, bi-institutional study","volume":"240","author":"V S Panossian","year":"2025","journal-title":"J Am Coll Surg"},{"issue":"04","key":"ref18","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1001\/jamasurg.2020.6361","article-title":"Effect of a predictive model on planned surgical duration accuracy, patient wait time, and use of presurgical resources: A randomized clinical trial","volume":"156","author":"C T Str\u00f6mblad","year":"2021","journal-title":"JAMA Surg"},{"issue":"06","key":"ref19","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1097\/SLA.0000000000005936","article-title":"Development, deployment, and implementation of a machine learning surgical case length prediction model and prospective evaluation","volume":"278","author":"H Zaribafzadeh","year":"2023","journal-title":"Ann Surg"},{"issue":"01","key":"ref20","doi-asserted-by":"crossref","first-page":"e44909","DOI":"10.2196\/44909","article-title":"Machine learning for the prediction of procedural case durations developed using a large multicenter database: Algorithm development and validation study","volume":"2","author":"S Kendale","year":"2023","journal-title":"JMIR AI"},{"issue":"11","key":"ref21","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1001\/jama.2018.11103","article-title":"On the prospects for a (deep) learning health care system","volume":"320","author":"C D Naylor","year":"2018","journal-title":"JAMA"},{"issue":"01","key":"ref22","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1038\/s41746-022-00742-2","article-title":"A large language model for electronic health records","volume":"5","author":"X Yang","year":"2022","journal-title":"NPJ Digit Med"},{"issue":"01","key":"ref23","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1097\/SLA.0000000000004354","article-title":"Accelerating surgical site infection abstraction with a semi-automated machine-learning approach","volume":"276","author":"S J Skube","year":"2022","journal-title":"Ann Surg"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"1464104","DOI":"10.3389\/fonc.2024.1464104","article-title":"Radiomics in precision medicine for colorectal cancer: A bibliometric analysis (2013-2023)","volume":"14","author":"H Li","year":"2024","journal-title":"Front Oncol"},{"issue":"02","key":"ref25","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1148\/radiol.211986","article-title":"MRI radiomics model predicts pathologic complete response of rectal cancer following chemoradiotherapy","volume":"303","author":"J Shin","year":"2022","journal-title":"Radiology"},{"issue":"01","key":"ref26","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1186\/s12876-024-03274-z","article-title":"Comparison of conventional MRI analysis versus MRI-based radiomics to predict the circumferential margin resection involvement of rectal cancer","volume":"24","author":"H Liang","year":"2024","journal-title":"BMC Gastroenterol"},{"issue":"01","key":"ref27","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s40644-025-00876-y","article-title":"Development and validation of machine learning models for predicting no. 253 lymph node metastasis in left-sided colorectal cancer using clinical and CT-based radiomic features","volume":"25","author":"H Zhang","year":"2025","journal-title":"Cancer Imaging"},{"issue":"03","key":"ref28","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1007\/s00464-024-11485-4","article-title":"Deep learning-based pelvimetry in pelvic MRI volumes for pre-operative difficulty assessment of total mesorectal excision","volume":"39","author":"S C Baltus","year":"2025","journal-title":"Surg Endosc"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"1337219","DOI":"10.3389\/fonc.2024.1337219","article-title":"Interpretable machine learning model to predict surgical difficulty in laparoscopic resection for rectal cancer","volume":"14","author":"M Yu","year":"2024","journal-title":"Front Oncol"},{"issue":"04","key":"ref30","doi-asserted-by":"crossref","first-page":"468","DOI":"10.3390\/bioengineering10040468","article-title":"Establishment of surgical difficulty grading system and application of MRI-based artificial intelligence to stratify difficulty in laparoscopic rectal surgery","volume":"10","author":"Z Sun","year":"2023","journal-title":"Bioengineering (Basel)"},{"issue":"03","key":"ref31","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1093\/ibd\/izac211","article-title":"Integrating radiomics with clinicoradiological scoring can predict high-risk patients who need surgery in Crohn's disease: A pilot study","volume":"29","author":"P Chirra","year":"2023","journal-title":"Inflamm Bowel Dis"},{"issue":"10","key":"ref32","doi-asserted-by":"crossref","first-page":"1660","DOI":"10.1093\/ecco-jcc\/jjae073","article-title":"Radiomics to detect inflammation and fibrosis on magnetic resonance enterography in stricturing Crohn's disease","volume":"18","author":"P Chirra","year":"2024","journal-title":"J Crohns Colitis"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"101805","DOI":"10.1016\/j.eclinm.2022.101805","article-title":"CT-based radiomics signature of visceral adipose tissue for prediction of disease progression in patients with Crohn's disease: A multicentre cohort study","volume":"56","author":"X Li","year":"2022","journal-title":"EClinicalMedicine"},{"issue":"03","key":"ref34","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1093\/ibd\/izae161","article-title":"Unraveling twisted Pouch Syndrome: A narrative review of classification, diagnosis, treatment, and prevention","volume":"31","author":"S D Holubar","year":"2025","journal-title":"Inflamm Bowel Dis"},{"issue":"09","key":"ref35","doi-asserted-by":"crossref","first-page":"1514","DOI":"10.1093\/ecco-jcc\/jjae058","article-title":"Three-dimensional pouchography: A proof-of-concept study of a breakthrough technique for visualising ileoanal pouch anatomy and morphology in normal and mechanical pouch complication patients","volume":"18","author":"S D Holubar","year":"2024","journal-title":"J Crohns Colitis"},{"issue":"01","key":"ref36","doi-asserted-by":"crossref","first-page":"2450207","DOI":"10.1080\/19490976.2025.2450207","article-title":"Host-microbe multi-omics and succinotype profiling have prognostic value for future relapse in patients with inflammatory bowel disease","volume":"17","author":"J O'Sullivan","year":"2025","journal-title":"Gut Microbes"},{"issue":"01","key":"ref37","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1093\/ibd\/izy228","article-title":"Predicting risk of postoperative disease recurrence in Crohn's disease: Patients with indolent Crohn's disease have distinct whole transcriptome profiles at the time of first surgery","volume":"25","author":"K C Cushing","year":"2019","journal-title":"Inflamm Bowel Dis"},{"key":"ref39","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s10620-024-08595-3","article-title":"Post-operative Crohn's disease recurrence and infectious complications: A transcriptomic analysis","volume":"70","author":"K A Chen","year":"2025","journal-title":"Dig Dis Sci"},{"issue":"08","key":"ref40","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1038\/s41591-022-01930-z","article-title":"Genomic and transcriptomic determinants of response to neoadjuvant therapy in rectal cancer","volume":"28","author":"W K Chatila","year":"2022","journal-title":"Nat Med"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"102002","DOI":"10.1016\/j.media.2021.102002","article-title":"Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy","volume":"70","author":"S Ali","year":"2021","journal-title":"Med Image Anal"},{"issue":"10","key":"ref42","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1016\/S2468-1253(24)00161-4","article-title":"Polyp detection with colonoscopy assisted by the GI Genius artificial intelligence endoscopy module compared with standard colonoscopy in routine colonoscopy practice (COLO-DETECT): a multicentre, open-label, parallel-arm, pragmatic randomised controlled trial","volume":"9","author":"A Seager","year":"2024","journal-title":"Lancet Gastroenterol Hepatol"},{"issue":"02","key":"ref43","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1097\/SLA.0000000000004594","article-title":"Artificial intelligence for intraoperative guidance: Using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy","volume":"276","author":"A Madani","year":"2022","journal-title":"Ann Surg"},{"issue":"04","key":"ref44","doi-asserted-by":"crossref","first-page":"e48","DOI":"10.1016\/j.jamcollsurg.2020.08.118","article-title":"AI for automated detection of the establishment of critical view of safety in laparoscopic cholecystectomy videos","volume":"231","author":"B Namazi","year":"2020","journal-title":"J Am Coll Surg"},{"key":"ref45","doi-asserted-by":"crossref","first-page":"997911","DOI":"10.3389\/fonc.2022.997911","article-title":"A novel 3D deep learning model to automatically demonstrate renal artery segmentation and its validation in nephron-sparing surgery","volume":"12","author":"S Zhang","year":"2022","journal-title":"Front Oncol"},{"issue":"03","key":"ref46","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/s11548-019-02102-0","article-title":"Kidney edge detection in laparoscopic image data for computer-assisted surgery: Kidney edge detection","volume":"15","author":"G Hattab","year":"2020","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"01","key":"ref47","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s00464-023-10524-w","article-title":"Deep learning-based vessel automatic recognition for laparoscopic right hemicolectomy","volume":"38","author":"K Ryu","year":"2024","journal-title":"Surg Endosc"},{"issue":"10","key":"ref48","doi-asserted-by":"crossref","first-page":"e1596","DOI":"10.1097\/DCR.0000000000003335","article-title":"Deep learning-based real-time ureter identification in laparoscopic colorectal surgery","volume":"67","author":"S Narihiro","year":"2024","journal-title":"Dis Colon Rectum"},{"issue":"06","key":"ref49","doi-asserted-by":"crossref","first-page":"3610","DOI":"10.1007\/s00464-025-11694-5","article-title":"Deep learning-based intraoperative visual guidance model for ureter identification in laparoscopic sigmoidectomy","volume":"39","author":"B Khojah","year":"2025","journal-title":"Surg Endosc"},{"issue":"06","key":"ref52","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1007\/s00464-020-07659-5","article-title":"Computer-assisted real-time automatic prostate segmentation during TaTME: A single-center feasibility study","volume":"35","author":"D Kitaguchi","year":"2021","journal-title":"Surg Endosc"},{"issue":"05","key":"ref53","doi-asserted-by":"crossref","first-page":"e329","DOI":"10.1097\/DCR.0000000000002393","article-title":"Artificial intelligence-based total mesorectal excision plane navigation in laparoscopic colorectal surgery","volume":"65","author":"T Igaki","year":"2022","journal-title":"Dis Colon Rectum"},{"issue":"04","key":"ref54","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1097\/JS9.0000000000000317","article-title":"Deep-learning-based semantic segmentation of autonomic nerves from laparoscopic images of colorectal surgery: An experimental pilot study","volume":"109","author":"S Kojima","year":"2023","journal-title":"Int J Surg"},{"key":"ref55","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1097\/DCR.0000000000003547","article-title":"Artificial intelligence recognition system of pelvic autonomic nerves in total mesorectal excision","volume":"68","author":"F Han","year":"2025","journal-title":"Dis Colon Rectum"},{"issue":"10","key":"ref56","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1093\/bjs\/znad249","article-title":"Artificial intelligence for the recognition of key anatomical structures in laparoscopic colorectal surgery","volume":"110","author":"D Kitaguchi","year":"2023","journal-title":"Br J Surg"},{"issue":"04","key":"ref57","doi-asserted-by":"crossref","first-page":"e688","DOI":"10.1097\/SLA.0000000000005907","article-title":"Blood perfusion assessment by indocyanine green fluorescence imaging for minimally invasive rectal cancer surgery (EssentiAL trial): A randomized clinical trial","volume":"278","author":"J Watanabe","year":"2023","journal-title":"Ann Surg"},{"issue":"10","key":"ref58","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1016\/S2468-1253(24)00198-5","article-title":"Indocyanine green near-infrared fluorescence bowel perfusion assessment to prevent anastomotic leakage in minimally invasive colorectal surgery (AVOID): a multicentre, randomised, controlled, phase 3 trial","volume":"9","author":"R A Faber","year":"2024","journal-title":"Lancet Gastroenterol Hepatol"},{"issue":"05","key":"ref59","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1001\/jamasurg.2025.0006","article-title":"Indocyanine green fluorescence imaging in prevention of colorectal anastomotic leakage: A randomized clinical trial","volume":"160","author":"J KA Rinne","year":"2025","journal-title":"JAMA Surg"},{"issue":"03","key":"ref60","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1177\/15266028221081085","article-title":"Normalization of time-intensity curves for quantification of foot perfusion using near-infrared fluorescence imaging with indocyanine green","volume":"30","author":"P Van Den Hoven","year":"2023","journal-title":"J Endovasc Ther"},{"issue":"06","key":"ref61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JBO.25.6.066002","article-title":"Intraoperative fluorescence perfusion assessment should be corrected by a measured subject-specific arterial input function","volume":"25","author":"J T Elliott","year":"2020","journal-title":"J Biomed Opt"},{"issue":"09","key":"ref62","doi-asserted-by":"crossref","first-page":"6824","DOI":"10.1007\/s00464-023-10140-8","article-title":"Quantification of indocyanine green near-infrared fluorescence bowel perfusion assessment in colorectal surgery","volume":"37","author":"R A Faber","year":"2023","journal-title":"Surg Endosc"},{"issue":"05","key":"ref63","first-page":"433","article-title":"Perfusion parameters in near-infrared fluorescence imaging with indocyanine green: A systematic review of the literature","volume":"11","author":"L N Goncalves","year":"2021","journal-title":"Life (Basel)"},{"issue":"23","key":"ref64","doi-asserted-by":"crossref","first-page":"5528","DOI":"10.3390\/cancers15235528","article-title":"NIR ICG-enhanced fluorescence: A quantitative evaluation of bowel microperfusion and its relation to central perfusion in colorectal surgery","volume":"15","author":"N Depalma","year":"2023","journal-title":"Cancers (Basel)"},{"issue":"44","key":"ref65","doi-asserted-by":"crossref","first-page":"6945","DOI":"10.3748\/wjg.v26.i44.6945","article-title":"Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery","volume":"26","author":"S H Park","year":"2020","journal-title":"World J Gastroenterol"},{"issue":"04","key":"ref66","doi-asserted-by":"crossref","first-page":"2677","DOI":"10.1007\/s00464-025-11621-8","article-title":"Quantification of indocyanine green fluorescence angiography in colorectal surgery: A systematic review of the literature","volume":"39","author":"P D McEntee","year":"2025","journal-title":"Surg Endosc"},{"issue":"01","key":"ref67","doi-asserted-by":"crossref","first-page":"14682","DOI":"10.1038\/s41598-022-16030-8","article-title":"Assessment of blood perfusion quality in laparoscopic colorectal surgery by means of machine learning","volume":"12","author":"P Arpaia","year":"2022","journal-title":"Sci Rep"},{"issue":"04","key":"ref68","doi-asserted-by":"crossref","first-page":"e246548","DOI":"10.1001\/jamanetworkopen.2024.6548","article-title":"ICG-fluorescence imaging for margin assessment during minimally invasive colorectal liver metastasis resection","volume":"7","author":"F B Achterberg","year":"2024","journal-title":"JAMA Netw Open"},{"issue":"01","key":"ref69","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1093\/bjs\/znaa004","article-title":"Artificial intelligence indocyanine green (ICG) perfusion for colorectal cancer intra-operative tissue classification","volume":"108","author":"R A Cahill","year":"2021","journal-title":"Br J Surg"},{"issue":"01","key":"ref70","doi-asserted-by":"crossref","first-page":"11349","DOI":"10.1038\/s41598-021-90089-7","article-title":"Digital dynamic discrimination of primary colorectal cancer using systemic indocyanine green with near-infrared endoscopy","volume":"11","author":"J Dalli","year":"2021","journal-title":"Sci Rep"},{"key":"ref71","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1097\/DCR.0000000000003547","article-title":"Artificial intelligence recognition system of pelvic autonomic nerve during total mesorectal excision","volume":"68","author":"F Han","year":"2025","journal-title":"Dis Colon Rectum"},{"issue":"02","key":"ref72","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s00384-020-03755-z","article-title":"Comparison of hyperspectral imaging and fluorescence angiography for the determination of the transection margin in colorectal resections-a comparative study","volume":"36","author":"B Jansen-Winkeln","year":"2021","journal-title":"Int J Colorectal Dis"},{"issue":"06","key":"ref73","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1097\/DCR.0000000000003098","article-title":"Clinical utility of laser speckle contrast imaging and real-time quantification of bowel perfusion in minimally invasive left-sided colorectal resections","volume":"67","author":"G C Skinner","year":"2024","journal-title":"Dis Colon Rectum"},{"issue":"12","key":"ref74","doi-asserted-by":"crossref","first-page":"9139","DOI":"10.1007\/s00464-023-10493-0","article-title":"Dye-free visualisation of intestinal perfusion using laser speckle contrast imaging in laparoscopic surgery: A prospective, observational multi-centre study","volume":"37","author":"W Heeman","year":"2023","journal-title":"Surg Endosc"},{"issue":"06","key":"ref75","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1038\/s41551-023-01010-8","article-title":"A vision transformer for decoding surgeon activity from surgical videos","volume":"7","author":"D Kiyasseh","year":"2023","journal-title":"Nat Biomed Eng"},{"issue":"01","key":"ref76","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1038\/s41597-021-00882-2","article-title":"Heidelberg colorectal data set for surgical data science in the sensor operating room","volume":"8","author":"L Maier-Hein","year":"2021","journal-title":"Sci Data"},{"issue":"01","key":"ref77","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1038\/s41597-024-04152-9","article-title":"A Rectal Cancer Surgery Dataset: Use of artificial intelligence to aid automation of error identification","volume":"12","author":"W Ghamrawi","year":"2025","journal-title":"Sci Data"},{"issue":"11","key":"ref78","doi-asserted-by":"crossref","first-page":"4924","DOI":"10.1007\/s00464-019-07281-0","article-title":"Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach","volume":"34","author":"D Kitaguchi","year":"2020","journal-title":"Surg Endosc"},{"issue":"01","key":"ref79","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s00423-024-03502-w","article-title":"Deep learning-based surgical step recognition for laparoscopic right-sided colectomy","volume":"409","author":"R Honda","year":"2024","journal-title":"Langenbecks Arch Surg"},{"issue":"02","key":"ref80","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1007\/s00464-021-08381-6","article-title":"Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision","volume":"36","author":"D Kitaguchi","year":"2022","journal-title":"Surg Endosc"},{"issue":"12","key":"ref81","doi-asserted-by":"crossref","first-page":"106996","DOI":"10.1016\/j.ejso.2023.106996","article-title":"Artificial Intelligence for context-aware surgical guidance in complex robot-assisted oncological procedures: An exploratory feasibility study","volume":"50","author":"F R Kolbinger","year":"2024","journal-title":"Eur J Surg Oncol"},{"issue":"08","key":"ref82","doi-asserted-by":"crossref","first-page":"e2120786","DOI":"10.1001\/jamanetworkopen.2021.20786","article-title":"Development and validation of a 3-dimensional convolutional neural network for automatic surgical skill assessment based on spatiotemporal video analysis","volume":"4","author":"D Kitaguchi","year":"2021","journal-title":"JAMA Netw Open"},{"issue":"08","key":"ref83","doi-asserted-by":"crossref","first-page":"e231131","DOI":"10.1001\/jamasurg.2023.1131","article-title":"Automatic surgical skill assessment system based on concordance of standardized surgical field development using artificial intelligence","volume":"158","author":"T Igaki","year":"2023","journal-title":"JAMA Surg"},{"issue":"08","key":"ref84","doi-asserted-by":"crossref","first-page":"e231140","DOI":"10.1001\/jamasurg.2023.1140","article-title":"Using artificial intelligence to assess surgeon skill","volume":"158","author":"J Wu","year":"2023","journal-title":"JAMA Surg"},{"issue":"02","key":"ref85","doi-asserted-by":"crossref","first-page":"e250","DOI":"10.1097\/SLA.0000000000005731","article-title":"Machine learning-based automatic evaluation of tissue handling skills in laparoscopic colorectal surgery: A retrospective experimental study","volume":"278","author":"S Sasaki","year":"2023","journal-title":"Ann Surg"},{"issue":"10","key":"ref86","doi-asserted-by":"crossref","first-page":"6423","DOI":"10.1007\/s00464-025-12036-1","article-title":"Automatic surgical skill assessment using a task classification model in laparoscopic sigmoidectomy","volume":"39","author":"K Obuchi","year":"2025","journal-title":"Surg Endosc"},{"issue":"11","key":"ref87","doi-asserted-by":"crossref","first-page":"6347","DOI":"10.1007\/s00464-024-11208-9","article-title":"Automated surgical skill assessment in colorectal surgery using a deep learning-based surgical phase recognition model","volume":"38","author":"K Nakajima","year":"2024","journal-title":"Surg Endosc"},{"issue":"01","key":"ref88","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1007\/s00423-025-03641-8","article-title":"Artificial intelligence assessment of tissue-dissection efficiency in laparoscopic colorectal surgery","volume":"410","author":"K Nakajima","year":"2025","journal-title":"Langenbecks Arch Surg"},{"issue":"15","key":"ref89","doi-asserted-by":"crossref","first-page":"5462","DOI":"10.3390\/jcm14155462","article-title":"A novel deep learning model for predicting colorectal anastomotic leakage: A pioneer multicenter Transatlantic study","volume":"14","author":"M Mascarenhas","year":"2025","journal-title":"J Clin Med"},{"issue":"11","key":"ref90","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1001\/jamasurg.2021.3651","article-title":"Deep learning to automate technical skills assessment in robotic surgery","volume":"156","author":"A J Hung","year":"2021","journal-title":"JAMA Surg"},{"key":"ref91","doi-asserted-by":"crossref","first-page":"105121","DOI":"10.1016\/j.compbiomed.2021.105121","article-title":"Automated tool detection with deep learning for monitoring kinematics and eye-hand coordination in microsurgery","volume":"141","author":"J Koskinen","year":"2022","journal-title":"Comput Biol Med"},{"key":"ref92","first-page":"281","volume-title":"SurgicalGPT: End-to-end language-vision GPT for visual question answering in surgery","author":"L Seenivasan","year":"2023"},{"issue":"05","key":"ref94","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1097\/XCS.0000000000001352","article-title":"Enhancing accuracy of operative reports with automated artificial intelligence analysis of surgical video","volume":"240","author":"A Khanna","year":"2025","journal-title":"J Am Coll Surg"},{"issue":"11","key":"ref95","doi-asserted-by":"crossref","first-page":"7777","DOI":"10.1097\/JS9.0000000000003069","article-title":"Vision-language models for automated video analysis and documentation in laparoscopic surgery: A proof-of-concept study","volume":"111","author":"E H Stueker","year":"2025","journal-title":"Int J Surg"},{"issue":"11","key":"ref97","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1038\/s42256-024-00917-4","article-title":"General-purpose foundation models for increased autonomy in robot-assisted surgery","volume":"6","author":"S Schmidgall","year":"2024","journal-title":"Nat Mach Intell"},{"issue":"07","key":"ref98","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1109\/TMI.2022.3222126","article-title":"Federated Cycling (FedCy): Semi-supervised federated learning of surgical phases","volume":"42","author":"H Kassem","year":"2023","journal-title":"IEEE Trans Med Imaging"},{"key":"ref99","first-page":"412","volume-title":"Mission balance: Generating under-represented class samples using video diffusion models","author":"D K Venkatesh","year":"2025"},{"issue":"09","key":"ref101","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1001\/jamasurg.2025.2564","article-title":"Artificial intelligence-augmented human instruction and surgical simulation performance: A randomized clinical trial","volume":"160","author":"B Giglio","year":"2025","journal-title":"JAMA Surg"},{"issue":"104","key":"ref102","doi-asserted-by":"crossref","first-page":"eadt5254","DOI":"10.1126\/scirobotics.adt5254","article-title":"SRT-H: A hierarchical framework for autonomous surgery via language-conditioned imitation learning","volume":"10","author":"J WB Kim","year":"2025","journal-title":"Sci Robot"},{"issue":"62","key":"ref103","doi-asserted-by":"crossref","first-page":"eabj2908","DOI":"10.1126\/scirobotics.abj2908","article-title":"Autonomous robotic laparoscopic surgery for intestinal anastomosis","volume":"7","author":"H Saeidi","year":"2022","journal-title":"Sci Robot"}],"container-title":["Clinics in Colon and Rectal Surgery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/a-2769-1052.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T22:39:21Z","timestamp":1775515161000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/a-2769-1052"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,31]]},"references-count":97,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2026,4,6]]},"published-print":{"date-parts":[[2026,5]]}},"URL":"https:\/\/doi.org\/10.1055\/a-2769-1052","archive":["Portico","CLOCKSS"],"relation":{},"ISSN":["1531-0043","1530-9681"],"issn-type":[{"value":"1531-0043","type":"print"},{"value":"1530-9681","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,31]]}}}