Predictions of postoperative and perioperative complications of laparoscopic cholecystectomy using machine learning algorithms: systematic review

Laparoscopic cholecystectomy (LC) is a widely performed procedure with potential postoperative and perioperative complications. Recent advances in machine learning (ML) can lead to early prediction of these complications, but no systematic review has synthesized this data. This review aims to assess ML algorithms’ accuracy in predicting these complications following LC. A systematic review was conducted by PRISMA guidelines. A comprehensive search was performed on PubMed, Embase, Scopus, and Web of Science databases for studies published between 2010 and 2024. Studies that applied ML algorithms to predict complications during and after LC were included. Quality assessment was performed using the Newcastle-Ottawa Scale (NOS). Due to study heterogeneity, a meta-analysis was not conducted; instead, a narrative synthesis was performed. A total of 6 studies were included in the review. Various machine learning algorithms, such as decision trees, deep learning, artificial neural networks (ANN), and adaptive boosting, were assessed for predicting postoperative and perioperative complications after laparoscopic cholecystectomy (LC). ANN models showed superior performance, with mean absolute percentage error (MAPE) values ranging from 4.20 to 8.60% in predicting quality of life post-LC. Deep learning models achieved a balanced accuracy of 71.4% for critical view of safety (CVS) assessment during LC. Adaboost algorithms effectively identified key risk factors for hepatic fibrosis in post-cholecystectomy patients. However, models predicting surgical adverse events faced limitations due to low prevalence, resulting in lower predictive values. ML models show great potential in predicting postoperative complications following LC while also considering intraoperative and perioperative outcomes that impact patient safety and postoperative recovery, but limitations such as small sample sizes and limited applicability remain. Further research is needed to validate these models in larger, more diverse populations.