Real-time detection of active bleeding in laparoscopic colectomy using artificial intelligence

Bohnen JD, Mavros MN, Ramly EP, Chang Y, Yeh DD, Lee J, de Moya M, King DR, Fagenholz PJ, Butler K, Velmahos GC, Kaafarani HMA (2017) Intraoperative adverse events in abdominal surgery: what happens in the operating room does not stay in the operating room. Ann Surg 265:1119–1125

Article  PubMed  Google Scholar 

Garbens A, Goldenberg M, Wallis CJD, Tricco A, Grantcharov TP (2018) The cost of intraoperative adverse events in abdominal and pelvic surgery: a systematic review. Am J Surg 215:163–170

Article  CAS  PubMed  Google Scholar 

Vassiliou MC, Feldman LS, Andrew CG, Bergman S, Leffondré K, Stanbridge D, Fried GM (2005) A global assessment tool for evaluation of intraoperative laparoscopic skills. Am J Surg 190:107–113

Article  PubMed  Google Scholar 

Champagne BJ, Steele SR, Hendren SK, Bakaki PM, Roberts PL, Delaney CP, Brady JT, MacRae HM (2017) The American Society of colon and rectal surgeons assessment tool for performance of laparoscopic colectomy. Dis Colon Rectum 60:738–744

Article  PubMed  Google Scholar 

Miskovic D, Ni M, Wyles SM, Parvaiz A, Hanna GB (2012) Observational clinical human reliability analysis (OCHRA) for competency assessment in laparoscopic colorectal surgery at the specialist level. Surg Endosc 26:796–803

Article  PubMed  Google Scholar 

Foster JD, Miskovic D, Allison AS, Conti JA, Ockrim J, Cooper EJ, Hanna GB, Francis NK (2016) Application of objective clinical human reliability analysis (OCHRA) in assessment of technical performance in laparoscopic rectal cancer surgery. Tech Coloproctol 20:361–367

Article  CAS  PubMed  Google Scholar 

Van Rutte P, Nienhuijs S, Jakimowicz J, Van Montfort G (2017) Identification of technical errors and hazard zones in sleeve gastrectomy using OCHRA. Surg Endosc 31:561–566

Article  PubMed  Google Scholar 

Jung JJ, Jüni P, Gee DW, Zak Y, Cheverie J, Yoo JS, Morton JM, Grantcharov T (2020) Development and evaluation of a novel instrument to measure severity of intraoperative events using video data. Ann Surg 272:220–226

Article  PubMed  Google Scholar 

Bonrath EM, Zevin B, Dedy NJ, Grantcharov TP (2013) Error rating tool to identify and analyse technical errors and events in laparoscopic surgery. Br J Surg 100:1080–1088

Article  CAS  PubMed  Google Scholar 

Wu WC, Smith TS, Henderson WG, Eaton CB, Poses RM, Uttley G, Mor V, Sharma SC, Vezeridis M, Khuri SF, Friedmann PD (2010) Operative blood loss, blood transfusion, and 30-day mortality in older patients after major noncardiac surgery. Ann Surg 252:11–17

Article  PubMed  Google Scholar 

Roshanov PS, Eikelboom JW, Sessler DI, Kearon C, Guyatt GH, Crowther M, Tandon V, Borges FK, Lamy A, Whitlock R, Biccard BM, Szczeklik W, Panju M, Spence J, Garg AX, McGillion M, VanHelder T, Kavsak PA, de Beer J, Winemaker M, Le Manach Y, Sheth T, Pinthus JH, Siegal D, Thabane L, Simunovic MRI, Mizera R, Ribas S, Devereaux PJ (2021) Bleeding Independently associated with Mortality after noncardiac Surgery (BIMS): an international prospective cohort study establishing diagnostic criteria and prognostic importance. Br J Anaesth 126:163–171

Article  CAS  PubMed  Google Scholar 

McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, Chesus M, Corrado GS, Darzi A, Etemadi M, Garcia-Vicente F, Gilbert FJ, Halling-Brown M, Hassabis D, Jansen S, Karthikesalingam A, Kelly CJ, King D, Ledsam JR, Melnick D, Mostofi H, Peng L, Reicher JJ, Romera-Paredes B, Sidebottom R, Suleyman M, Tse D, Young KC, De Fauw J, Shetty S (2020) International evaluation of an AI system for breast cancer screening. Nature 577:89–94

Article  CAS  PubMed  Google Scholar 

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–118

Article  CAS  PubMed  PubMed Central  Google Scholar 

Misawa M, Kudo SE, Mori Y, Cho T, Kataoka S, Yamauchi A, Ogawa Y, Maeda Y, Takeda K, Ichimasa K, Nakamura H, Yagawa Y, Toyoshima N, Ogata N, Kudo T, Hisayuki T, Hayashi T, Wakamura K, Baba T, Ishida F, Itoh H, Roth H, Oda M, Mori K (2018) Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology 154:2027-2029.e2023

Article  PubMed  Google Scholar 

Hirasawa T, Aoyama K, Tanimoto T, Ishihara S, Shichijo S, Ozawa T, Ohnishi T, Fujishiro M, Matsuo K, Fujisaki J, Tada T (2018) Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 21:653–660

Article  PubMed  Google Scholar 

Madani A, Namazi B, Altieri MS, Hashimoto DA, Rivera AM, Pucher PH, Navarrete-Welton A, Sankaranarayanan G, Brunt LM, Okrainec A, Alseidi A (2020) Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy. Ann Surg. https://doi.org/10.1097/SLA.0000000000004594

Article  PubMed  Google Scholar 

Kumazu Y, Kobayashi N, Kitamura N, Rayan E, Neculoiu P, Misumi T, Hojo Y, Nakamura T, Kumamoto T, Kurahashi Y, Ishida Y, Masuda M, Shinohara H (2021) Automated segmentation by deep learning of loose connective tissue fibers to define safe dissection planes in robot-assisted gastrectomy. Sci Rep 11:21198

Article  PubMed  PubMed Central  Google Scholar 

Tokuyasu T, Iwashita Y, Matsunobu Y, Kamiyama T, Ishikake M, Sakaguchi S, Ebe K, Tada K, Endo Y, Etoh T, Nakashima M, Inomata M (2021) Development of an artificial intelligence system using deep learning to indicate anatomical landmarks during laparoscopic cholecystectomy. Surg Endosc 35:1651–1658

Article  PubMed  Google Scholar 

Kitaguchi D, Takeshita N, Matsuzaki H, Igaki T, Hasegawa H, Kojima S, Mori K, Ito M (2022) Real-time vascular anatomical image navigation for laparoscopic surgery: experimental study. Surg Endosc 36:6105–6112

Article  PubMed  Google Scholar 

Sato Y, Sese J, Matsuyama T, Onuki M, Mase S, Okuno K, Saito K, Fujiwara N, Hoshino A, Kawada K, Tokunaga M, Kinugasa Y (2022) Preliminary study for developing a navigation system for gastric cancer surgery using artificial intelligence. Surg Today 52:1753–1758

Article  PubMed  Google Scholar 

Sato K, Fujita T, Matsuzaki H, Takeshita N, Fujiwara H, Mitsunaga S, Kojima T, Mori K, Daiko H (2022) Real-time detection of the recurrent laryngeal nerve in thoracoscopic esophagectomy using artificial intelligence. Surg Endosc 36:5531–5539

Article  PubMed  Google Scholar 

Takeuchi M, Collins T, Lipps C, Haller M, Uwineza J, Okamoto N, Nkusi R, Marescaux J, Kawakubo H, Kitagawa Y, Gonzalez C, Mutter D, Perretta S, Hostettler A, Dallemagne B (2023) Towards automatic verification of the critical view of the myopectineal orifice with artificial intelligence. Surg Endosc 37:4525–4534

Article  PubMed  Google Scholar 

Yamazaki Y, Kanaji S, Matsuda T, Oshikiri T, Nakamura T, Suzuki S, Hiasa Y, Otake Y, Sato Y, Kakeji Y (2020) Automated surgical instrument detection from laparoscopic gastrectomy video images using an open source convolutional neural network platform. J Am Coll Surg 230:725-732.e721

Article  PubMed  Google Scholar 

Kitaguchi D, Lee Y, Hayashi K, Nakajima K, Kojima S, Hasegawa H, Takeshita N, Mori K, Ito M (2022) Development and validation of a model for laparoscopic colorectal surgical instrument recognition using convolutional neural network-based instance segmentation and videos of laparoscopic procedures. JAMA Netw Open 5:e2226265

Article  PubMed  PubMed Central  Google Scholar 

Kitaguchi D, Takeshita N, Matsuzaki H, Takano H, Owada Y, Enomoto T, Oda T, Miura H, Yamanashi T, Watanabe M, Sato D, Sugomori Y, Hara S, Ito M (2020) Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach. Surg Endosc 34:4924–4931

Article  PubMed  Google Scholar 

Shinozuka K, Turuda S, Fujinaga A, Nakanuma H, Kawamura M, Matsunobu Y, Tanaka Y, Kamiyama T, Ebe K, Endo Y, Etoh T, Inomata M, Tokuyasu T (2022) Artificial intelligence software available for medical devices: surgical phase recognition in laparoscopic cholecystectomy. Surg Endosc 36:7444–7452

Article  PubMed  PubMed Central  Google Scholar 

Funke I, Mees ST, Weitz J, Speidel S (2019) Video-based surgical skill assessment using 3D convolutional neural networks. Int J Comput Assist Radiol Surg 14:1217–1225

Article  PubMed  Google Scholar 

Kitaguchi D, Takeshita N, Matsuzaki H, Igaki T, Hasegawa H, Ito M (2021) Development and validation of a 3-dimensional convolutional neural network for automatic surgical skill assessment based on spatiotemporal video analysis. JAMA Netw Open 4:e2120786

Article  PubMed  PubMed Central  Google Scholar 

Igaki T, Kitaguchi D, Matsuzaki H, Nakajima K, Kojima S, Hasegawa H, Takeshita N, Kinugasa Y, Ito M (2023) Automatic surgical skill assessment system based on concordance of standardized surgical field development using artificial intelligence. JAMA Surg 158:e231131

Article  PubMed  Google Scholar 

Kiyasseh D, Ma R, Haque TF, Miles BJ, Wagner C, Donoho DA, Anandkumar A, Hung AJ (2023) A vision transformer for decoding surgeon activity from surgical videos. Nat Biomed Eng 7:780–796

Article  PubMed  PubMed Central  Google Scholar 

Kyungmin J, Bareum C, Songe C, Youngjin M, Jaesoon C (2016) Automatic detection of hemorrhage and surgical instrument in laparoscopic surgery image. Annu Int Conf IEEE Eng Med Biol Soc 2016:1260–1263

Google Scholar 

Garcia-Martinez A, Vicente-Samper JM, Sabater-Navarro JM (2017) Automatic detection of surgical haemorrhage using computer vision. Artif Intell Med 78:55–60

Article  PubMed  Google Scholar 

Okamoto T, Ohnishi T, Kawahira H, Dergachyava O, Jannin P, Haneishi H (2019) Real-time identification of blood regions for hemostasis support in laparoscopic surgery. SIViP 13:405–412

Article  Google Scholar 

Mongan J, Moy L, Kahn CE Jr (2020) Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2:e200029

Article  PubMed  PubMed Central  Google Scholar 

World Medical Association Declaration of Helsinki (2013) ethical principles for medical research involving human subjects. JAMA 310:2191–2194

Article  Google Scholar 

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