Open AccessMachine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review
Author(s) -
Bektaş Mustafa,
Tuynman Jurriaan B.,
Costa Pereira Jaime,
Burchell George L.,
Peet Donald L.
Publication year - 2022
Publication title -
world journal of surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.115
H-Index - 148
eISSN - 1432-2323
pISSN - 0364-2313
DOI - 10.1007/s00268-022-06728-1
Subject(s) - medicine , machine learning , colorectal surgery , artificial intelligence , medline , vascular surgery , abdominal surgery , algorithm , surgery , medical physics , computer science , cardiac surgery , political science , law
Background Machine 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. Methods Databases 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. Results A 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. Conclusions Machine 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.