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Shapey et al. Art Int Surg 2023;3:1-13 Artificial
DOI: 10.20517/ais.2022.31
Intelligence Surgery

Review Open Access

Machine learning for prediction of postoperative
complications after hepato-biliary and pancreatic

surgery

1,2
Iestyn M. Shapey , Mustafa Sultan 3
1
Department of Pancreatic Surgery, St James’s University Hospital, Leeds LS9 7TF, UK.
2
Faculty of Medicine and Health, University of Leeds, Leeds LS9 7TF, UK.
3
Manchester University NHS Foundation Trust, Manchester M13 9PT, UK.
Correspondence to: Dr. Iestyn M. Shapey, Department of Pancreatic Surgery, St James’s University Hospital, Beckett Street,
Leeds LS9 7TF, UK. E-mail: iestyn.shapey@nhs.net
How to cite this article: Shapey IM, Sultan M. Machine learning for prediction of postoperative complications after hepato-biliary
and pancreatic surgery. Art Int Surg 2023;3:1-13. https://dx.doi.org/10.20517/ais.2022.31
Received: 30 Sep 2022 First Decision: 6 Dec 2022 Revised: 16 Dec 2022 Accepted: 5 Jan 2023 Published: 31 Jan 2023

Academic Editors: Henry A. Pitt, Takeaki Ishizawa Copy Editor: Ke-Cui Yang Production Editor: Ke-Cui Yang

Abstract
Decision making in Hepatobiliary and Pancreatic Surgery is challenging, not least because of the significant
complications that may occur following surgery and the complexity of interventions to treat them. Machine
Learning (ML) relates to the use of computer derived algorithms and systems to enhance knowledge in order to
facilitate decision making and could be of great benefit to surgical patients. ML could be employed pre- or peri-
operatively to shape treatment choices prospectively, or could be utilised in the post-hoc analysis of complications
in order to inform future practice. ML could reduce errors by drawing attention to known risks of complications
through supervised learning, and gain greater insights by identifying previously under-appreciated aspects of care
through unsupervised learning. Accuracy, validity and integrity of data are of fundamental importance if predictive
models generated by ML are to be successfully integrated into surgical practice. The choice of appropriate ML
models and the interface between ML, traditional statistical methodologies and human expertise will also impact
the potential to incorporate data science techniques into daily clinical practice.

Keywords: Machine Learning, artificial intelligence, hepatic surgery, pancreatic surgery

© The Author(s) 2023. 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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