The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: A systematic review

Background: Conventional statistics are based on a simple cause-and-effect principle. Postoperative complications, however, have a multifactorial and interrelated etiology. The application of artificial intelligence might be more accurate to predict postoperative outcomes. The objective of this study was to determine the current quality of studies describing the use of artificial intelligence in predicting complications in patients undergoing major abdominal surgery. Methods: A literature search was performed in PubMed, Embase, and Web of Science. Inclusion criteria were (1) empirical studies including patients undergoing (2) any type of gastrointestinal surgery, including hepatopancreaticobiliary surgery, whose (3) complications or mortality were predicted with the use of (4) any artificial intelligence system. Studies were screened for description of method of validation and testing in methodology. Outcome measurements were sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. Results: From a total of 1,537 identified articles, 15 were included for the review. Among a large variety of algorithms used by the included studies, sensitivity was between 0.06 and 0.96, specificity was between 0.61 and 0.98, accuracy was between 0.78 and 0.95, and area under the receiver operating characteristic curve varied between 0.50 and 0.96. Conclusion: Artificial intelligence algorithms have the ability to accurately predict postoperative complications. Nevertheless, algorithms should be properly tested and validated, both internally and externally. Furthermore, a complete database and the absence of unsampled imbalanced data are absolute prerequisites for algorithms to predict accurately. (C) 2021 The Author(s). Published by Elsevier Inc.

Automated summary

Artificial intelligence algorithms have the ability to accurately predict postoperative complications. However, proper testing and validation are necessary. Additionally, a complete database and the absence of unsampled imbalanced data are prerequisites for accurate prediction by algorithms.