{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T18:01:56Z","timestamp":1789581716496,"version":"build-2803163510"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Surg Endosc"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1007\/s00464-026-13257-8","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T17:10:25Z","timestamp":1786641025000},"page":"7391-7410","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence for predicting surgical difficulty in laparoscopic cholecystectomy: a systematic review and meta-analysis"],"prefix":"10.1007","volume":"40","author":[{"given":"Haoyang","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuekai","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"key":"13257_CR1","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.1001\/jama.1993.03500080066034","volume":"269","author":"JL Gollan","year":"1993","unstructured":"Gollan JL, Bulkley GB, Diehl AM, Elashoff JD, Federle MP, Hogan WJ, Kelly KA, Massanari DL, Powell DW, Sorrell M, Wilson JAP, Barkun JST, Bass EB, Bernard H, Carr-Locke DL, Fitzgibbons RJ Jr, Friedman GD, Gadacz TR, Hofmann AF, Hunter JG, McSherry CK, Meyers WC, Moody FG, Nahrwold DL, Olsen DO, Pellegrini CA, Perissat J, Petelin JB, Phillips EH, Pitt HA, Schoenfield LJ, Soper NJ, Strasberg SM, Traverso LW, White JV, Zucker KA, Kaiser SC, Bray EA, Burton BT, Everhart J, Ferguson JH, Foster WR, Gadacz TK, Hall WH, Hamilton FA, Hoofnagle JH, Meyers W (1993) Gallstones and laparoscopic cholecystectomy. JAMA 269:1018\u20131024","journal-title":"JAMA"},{"key":"13257_CR2","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.ijsu.2015.04.083","volume":"18","author":"F Coccolini","year":"2015","unstructured":"Coccolini F, Catena F, Pisano M, Gheza F, Fagiuoli S, Di Saverio S, Leandro G, Montori G, Ceresoli M, Corbella D, Sartelli M, Sugrue M, Ansaloni L (2015) Open versus laparoscopic cholecystectomy in acute cholecystitis. Systematic review and meta-analysis. Int J Surg 18:196\u2013204","journal-title":"Int J Surg"},{"key":"13257_CR3","first-page":"187","volume":"44","author":"A Cuschieri","year":"1999","unstructured":"Cuschieri A (1999) Laparoscopic cholecystectomy. J R Coll Surg Edinb 44:187\u2013192","journal-title":"J R Coll Surg Edinb"},{"key":"13257_CR4","first-page":"CD006231","volume":"2006","author":"F Keus","year":"2006","unstructured":"Keus F, de Jong JA, Gooszen HG, van Laarhoven CJ (2006) Laparoscopic versus open cholecystectomy for patients with symptomatic cholecystolithiasis. Cochrane Database Syst Rev 2006:CD006231","journal-title":"Cochrane Database Syst Rev"},{"key":"13257_CR5","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1002\/jhbp.1258","volume":"30","author":"N Egawa","year":"2022","unstructured":"Egawa N, Miyoshi A, Manabe T, Sadashima E, Koga H, Sato H, Ikeda O, Tanaka T, Kitahara K, Noshiro H (2022) Clinical evaluation of a surgical difficulty score for laparoscopic cholecystectomy for acute cholecystitis proposed in the Tokyo Guidelines 2018. J Hepatobiliary Pancreat Sci 30:625\u2013632","journal-title":"J Hepatobiliary Pancreat Sci"},{"key":"13257_CR6","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1002\/jhbp.284","volume":"22","author":"G Paul Wright","year":"2015","unstructured":"Paul Wright G, Stilwell K, Johnson J, Hefty MT, Chung MH (2015) Predicting length of stay and conversion to open cholecystectomy for acute cholecystitis using the 2013 Tokyo Guidelines in a US population. J Hepatobiliary Pancreat Sci 22:795\u2013801","journal-title":"J Hepatobiliary Pancreat Sci"},{"key":"13257_CR7","first-page":"1871","volume":"109","author":"C Ram\u00edrez-Giraldo","year":"2023","unstructured":"Ram\u00edrez-Giraldo C, Isaza-Restrepo A, Conde Monroy D, Castillo-Barbosa AC, Rubio-Avilez JJ, Van-Londo\u00f1o I (2023) What is the best score for predicting difficult laparoscopic cholecystectomy? A diagnostic trial study. Int J Surg 109:1871\u20131879","journal-title":"Int J Surg"},{"key":"13257_CR8","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1016\/j.asjsur.2020.11.018","volume":"44","author":"A Tongyoo","year":"2021","unstructured":"Tongyoo A, Chotiyasilp P, Sriussadaporn E, Limpavitayaporn P, Mingmalairak C (2021) The pre-operative predictive model for difficult elective laparoscopic cholecystectomy: a modification. Asian J Surg 44:656\u2013661","journal-title":"Asian J Surg"},{"key":"13257_CR9","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1002\/jhbp.517","volume":"25","author":"G Wakabayashi","year":"2018","unstructured":"Wakabayashi G, Iwashita Y, Hibi T, Takada T, Strasberg SM, Asbun HJ, Endo I, Umezawa A, Asai K, Suzuki K, Mori Y, Okamoto K, Pitt HA, Han H-S, Hwang T-L, Yoon Y-S, Yoon D-S, Choi I-S, Huang WS-W, Gim\u00e9nez ME, Garden OJ, Gouma DJ, Belli G, Dervenis C, Jagannath P, Chan ACW, Lau WY, Liu K-H, Su C-H, Misawa T, Nakamura M, Horiguchi A, Tagaya N, Fujioka S, Higuchi R, Shikata S, Noguchi Y, Ukai T, Yokoe M, Cherqui D, Honda G, Sugioka A, de Santiba\u00f1es E, Supe AN, Tokumura H, Kimura T, Yoshida M, Mayumi T, Kitano S, Inomata M, Hirata K, Sumiyama Y, Inui K, Yamamoto M (2018) Tokyo guidelines 2018: surgical management of acute cholecystitis: safe steps in laparoscopic cholecystectomy for acute cholecystitis (with videos). J Hepatobiliary Pancreat Sci 25:73\u201386","journal-title":"J Hepatobiliary Pancreat Sci"},{"key":"13257_CR10","doi-asserted-by":"publisher","first-page":"10","DOI":"10.4103\/ijabmr.ijabmr_553_22","volume":"13","author":"M Trehan","year":"2023","unstructured":"Trehan M, Mangotra V, Singh J, Singla S, Gautam SS, Garg R (2023) Evaluation of preoperative scoring system for predicting difficult laparoscopic cholecystectomy. Int J Appl Basic Med Res 13:10\u201315","journal-title":"Int J Appl Basic Med Res"},{"key":"13257_CR11","doi-asserted-by":"publisher","first-page":"63","DOI":"10.3109\/13645709509152757","volume":"4","author":"AHM Nassar","year":"1995","unstructured":"Nassar AHM, Ashkar KA, Mohamed AY, Hafiz AA (1995) Is laparoscopic cholecystectomy possible without video technology? Minim Invasive Ther 4:63\u201365","journal-title":"Minim Invasive Ther"},{"key":"13257_CR12","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1016\/j.amjsurg.2017.05.017","volume":"215","author":"TD Madni","year":"2017","unstructured":"Madni TD, Leshikar DE, Minshall CT, Nakonezny PA, Cornelius CC, Imran JB, Clark AT, Williams BH, Eastman AL, Minei JP, Phelan HA, Cripps MW (2017) The Parkland grading scale for cholecystitis. Am J Surg 215:625\u2013630","journal-title":"Am J Surg"},{"key":"13257_CR13","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s13017-015-0005-x","volume":"10","author":"M Sugrue","year":"2015","unstructured":"Sugrue M, Sahebally SM, Ansaloni L, Zielinski MD (2015) Grading operative findings at laparoscopic cholecystectomy- a new scoring system. World J Emerg Surg 10:14","journal-title":"World J Emerg Surg"},{"key":"13257_CR14","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.1007\/s00464-020-07548-x","volume":"35","author":"T Tokuyasu","year":"2020","unstructured":"Tokuyasu T, Iwashita Y, Matsunobu Y, Kamiyama T, Ishikake M, Sakaguchi S, Ebe K, Tada K, Endo Y, Etoh T, Nakashima M, Inomata M (2020) Development of an artificial intelligence system using deep learning to indicate anatomical landmarks during laparoscopic cholecystectomy. Surg Endosc 35:1651\u20131658","journal-title":"Surg Endosc"},{"key":"13257_CR15","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.i6460","volume":"356","author":"TPA Debray","year":"2017","unstructured":"Debray TPA, Damen JAAG, Snell KIE, Ensor J, Hooft L, Reitsma JB, Riley RD, Moons KGM (2017) A guide to systematic review and meta-analysis of prediction model performance. BMJ 356:i6460","journal-title":"BMJ"},{"key":"13257_CR16","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n2281","volume":"375","author":"CL Andaur Navarro","year":"2021","unstructured":"Andaur Navarro CL, Damen JAA, Takada T, Nijman SWJ, Dhiman P, Ma J, Collins GS, Bajpai R, Riley RD, Moons KGM, Hooft L (2021) Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ 375:n2281","journal-title":"BMJ"},{"key":"13257_CR17","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jclinepi.2019.02.004","volume":"110","author":"E Christodoulou","year":"2019","unstructured":"Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B (2019) A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 110:12\u201322","journal-title":"J Clin Epidemiol"},{"key":"13257_CR18","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1186\/s12916-019-1466-7","volume":"17","author":"B Van Calster","year":"2019","unstructured":"Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW (2019) Calibration: the Achilles heel of predictive analytics. BMC Med 17:230","journal-title":"BMC Med"},{"key":"13257_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2023.100804","volume":"4","author":"S Kapoor","year":"2023","unstructured":"Kapoor S, Narayanan A (2023) Leakage and the reproducibility crisis in machine-learning-based science. Patterns (N Y) 4:100804","journal-title":"Patterns (N Y)"},{"key":"13257_CR20","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-082505","volume":"388","author":"KGM Moons","year":"2025","unstructured":"Moons KGM, Damen JAA, Kaul T, Hooft L, Andaur Navarro C, Dhiman P, Beam AL, Van Calster B, Celi LA, Denaxas S, Denniston AK, Ghassemi M, Heinze G, Kengne AP, Maier-Hein L, Liu X, Logullo P, McCradden MD, Liu N, Oakden-Rayner L, Singh K, Ting DS, Wynants L, Yang B, Reitsma JB, Riley RD, Collins GS, van Smeden M (2025) PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ 388:e082505","journal-title":"BMJ"},{"key":"13257_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijsu.2021.105906","volume":"88","author":"MJ Page","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hr\u00f3bjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg 88:105906","journal-title":"Int J Surg"},{"key":"13257_CR22","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","volume":"170","author":"RF Wolff","year":"2019","unstructured":"Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S (2019) PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 170:51\u201358","journal-title":"Ann Intern Med"},{"key":"13257_CR23","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1016\/0895-4356(90)90159-M","volume":"43","author":"DV Cicchetti","year":"1990","unstructured":"Cicchetti DV, Feinstein AR (1990) High agreement but low kappa: II. Resolving the paradoxes. J Clin Epidemiol 43:551\u2013558","journal-title":"J Clin Epidemiol"},{"key":"13257_CR24","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1016\/0895-4356(90)90158-L","volume":"43","author":"AR Feinstein","year":"1990","unstructured":"Feinstein AR, Cicchetti DV (1990) High agreement but low kappa: I. The problems of two paradoxes. J Clin Epidemiol 43:543\u2013549","journal-title":"J Clin Epidemiol"},{"key":"13257_CR25","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1136\/bmj.39500.677199.AE","volume":"336","author":"HJ Sch\u00fcnemann","year":"2008","unstructured":"Sch\u00fcnemann HJ, Oxman AD, Brozek J, Glasziou P, Jaeschke R, Vist GE, Williams JW, Kunz R, Craig J, Montori VM, Bossuyt P, Guyatt GH (2008) Grading quality of evidence and strength of recommendations for diagnostic tests and strategies. BMJ 336:1106\u20131110","journal-title":"BMJ"},{"key":"13257_CR26","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1136\/bmj.39489.470347.AD","volume":"336","author":"GH Guyatt","year":"2008","unstructured":"Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, Sch\u00fcnemann HJ (2008) GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ 336:924\u2013926","journal-title":"BMJ"},{"key":"13257_CR27","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"JA Hanley","year":"1982","unstructured":"Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29\u201336","journal-title":"Radiology"},{"key":"13257_CR28","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1097\/EDE.0b013e3181c30fb2","volume":"21","author":"EW Steyerberg","year":"2010","unstructured":"Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, Pencina MJ, Kattan MW (2010) Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21:128\u2013138","journal-title":"Epidemiology"},{"key":"13257_CR29","doi-asserted-by":"publisher","first-page":"E901","DOI":"10.1503\/cmaj.200077","volume":"192","author":"S Schandelmaier","year":"2020","unstructured":"Schandelmaier S, Briel M, Varadhan R, Schmid CH, Devasenapathy N, Hayward RA, Gagnier J, Borenstein M, van der Heijden GJMG, Dahabreh IJ, Sun X, Sauerbrei W, Walsh M, Ioannidis JPA, Thabane L, Guyatt GH (2020) Development of the instrument to assess the credibility of effect modification analyses (ICEMAN) in randomized controlled trials and meta-analyses. Can Med Assoc J 192:E901\u2013E906","journal-title":"Can Med Assoc J"},{"key":"13257_CR30","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1148\/radiology.148.3.6878708","volume":"148","author":"JA Hanley","year":"1983","unstructured":"Hanley JA, McNeil BJ (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148:839\u2013843","journal-title":"Radiology"},{"key":"13257_CR31","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1016\/j.cmi.2022.07.019","volume":"29","author":"JAA Damen","year":"2022","unstructured":"Damen JAA, Moons KGM, van Smeden M, Hooft L (2022) How to conduct a systematic review and meta-analysis of prognostic model studies. Clin Microbiol Infect 29:434\u2013440","journal-title":"Clin Microbiol Infect"},{"key":"13257_CR32","doi-asserted-by":"publisher","first-page":"2693","DOI":"10.1002\/sim.1482","volume":"22","author":"G Knapp","year":"2003","unstructured":"Knapp G, Hartung J (2003) Improved tests for a random effects meta-regression with a single covariate. Stat Med 22:2693\u20132710","journal-title":"Stat Med"},{"key":"13257_CR33","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1002\/jrsm.5","volume":"1","author":"LV Hedges","year":"2010","unstructured":"Hedges LV, Tipton E, Johnson MC (2010) Robust variance estimation in meta-regression with dependent effect size estimates. Res Synth Methods 1:39\u201365","journal-title":"Res Synth Methods"},{"key":"13257_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i03","volume":"36","author":"W Viechtbauer","year":"2010","unstructured":"Viechtbauer W (2010) Conducting meta-analyses in R with the metafor package. J Stat Softw 36:1\u201348","journal-title":"J Stat Softw"},{"key":"13257_CR35","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1136\/bmj.327.7414.557","volume":"327","author":"JPT Higgins","year":"2003","unstructured":"Higgins JPT, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327:557\u2013560","journal-title":"BMJ"},{"key":"13257_CR36","doi-asserted-by":"publisher","first-page":"154","DOI":"10.20982\/tqmp.12.3.p154","volume":"12","author":"M Assink","year":"2016","unstructured":"Assink M, Wibbelink CJ (2016) Fitting three-level meta-analytic models in R: a step-by-step tutorial. Quant Methods Psychol 12:154\u2013174","journal-title":"Quant Methods Psychol"},{"key":"13257_CR37","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.44120","volume":"6","author":"H Bae","year":"2023","unstructured":"Bae H, Shin H, Ji H-G, Kwon JS, Kim H, Hur J-W (2023) App-based interventions for moderate to severe depression: a systematic review and meta-analysis. JAMA Netw Open 6:e2344120","journal-title":"JAMA Netw Open"},{"key":"13257_CR38","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1136\/bmj.315.7109.629","volume":"315","author":"M Egger","year":"1997","unstructured":"Egger M, Davey Smith G, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315:629\u2013634","journal-title":"BMJ"},{"key":"13257_CR39","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.artmed.2011.04.012","volume":"52","author":"L Bouarfa","year":"2011","unstructured":"Bouarfa L, Schneider A, Feussner H, Navab N, Lemke HU, Jonker PP, Dankelman J (2011) Prediction of intraoperative complexity from preoperative patient data for laparoscopic cholecystectomy. Artif Intell Med 52:169\u2013176","journal-title":"Artif Intell Med"},{"key":"13257_CR40","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1515\/bmt-2024-0431","volume":"70","author":"S Kostopoulos","year":"2025","unstructured":"Kostopoulos S, Cavouras D, Glotsos D, Loukas C (2025) Prediction of remaining surgery duration based on machine learning methods and laparoscopic annotation data. Biomed Tech (Berl) 70:229\u2013239","journal-title":"Biomed Tech (Berl)"},{"key":"13257_CR41","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2482-9-13","volume":"9","author":"C Gholipour","year":"2009","unstructured":"Gholipour C, Fakhree MB, Shalchi RA, Abbasi M (2009) Prediction of conversion of laparoscopic cholecystectomy to open surgery with artificial neural networks. BMC Surg 9:13","journal-title":"BMC Surg"},{"key":"13257_CR42","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1080\/21681163.2022.2163296","volume":"11","author":"JR Abbing","year":"2023","unstructured":"Abbing JR, Voskens FJ, Gerats BGA, Egging RM, Milletari F, Broeders IAMJ (2023) Towards an AI-based assessment model of surgical difficulty during early phase laparoscopic cholecystectomy. Comput Methods Biomech Biomed Eng Imaging Vis 11:1299\u20131306","journal-title":"Comput Methods Biomech Biomed Eng Imaging Vis"},{"key":"13257_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2023.102314","author":"J Wu","year":"2023","unstructured":"Wu J, Zou X, Tao R, Zheng G (2023) Nonlinear regression of remaining surgery duration from videos via Bayesian LSTM-based deep negative correlation learning. Comput Med Imaging Graph. https:\/\/doi.org\/10.1016\/j.compmedimag.2023.102314","journal-title":"Comput Med Imaging Graph"},{"key":"13257_CR44","doi-asserted-by":"publisher","first-page":"1582425","DOI":"10.3389\/fsurg.2025.1582425","volume":"12","author":"C Wang","year":"2025","unstructured":"Wang C, Wen J, Su Z, Yu H (2025) Machine learning algorithms as early diagnostic tools for prolonged operative time in patients with fluorescent laparoscopic cholecystectomy: a retrospective cohort study. Front Surg 12:1582425","journal-title":"Front Surg"},{"key":"13257_CR45","doi-asserted-by":"publisher","first-page":"1561769","DOI":"10.3389\/fmed.2025.1561769","volume":"12","author":"B Zhu","year":"2025","unstructured":"Zhu B, Wang Y, Zhang Z, Wang L, Ma Y, Li M (2025) Development and validation of a radiologically-based nomogram for preoperative prediction of difficult laparoscopic cholecystectomy. Front Med (Lausanne) 12:1561769","journal-title":"Front Med (Lausanne)"},{"key":"13257_CR46","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1109\/TMI.2018.2878055","volume":"38","author":"AP Twinanda","year":"2019","unstructured":"Twinanda AP, Yengera G, Mutter D, Marescaux J, Padoy N (2019) RSDNet: learning to predict remaining surgery duration from laparoscopic videos without manual annotations. IEEE Trans Med Imaging 38:1069\u20131078","journal-title":"IEEE Trans Med Imaging"},{"key":"13257_CR47","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/s12262-024-04083-0","volume":"87","author":"R Agrawal","year":"2025","unstructured":"Agrawal R, Hossain S, Bisht H, Sista R, Chakrabarti PP, Sheet D, De U (2025) Artificial intelligence (AI)-based model for prediction of adversity outcome following laparoscopic cholecystectomy-a preliminary report. Indian J Surg 87:52\u201359","journal-title":"Indian J Surg"},{"key":"13257_CR48","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1007\/s11548-025-03372-7","volume":"20","author":"S Sharma","year":"2025","unstructured":"Sharma S, Vannucci M, Pestana Legori L, Scaglia M, Laracca GG, Mutter D, Alfieri S, Mascagni P, Padoy N (2025) Early operative difficulty assessment in laparoscopic cholecystectomy via snapshot-centric video analysis. Int J Comput Assist Radiol Surg 20:1185\u20131193","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"13257_CR49","doi-asserted-by":"publisher","first-page":"e2632","DOI":"10.1002\/rcs.2632","volume":"20","author":"C Loukas","year":"2024","unstructured":"Loukas C, Seimenis I, Prevezanou K, Schizas D (2024) Prediction of remaining surgery duration in laparoscopic videos based on visual saliency and the transformer network. Int J Med Robot 20:e2632","journal-title":"Int J Med Robot"},{"key":"13257_CR50","doi-asserted-by":"publisher","first-page":"6670","DOI":"10.1007\/s00464-025-12067-8","volume":"39","author":"NAR Mej\u00eda","year":"2025","unstructured":"Mej\u00eda NAR, De la Cruz Rey S, Cervantes-S\u00e1nchez CR, Figueroa EC, Alonso GR (2025) Development and validation of the ENDOLAP artificial intelligence framework for inflammation severity classification in laparoscopic cholecystectomy: a cross-sectional study. Surg Endosc 39:6670\u20136684","journal-title":"Surg Endosc"},{"key":"13257_CR51","doi-asserted-by":"publisher","first-page":"6832","DOI":"10.1007\/s00464-022-09009-z","volume":"36","author":"TM Ward","year":"2022","unstructured":"Ward TM, Hashimoto DA, Ban Y, Rosman G, Meireles OR (2022) Artificial intelligence prediction of cholecystectomy operative course from automated identification of gallbladder inflammation. Surg Endosc 36:6832\u20136840","journal-title":"Surg Endosc"},{"key":"13257_CR52","doi-asserted-by":"publisher","first-page":"6560","DOI":"10.1007\/s00464-025-12015-6","volume":"39","author":"GH Olsen","year":"2025","unstructured":"Olsen GH, Goodman ED, Aklilu JG, Bartoletti S, Hung KS, Yang JH, Sorenson EC, Jopling JK, Yeung SY, Azagury DE (2025) Using artificial intelligence to model expert panel diagnosis of cholecystitis severity. Surg Endosc 39:6560\u20136568","journal-title":"Surg Endosc"},{"key":"13257_CR53","doi-asserted-by":"publisher","first-page":"49","DOI":"10.21037\/tgh-24-124","volume":"10","author":"K Huang","year":"2025","unstructured":"Huang K, Jia S, Yuan X, Zhao P, Bai D (2025) Development and validation of a machine learning-based nomogram for preoperative prediction of laparoscopic surgical difficulty in gallstone patients. Transl Gastroenterol Hepatol 10:49","journal-title":"Transl Gastroenterol Hepatol"},{"key":"13257_CR54","doi-asserted-by":"publisher","DOI":"10.1007\/s13304-026-02526-9","author":"N Yang","year":"2026","unstructured":"Yang N, Zhang W, Liu J, Yang C, Ding Z (2026) Predicting the surgical difficulty of laparoscopic cholecystectomy after percutaneous transhepatic gallbladder drainage in patients with acute cholecystitis: a multi-center study. Updates Surg. https:\/\/doi.org\/10.1007\/s13304-026-02526-9","journal-title":"Updates Surg"},{"key":"13257_CR55","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s00268-001-0185-2","volume":"26","author":"S Eldar","year":"2002","unstructured":"Eldar S, Siegelmann HT, Buzaglo D, Matter I, Cohen A, Sabo E, Abrahamson J (2002) Conversion of laparoscopic cholecystectomy to open cholecystectomy in acute cholecystitis: artificial neural networks improve the prediction of conversion. World J Surg 26:79\u201385","journal-title":"World J Surg"},{"key":"13257_CR56","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1186\/s13017-025-00635-1","volume":"20","author":"RT Sun","year":"2025","unstructured":"Sun RT, Li CL, Jiang YM, Hao AY, Liu K, Li K, Tan B, Yang XN, Cui JF, Bai WY, Hu WY, Cao JY, Qu C (2025) A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study. World J Emerg Surg 20:62","journal-title":"World J Emerg Surg"},{"key":"13257_CR57","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1001\/jama.2017.12126","volume":"318","author":"AC Alba","year":"2017","unstructured":"Alba AC, Agoritsas T, Walsh M, Hanna S, Iorio A, Devereaux PJ, McGinn T, Guyatt G (2017) Discrimination and calibration of clinical prediction models: users\u2019 guides to the medical literature. JAMA 318:1377\u20131384","journal-title":"JAMA"},{"key":"13257_CR58","doi-asserted-by":"publisher","first-page":"195","DOI":"10.4103\/tjem.tjem_182_23","volume":"23","author":"\u015eK \u00c7orbac\u0131o\u011flu","year":"2023","unstructured":"\u00c7orbac\u0131o\u011flu \u015eK, Aksel G (2023) Receiver operating characteristic curve analysis in diagnostic accuracy studies: a guide to interpreting the area under the curve value. Turk J Emerg Med 23:195\u2013198","journal-title":"Turk J Emerg Med"},{"key":"13257_CR59","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1038\/s41591-022-01981-2","volume":"28","author":"JN Acosta","year":"2022","unstructured":"Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ (2022) Multimodal biomedical AI. Nat Med 28:1773\u20131784","journal-title":"Nat Med"},{"key":"13257_CR60","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltrusaitis","year":"2018","unstructured":"Baltrusaitis T, Ahuja C, Morency L-P (2018) Multimodal machine learning: a survey and taxonomy. IEEE Trans Pattern Anal Mach Intell 41:423\u2013443","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"13257_CR61","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00712-8","volume":"5","author":"A Kline","year":"2022","unstructured":"Kline A, Wang H, Li Y, Dennis S, Hutch M, Xu Z, Wang F, Cheng F, Luo Y (2022) Multimodal machine learning in precision health: a scoping review. NPJ Digit Med 5:171","journal-title":"NPJ Digit Med"},{"key":"13257_CR62","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1186\/s12893-025-02847-3","volume":"25","author":"HS Abdallah","year":"2025","unstructured":"Abdallah HS, Sedky MH, Sedky ZH (2025) The difficult laparoscopic cholecystectomy: a narrative review. BMC Surg 25:156","journal-title":"BMC Surg"},{"key":"13257_CR63","doi-asserted-by":"publisher","DOI":"10.4274\/globecc.galenos.2025.75047","author":"P Ko\u00e7atakan","year":"2025","unstructured":"Ko\u00e7atakan P (2025) Factors that extend the operative time in laparoscopic cholecystectomy. Glob Emerg Crit Care. https:\/\/doi.org\/10.4274\/globecc.galenos.2025.75047","journal-title":"Glob Emerg Crit Care"},{"key":"13257_CR64","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1308\/rcsann.2017.0171","volume":"100","author":"H Tafazal","year":"2018","unstructured":"Tafazal H, Spreadborough P, Zakai D, Shastri-Hurst N, Ayaani S, Hanif M (2018) Laparoscopic cholecystectomy: a prospective cohort study assessing the impact of grade of operating surgeon on operative time and 30-day morbidity. Ann R Coll Surg Engl 100:178\u2013184","journal-title":"Ann R Coll Surg Engl"},{"key":"13257_CR65","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1186\/s13017-020-00336-x","volume":"15","author":"M Pisano","year":"2020","unstructured":"Pisano M, Allievi N, Gurusamy K, Borzellino G, Cimbanassi S, Boerna D, Coccolini F, Tufo A, Di Martino M, Leung J, Sartelli M, Ceresoli M, Maier RV, Poiasina E, De Angelis N, Magnone S, Fugazzola P, Paolillo C, Coimbra R, Di Saverio S, De Simone B, Weber DG, Sakakushev BE, Lucianetti A, Kirkpatrick AW, Fraga GP, Wani I, Biffl WL, Chiara O, Abu-Zidan F, Moore EE, Lepp\u00e4niemi A, Kluger Y, Catena F, Ansaloni L (2020) 2020 World Society of Emergency Surgery updated guidelines for the diagnosis and treatment of acute calculus cholecystitis. World J Emerg Surg 15:61","journal-title":"World J Emerg Surg"},{"key":"13257_CR66","doi-asserted-by":"publisher","first-page":"555","DOI":"10.18203\/2349-2902.isj20250814","volume":"12","author":"A Kapoor","year":"2025","unstructured":"Kapoor A, Sidhu B, Singh J (2025) Artificial intelligence-based detection of pre-operative surgical difficulty in laparoscopic cholecystectomy patients using gradient based model. Int Surg J 12:555\u2013561","journal-title":"Int Surg J"},{"key":"13257_CR67","doi-asserted-by":"publisher","DOI":"10.1186\/s12893-023-02036-0","volume":"23","author":"Y-Q Liu","year":"2023","unstructured":"Liu Y-Q, Wang C, Cai X, Zheng Z-X, Bi J-T (2023) Can the Parkland grading scale predict the difficulty of laparoscopic cholecystectomy? A new approach to validation. BMC Surg 23:142","journal-title":"BMC Surg"},{"key":"13257_CR68","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1002\/jhbp.516","volume":"25","author":"K Okamoto","year":"2017","unstructured":"Okamoto K, Suzuki K, Takada T, Strasberg SM, Asbun HJ, Endo I, Iwashita Y, Hibi T, Pitt HA, Umezawa A, Asai K, Han H-S, Hwang T-L, Mori Y, Yoon Y-S, Huang WS-W, Belli G, Dervenis C, Yokoe M, Kiriyama S, Itoi T, Jagannath P, Garden OJ, Miura F, Nakamura M, Horiguchi A, Wakabayashi G, Cherqui D, de Santiba\u00f1es E, Shikata S, Noguchi Y, Ukai T, Higuchi R, Wada K, Honda G, Supe AN, Yoshida M, Mayumi T, Gouma DJ, Deziel DJ, Liau K-H, Chen M-F, Shibao K, Liu K-H, Su C-H, Chan ACW, Yoon D-S, Choi I-S, Jonas E, Chen X-P, Fan ST, Ker C-G, Gim\u00e9nez ME, Kitano S, Inomata M, Hirata K, Inui K, Sumiyama Y, Yamamoto M (2017) Tokyo guidelines 2018: flowchart for the management of acute cholecystitis. J Hepatobiliary Pancreat Sci 25:55\u201372","journal-title":"J Hepatobiliary Pancreat Sci"},{"key":"13257_CR69","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1007\/s00464-011-1978-5","volume":"26","author":"DT Farkas","year":"2011","unstructured":"Farkas DT, Moradi D, Moaddel D, Nagpal K, Cosgrove JM (2011) The impact of body mass index on outcomes after laparoscopic cholecystectomy. Surg Endosc 26:964\u2013969","journal-title":"Surg Endosc"},{"key":"13257_CR70","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1007\/s00464-006-9084-9","volume":"21","author":"Y Seki","year":"2007","unstructured":"Seki Y, Ohue M, Sekimoto M, Takiguchi S, Takemasa I, Ikeda M, Yamamoto H, Monden M (2007) Evaluation of the technical difficulty performing laparoscopic resection of a rectosigmoid carcinoma: visceral fat reflects technical difficulty more accurately than body mass index. Surg Endosc 21:929\u2013934","journal-title":"Surg Endosc"},{"key":"13257_CR71","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/S0140-6736(03)15268-3","volume":"363","author":"WHO Expert Consultation","year":"2004","unstructured":"WHO Expert Consultation (2004) Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363:157\u2013163","journal-title":"Lancet"},{"key":"13257_CR72","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.1001\/jamainternmed.2018.3763","volume":"178","author":"MA Gianfrancesco","year":"2018","unstructured":"Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G (2018) Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med 178:1544\u20131547","journal-title":"JAMA Intern Med"},{"key":"13257_CR73","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1186\/s12916-019-1426-2","volume":"17","author":"CJ Kelly","year":"2019","unstructured":"Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D (2019) Key challenges for delivering clinical impact with artificial intelligence. BMC Med 17:195","journal-title":"BMC Med"}],"container-title":["Surgical Endoscopy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00464-026-13257-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00464-026-13257-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00464-026-13257-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T17:02:44Z","timestamp":1789578164000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00464-026-13257-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,13]]},"references-count":73,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["13257"],"URL":"https:\/\/doi.org\/10.1007\/s00464-026-13257-8","relation":{},"ISSN":["0930-2794","1432-2218"],"issn-type":[{"value":"0930-2794","type":"print"},{"value":"1432-2218","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,13]]},"assertion":[{"value":"14 May 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Haoyang Liu, Xuekai Wu, and Fang Luo have no conflicts of interest or financial ties to disclose.","order":1,"name":"Ethics","label":"Disclosures","group":{"name":"EthicsHeading","label":"Declarations"}}]}}