Skip to main content

Advertisement

SpringerLink
Log in

Machine learning in gastrointestinal surgery

  • Review Article
  • Published:
Surgery Today Aims and scope Submit manuscript

Abstract

Machine learning (ML) is a collection of algorithms allowing computers to learn directly from data without predetermined equations. It is used widely to analyze “big data”. In gastrointestinal surgery, surgeons deal with various data such as clinical parameters, surgical videos, and pathological images, to stratify surgical risk, perform safe surgery and predict patient prognosis. In the current “big data” era, the accelerating accumulation of a large amount of data drives studies using ML algorithms. Three subfields of ML are supervised learning, unsupervised learning, and reinforcement learning. In this review, we summarize applications of ML to surgical practice in the preoperative, intraoperative, and postoperative phases of care. Prediction and stratification using ML is promising; however, the current overarching concern is the availability of ML models. Information systems that can manage “big data” and integrate ML models into electronic health records are essential to incorporate ML into daily practice. ML is fundamental technology to meaningfully process data that exceeds the capacity of the human mind to comprehend. The accelerating accumulation of a large amount of data is changing the nature of surgical practice fundamentally. Artificial intelligence (AI), represented by ML, is being incorporated into daily surgical practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Bilimoria KY, Liu Y, Paruch JL, Zhou L, Kmiecik TE, Ko CY, et al. Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg. 2013;217(5):833-842 e1-3.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Boffa DJ, Rosen JE, Mallin K, Loomis A, Gay G, Palis B, et al. Using the National Cancer Database for outcomes research: a review. JAMA Oncol. 2017;3(12):1722–8.

    Article  PubMed  Google Scholar 

  3. Doll KM, Rademaker A, Sosa JA. Practical guide to surgical data sets: surveillance, epidemiology, and end results (SEER) database. JAMA Surg. 2018;153(6):588–9.

    Article  PubMed  Google Scholar 

  4. Lee TH, Marcantonio ER, Mangione CM, Thomas EJ, Polanczyk CA, Cook EF, et al. Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery. Circulation. 1999;100(10):1043–9.

    Article  CAS  PubMed  Google Scholar 

  5. Beal EW, Saunders ND, Kearney JF, Lyon E, Wei L, Squires MH, et al. Accuracy of the ACS NSQIP online risk calculator depends on how you look at it: results from the United States gastric cancer collaborative. Am Surg. 2018;84(3):358–64.

    Article  PubMed  Google Scholar 

  6. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–58.

    Article  PubMed  Google Scholar 

  7. Hung AJ, Chen J, Gill IS. Automated performance metrics and machine learning algorithms to measure surgeon performance and anticipate clinical outcomes in robotic surgery. JAMA Surg. 2018;153(8):770–1.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kilic A, Goyal A, Miller JK, Gjekmarkaj E, Tam WL, Gleason TG, et al. Predictive utility of a machine learning algorithm in estimating mortality risk in cardiac surgery. Ann Thorac Surg. 2020;109(6):1811–9.

    Article  PubMed  Google Scholar 

  9. Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–30.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Seymour CW, Kennedy JN, Wang S, Chang CH, Elliott CF, Xu Z, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA. 2019;321(20):2003–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Loftus TJ, Tighe PJ, Filiberto AC, Efron PA, Brakenridge SC, Mohr AM, et al. Artificial intelligence and surgical decision-making. JAMA Surg. 2020;155(2):148–58.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24(11):1716–20.

    Article  CAS  PubMed  Google Scholar 

  13. Horie Y, Yoshio T, Aoyama K, Yoshimizu S, Horiuchi Y, Ishiyama A, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc. 2019;89(1):25–32.

    Article  PubMed  Google Scholar 

  14. Guo L, Xiao X, Wu C, Zeng X, Zhang Y, Du J, et al. Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos). Gastrointest Endosc. 2020;91(1):41–51.

    Article  PubMed  Google Scholar 

  15. Hashimoto R, Requa J, Dao T, Ninh A, Tran E, Mai D, et al. Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett’s esophagus (with video). Gastrointest Endosc. 2020;91(6):1264-1271 e1.

    Article  PubMed  Google Scholar 

  16. de Groof AJ, Struyvenberg MR, van der Putten J, van der Sommen F, Fockens KN, Curvers WL, et al. Deep-learning system detects neoplasia in patients with Barrett’s esophagus with higher accuracy than endoscopists in a multistep training and validation study with benchmarking. Gastroenterology. 2020;158(4):915-929.e4.

    Article  PubMed  Google Scholar 

  17. Wu L, Zhou W, Wan X, Zhang J, Shen L, Hu S, et al. A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy. 2019;51(6):522–31.

    Article  PubMed  Google Scholar 

  18. Hirasawa T, Aoyama K, Tanimoto T, Ishihara S, Shichijo S, Ozawa T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer. 2018;21(4):653–60.

    Article  PubMed  Google Scholar 

  19. Ikenoyama Y, Hirasawa T, Ishioka M, Namikawa K, Yoshimizu S, Horiuchi Y, et al. Detecting early gastric cancer: comparison between the diagnostic ability of convolutional neural networks and endoscopists. Dig Endosc: Off J Jpn Gastroenterol Endosc Soc. 2020. https://doi.org/10.1111/den.13688.

    Article  Google Scholar 

  20. Zhu Y, Wang QC, Xu MD, Zhang Z, Cheng J, Zhong YS, et al. Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy. Gastrointest Endosc. 2019;89(4):806-815 e1.

    Article  PubMed  Google Scholar 

  21. Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology. 2018;155(4):1069-1078 e8.

    Article  PubMed  Google Scholar 

  22. Luo H, Xu G, Li C, He L, Luo L, Wang Z, et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Lancet Oncol. 2019;20(12):1645–54.

    Article  CAS  PubMed  Google Scholar 

  23. Hamm CA, Wang CJ, Savic LJ, Ferrante M, Schobert I, Schlachter T, et al. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. Eur Radiol. 2019;29(7):3338–47.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Liu X, Khalvati F, Namdar K, Fischer S, Lewis S, Taouli B, et al. Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning? Eur Radiol. 2020. https://doi.org/10.1007/s00330-020-07119-7.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Luo Y, Chen X, Chen J, Song C, Shen J, Xiao H, et al. Preoperative prediction of pancreatic neuroendocrine neoplasms grading based on enhanced computed tomography imaging: validation of deep learning with a convolutional neural network. Neuroendocrinology. 2020;110(5):338–50.

    Article  CAS  PubMed  Google Scholar 

  26. Liu XL, Shao CY, Sun L, Liu YY, Hu LW, Cong ZZ, et al. An artificial neural network model predicting pathologic nodal metastases in clinical stage I-II esophageal squamous cell carcinoma patients. J Thorac Dis. 2020;12(10):5580–92.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Jin C, Jiang Y, Yu H, Wang W, Li B, Chen C, et al. Deep learning analysis of the primary tumour and the prediction of lymph node metastases in gastric cancer. Br J Surg. 2020;108(5):542–9.

    Article  Google Scholar 

  28. Dong D, Fang MJ, Tang L, Shan XH, Gao JB, Giganti F, et al. Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study. Ann Oncol. 2020;31(7):912–20.

    Article  CAS  PubMed  Google Scholar 

  29. Liu C, Qi L, Feng QX, Sun SW, Zhang YD, Liu XS. Performance of a machine learning-based decision model to help clinicians decide the extent of lymphadenectomy (D1 vs. D2) in gastric cancer before surgical resection. Abdom Radiol (NY). 2019;44(9):3019–29.

    Article  Google Scholar 

  30. Fukagawa T, Katai H, Mizusawa J, Nakamura K, Sano T, Terashima M, et al. A prospective multi-institutional validity study to evaluate the accuracy of clinical diagnosis of pathological stage III gastric cancer (JCOG1302A). Gastric Cancer. 2018;21(1):68–73.

    Article  PubMed  Google Scholar 

  31. Taghavi M, Trebeschi S, Simoes R, Meek DB, Beckers RCJ, Lambregts DMJ, et al. Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases. Abdom Radiol (NY). 2020. https://doi.org/10.1007/s00261-020-02624-1.

    Article  Google Scholar 

  32. Yuan Z, Xu T, Cai J, Zhao Y, Cao W, Fichera A, et al. Development and validation of an image-based deep learning algorithm for detection of synchronous peritoneal carcinomatosis in colorectal cancer. Ann Surg. 2020. https://doi.org/10.1097/SLA.0000000000004229.

    Article  PubMed  Google Scholar 

  33. Shung DL, Au B, Taylor RA, Tay JK, Laursen SB, Stanley AJ, et al. Validation of a machine learning model that outperforms clinical risk scoring systems for upper gastrointestinal bleeding. Gastroenterology. 2020;158(1):160–7.

    Article  PubMed  Google Scholar 

  34. Loftus TJ, Brakenridge SC, Croft CA, Smith RS, Efron PA, Moore FA, et al. Neural network prediction of severe lower intestinal bleeding and the need for surgical intervention. J Surg Res. 2017;212:42–7.

    Article  PubMed  Google Scholar 

  35. Que SJ, Chen QY, Qing Z, Liu ZY, Wang JB, Lin JX, et al. Application of preoperative artificial neural network based on blood biomarkers and clinicopathological parameters for predicting long-term survival of patients with gastric cancer. World J Gastroenterol. 2019;25(43):6451–64.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Zhao B, Gabriel RA, Vaida F, Eisenstein S, Schnickel GT, Sicklick JK, et al. Using machine learning to construct nomograms for patients with metastatic colon cancer. Colorectal Dis. 2020;22(8):914–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Paredes AZ, Hyer JM, Tsilimigras DI, Moro A, Bagante F, Guglielmi A, et al. A novel machine-learning approach to predict recurrence after resection of colorectal liver metastases. Ann Surg Oncol. 2020;27(13):5139–47.

    Article  PubMed  Google Scholar 

  38. Hyer JM, Ejaz A, Tsilimigras DI, Paredes AZ, Mehta R, Pawlik TM. Novel machine learning approach to identify preoperative risk factors associated with super-utilization of medicare expenditure following surgery. JAMA Surg. 2019;154(11):1014–21.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Hyer JM, White S, Cloyd J, Dillhoff M, Tsung A, Pawlik TM, et al. Can we improve prediction of adverse surgical outcomes? Development of a surgical complexity score using a novel machine learning technique. J Am Coll Surg. 2020;230(1):43–52.

    Article  PubMed  Google Scholar 

  40. Wolters U, Wolf T, Stutzer H, Schroder T. ASA classification and perioperative variables as predictors of postoperative outcome. Br J Anaesth. 1996;77(2):217–22.

    Article  CAS  PubMed  Google Scholar 

  41. Bihorac A, Ozrazgat-Baslanti T, Ebadi A, Motaei A, Madkour M, Pardalos PM, et al. MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery. Ann Surg. 2019;269(4):652–62.

    Article  PubMed  Google Scholar 

  42. Brennan M, Puri S, Ozrazgat-Baslanti T, Feng Z, Ruppert M, Hashemighouchani H, et al. Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: a pilot usability study. Surgery. 2019;165(5):1035–45.

    Article  PubMed  Google Scholar 

  43. Bertsimas D, Dunn J, Velmahos GC, Kaafarani HMA. 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. Ann Surg. 2018;268(4):574–83.

    Article  PubMed  Google Scholar 

  44. Chen W, Lu Z, You L, Zhou L, Xu J, Chen K. Artificial intelligence-based multimodal risk assessment model for surgical site infection (AMRAMS): development and validation study. JMIR Med Inform. 2020;8(6):e18186.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Merath K, Hyer JM, Mehta R, Farooq A, Bagante F, Sahara K, et al. Use of machine learning for prediction of patient risk of postoperative complications after liver, pancreatic, and colorectal surgery. J Gastrointest Surg: Off J Soc Surg Aliment Tract. 2020;24(8):1843–51.

    Article  Google Scholar 

  46. Mai RY, Lu HZ, Bai T, Liang R, Lin Y, Ma L, et al. Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma. Surgery. 2020;168(4):643–52.

    Article  PubMed  Google Scholar 

  47. Kambakamba P, Mannil M, Herrera PE, Muller PC, Kuemmerli C, Linecker M, et al. The potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced CT: a proof-of-principle study. Surgery. 2020;167(2):448–54.

    Article  PubMed  Google Scholar 

  48. Garrow CR, Kowalewski KF, Li L, Wagner M, Schmidt MW, Engelhardt S, et al. Machine learning for surgical phase recognition: a systematic review. Ann Surg. 2020. https://doi.org/10.1097/SLA.0000000000004425.

    Article  Google Scholar 

  49. Winkler-Schwartz A, Yilmaz R, Mirchi N, Bissonnette V, Ledwos N, Siyar S, et al. Machine learning identification of surgical and operative factors associated with surgical expertise in virtual reality simulation. JAMA Netw Open. 2019;2(8):e198363.

    Article  PubMed  Google Scholar 

  50. Ward TM, Mascagni P, Ban Y, Rosman G, Padoy N, Meireles O, et al. Computer vision in surgery. Surgery. 2020. https://doi.org/10.1016/j.surg.2020.10.039.

    Article  PubMed  Google Scholar 

  51. Nguyen XA, Ljuhar D, Pacilli M, Nataraja RM, Chauhan S. Surgical skill levels: classification and analysis using deep neural network model and motion signals. Comput Methods Programs Biomed. 2019;177:1–8.

    Article  PubMed  Google Scholar 

  52. Gao Y, Kruger U, Intes X, Schwaitzberg S, De S. A machine learning approach to predict surgical learning curves. Surgery. 2020;167(2):321–7.

    Article  PubMed  Google Scholar 

  53. Madani A, Namazi B, Altieri MS, Hashimoto DA, Rivera AM, Pucher PH, et al. Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy. Ann Surg. 2020. https://doi.org/10.1097/SLA.0000000000004594.

    Article  PubMed  Google Scholar 

  54. Mascagni P, Vardazaryan A, Alapatt D, Urade T, Emre T, Fiorillo C, et al. Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann Surg. 2020. https://doi.org/10.1097/SLA.0000000000004351.

    Article  PubMed  Google Scholar 

  55. Guedon ACP, Meij SEP, Osman K, Kloosterman HA, van Stralen KJ, Grimbergen MCM, et al. Deep learning for surgical phase recognition using endoscopic videos. Surg Endosc. 2020. https://doi.org/10.1007/s00464-020-08110-5.

    Article  PubMed  Google Scholar 

  56. Kitaguchi D, Takeshita N, Matsuzaki H, Oda T, Watanabe M, Mori K, et al. Automated laparoscopic colorectal surgery workflow recognition using artificial intelligence: experimental research. Int J Surg. 2020;79:88–94.

    Article  PubMed  Google Scholar 

  57. Kitaguchi D, Takeshita N, Matsuzaki H, Takano H, Owada Y, Enomoto T, et al. Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach. Surg Endosc. 2020;34(11):4924–31.

    Article  PubMed  Google Scholar 

  58. Zhao Z, Cai T, Chang F, Cheng X. Real-time surgical instrument detection in robot-assisted surgery using a convolutional neural network cascade. Healthc Technol Lett. 2019;6(6):275–9.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Khalid S, Goldenberg M, Grantcharov T, Taati B, Rudzicz F. Evaluation of deep learning models for identifying surgical actions and measuring performance. JAMA Netw Open. 2020;3(3):e201664.

    Article  PubMed  Google Scholar 

  60. Luongo F, Hakim R, Nguyen JH, Anandkumar A, Hung AJ. Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery. Surgery. 2020. https://doi.org/10.1016/j.surg.2020.08.016.

    Article  PubMed  Google Scholar 

  61. Yamazaki Y, Kanaji S, Matsuda T, Oshikiri T, Nakamura T, Suzuki S, et al. Automated surgical instrument detection from laparoscopic gastrectomy video images using an open source convolutional neural network platform. J Am Coll Surg. 2020;230(5):725-32 e1.

    Article  PubMed  Google Scholar 

  62. Zhang Y, Zhu S, Yuan Z, Li Q, Ding R, Bao X, et al. Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis. BMC Cancer. 2020;20(1):1161.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Nudel J, Bishara AM, de Geus SWL, Patil P, Srinivasan J, Hess DT, et al. Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database. Surg Endosc. 2021;35(1):182–91.

    Article  PubMed  Google Scholar 

  64. Safavi KC, Khaniyev T, Copenhaver M, Seelen M, Zenteno Langle AC, Zanger J, et al. Development and validation of a machine learning model to aid discharge processes for inpatient surgical care. JAMA Netw Open. 2019;2(12):e1917221.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Jiang Y, Jin C, Yu H, Wu J, Chen C, Yuan Q, et al. Development and validation of a deep learning CT signature to predict survival and chemotherapy benefit in gastric cancer: a multicenter retrospective study. Ann Surg. 2020. https://doi.org/10.1097/SLA.0000000000003778.

    Article  PubMed  Google Scholar 

  66. Peng JH, Fang YJ, Li CX, Ou QJ, Jiang W, Lu SX, et al. A scoring system based on artificial neural network for predicting 10-year survival in stage II A colon cancer patients after radical surgery. Oncotarget. 2016;7(16):22939–47.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Rahman SA, Walker RC, Lloyd MA, Grace BL, van Boxel GI, Kingma BF, et al. Machine learning to predict early recurrence after oesophageal cancer surgery. Br J Surg. 2020;107(8):1042–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Skrede OJ, De Raedt S, Kleppe A, Hveem TS, Liestøl K, Maddison J, et al. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet (London, England). 2020;395(10221):350–60.

    Article  CAS  Google Scholar 

  69. Shi HY, Lee KT, Wang JJ, Sun DP, Lee HH, Chiu CC. Artificial neural network model for predicting 5-year mortality after surgery for hepatocellular carcinoma: a nationwide study. J Gastrointest Surg: Off J Soc Surg Aliment Tract. 2012;16(11):2126–31.

    Article  Google Scholar 

  70. Ji GW, Wang K, Xia YX, Wang JS, Wang XH, Li XC. Integrating machine learning and tumor immune signature to predict oncologic outcomes in resected biliary tract cancer. Ann Surg Oncol. 2020. https://doi.org/10.1245/s10434-020-09374-w.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Chen T, Liu S, Li Y, Feng X, Xiong W, Zhao X, et al. Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning. EBioMedicine. 2019;39:272–9.

    Article  PubMed  Google Scholar 

  72. Kather JN, Pearson AT, Halama N, Jager D, Krause J, Loosen SH, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019;25(7):1054–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Bychkov D, Linder N, Turkki R, Nordling S, Kovanen PE, Verrill C, et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci Rep. 2018;8(1):3395.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Kather JN, Krisam J, Charoentong P, Luedde T, Herpel E, Weis CA, et al. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med. 2019;16(1):e1002730.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Hoshino A, Kim HS, Bojmar L, Gyan KE, Cioffi M, Hernandez J, et al. Extracellular vesicle and particle biomarkers define multiple human cancers. Cell. 2020;182(4):1044-1061 e18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Bi Q, Goodman KE, Kaminsky J, Lessler J. What is machine learning? A primer for the epidemiologist. Am J Epidemiol. 2019;188(12):2222–39.

    PubMed  Google Scholar 

  77. Panesar S, Cagle Y, Chander D, Morey J, Fernandez-Miranda J, Kliot M. Artificial intelligence and the future of surgical robotics. Ann Surg. 2019;270(2):223–6.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Gastroenterological Surgery, Gastroenterological Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto, Tokyo, 135-8550, Japan

    Takashi Sakamoto

  2. Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan

    Takashi Sakamoto & Tadahiro Goto

  3. TXP Medical Co. Ltd, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 114-8485, Japan

    Tadahiro Goto

  4. Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA

    Michimasa Fujiogi

  5. Department of Pediatric Surgery, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan

    Michimasa Fujiogi

  6. Department of Surgery, Jichi Medical University, Shimotsuke, Tochigi, 3290498, Japan

    Alan Kawarai Lefor

Authors
  1. Takashi Sakamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tadahiro Goto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michimasa Fujiogi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alan Kawarai Lefor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Takashi Sakamoto.

Ethics declarations

Conflict of interest

Takashi Sakamoto and the co-authors report no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sakamoto, T., Goto, T., Fujiogi, M. et al. Machine learning in gastrointestinal surgery. Surg Today 52, 995–1007 (2022). https://doi.org/10.1007/s00595-021-02380-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00595-021-02380-9

Keywords

Search

Navigation