Predictions of postoperative and perioperative complications of laparoscopic cholecystectomy using machine learning algorithms: systematic review
Bibliographic record
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".