SynthesisWorld journal of surgery2022
Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review.
Synthesis in World journal of surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluating predictive performance, validity, and applicability of machine learning models for predicting HIV treatment interruption: a systematic review.BMC global and public health · 2025Pooled it
- Pooled it
- Beyond robotic platforms: artificial intelligence and the emergence of intelligent surgical ecosystems in colorectal surgery.Journal of robotic surgery · 2026Review
- A Predictive Model for Major Adverse Aortic Events in Patients with Abdominal Aortic Aneurysm Using Clinical and Biomarker Data.International journal of molecular sciences · 2026Article
- Trefoil Factor 3 as a Biomarker for Peripheral Artery Disease.Biomolecules · 2026Article
- Artificial intelligence in anal fistula: mapping evidence to IDEAL stages.Annals of coloproctology · 2026Review
- Comparison of Fatty Acid Binding Protein 3 and Ankle Brachial Index for Predicting Peripheral Artery Disease Outcomes.Biomolecules · 2026Article
- Machine Learning-Based Predictive Model for Grade 3 Primary Graft Dysfunction Following Lung Transplantation: A Retrospective Cohort Study.International journal of general medicine · 2026Article
- Exploring Machine Learning Approaches for Decision Support in Neoadjuvant Therapy of Locally Advanced Rectal Cancer.Oncology research · 2026Article
- Predicting 1-year successful clinical use of an arteriovenous access for hemodialysis using machine learning.NPJ digital medicine · 2025Article
- Development of a Clinical Prediction Model for Anastomotic Leakage in Colorectal Surgery.JAMA network open · 2025Observational
- Predictive model for early postoperative stomal complications in colorectal cancer: A systematic review.World journal of gastrointestinal oncology · 2025Article
- Investigation of Growth Differentiation Factor 15 as a Prognostic Biomarker for Major Adverse Limb Events in Peripheral Artery Disease.Journal of clinical medicine · 2025Article
- Predicting outcomes following endovascular aortoiliac revascularization using machine learning.NPJ digital medicine · 2025Article
- Growth Differentiation Factor 15 Predicts Cardiovascular Events in Peripheral Artery Disease.Biomolecules · 2025Article
- Machine Learning to Predict Early Death Despite Pancreaticoduodenectomy.The Journal of surgical research · 2025Article
- Personalized Medical Approach in Gastrointestinal Surgical Oncology: Current Trends and Future Perspectives.Journal of personalized medicine · 2025Review
- Predicting outcomes following open abdominal aortic aneurysm repair using machine learning.Scientific reports · 2025Article
- Article
- Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair.Journal of the American Heart Association · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning (ML) has been introduced in various fields of healthcare. In colorectal surgery, the role of ML has yet to be reported. In this systematic review, an overview of machine learning models predicting surgical outcomes after colorectal surgery is provided.
methodsDatabases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using machine learning models for patients undergoing colorectal surgery. To be eligible for inclusion, studies needed to apply machine learning models for patients undergoing colorectal surgery. Absence of machine learning or colorectal surgery or studies reporting on reviews, children, study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of machine learning models.
resultsA total of 1821 studies were analysed, resulting in the inclusion of 31 articles. A vast proportion of ML algorithms have been used to predict the course of disease and response to neoadjuvant chemoradiotherapy. Radiomics have been applied most frequently, along with predictive accuracies up to 91%. However, most studies included a retrospective study design without external validation or calibration.
conclusionsMachine learning models have shown promising potential in predicting surgical outcomes after colorectal surgery. However, large-scale data is warranted to bridge the gap between calibration and external validation. Clinical implementation is needed to demonstrate the contribution of ML within daily practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.