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Bektaş et al. Art Int Surg 2022;2:132-43 Artificial
DOI: 10.20517/ais.2022.20
Intelligence Surgery

Systematic Review Open Access

Artificial intelligence in hepatopancreaticobiliary
surgery: a systematic review

2,3
1
4
Mustafa Bektaş 1 , Babs M. Zonderhuis , Henk A. Marquering , Jaime Costa Pereira , George L.
5
Burchell , Donald L. van der Peet 1
1
Department of Surgery, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, the Netherlands.
2
Department of Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam 1105 AZ, the
Netherlands.
3
Department of Biomedical Engineering and Physics, Amsterdam UMC location University of Amsterdam, Amsterdam 1105 AZ,
the Netherlands.
4
Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, the Netherlands.
5
Medical Library, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, the Netherlands.
Correspondence to: Dr. Mustafa Bektaş, Department of Surgery, Amsterdam UMC location Vrije Universiteit Amsterdam, De
Boelelaan 1117, Amsterdam 1081 HV, the Netherlands. E-mail: m.bektas@amsterdamumc.nl
How to cite this article: Bektaş M, Zonderhuis BM, Marquering HA, Costa Pereira J, Burchell GL, van der Peet DL. Artificial
intelligence in hepatopancreaticobiliary surgery: a systematic review. Art Int Surg 2022;2:132-43.
https://dx.doi.org/10.20517/ais.2022.20
Received: 16 Jul 2022 First Decision: 18 Aug 2022 Revised: 28 Aug 2022 Accepted: 9 Sep 2022 Published: 19 Sep 2022
Academic Editors: Andrew A. Gumbs, Xin Wang Copy Editor: Peng-Juan Wen Production Editor: Peng-Juan Wen

Abstract
Aim: The aim of this systematic review was to provide an overview of Machine Learning applications within
hepatopancreaticobiliary surgery. The secondary aim was to evaluate the predictive performances of applied
Machine Learning models.

Methods: A systematic search was conducted in PubMed, EMBASE, Cochrane, and Web of Science. Studies were
only eligible for inclusion when they described Machine Learning in hepatopancreaticobiliary surgery. The
Cochrane and PROBAST risk of bias tools were used to evaluate the quality of studies and included Machine
Learning models.

Results: Out of 1821 articles, 52 studies have met the inclusion criteria. The majority of Machine Learning models
were developed to predict the course of disease, and postoperative complications. The course of disease has been
predicted with accuracies up to 99%, and postoperative complications with accuracies up to 89%. Most studies
had a retrospective study design, in which external validation was absent for Machine Learning models.

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

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