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Page 133 Bektaş et al. Art Int Surg 2022;2:132-43 https://dx.doi.org/10.20517/ais.2022.20

Conclusion: Machine learning models have shown promising accuracies in the prediction of short-term and long-
term surgical outcomes after hepatopancreaticobiliary surgery. External validation of Machine Learning models is
required to facilitate the clinical introduction of Machine Learning.

Keywords: Artificial intelligence, machine learning, hepatectomy, cholecystectomy, pancreatectomy

INTRODUCTION
Artificial intelligence (AI) has made major progress in healthcare recently, causing an increase in interest in
AI algorithms within clinical settings. This may signify the start of a revolutionized digital era within the
field of medicine .
[1]

Artificial intelligence has been defined as the ability of machines to demonstrate human behavior and
intelligence . As a major division of AI, Machine Learning (ML) models are able to improve by learning
[2]
from large-scale data . Decision Tree, Gradient Boosting (GBM), Random Forest, and Support Vector
[3]
Machine algorithms (SVM) are frequently applied models of ML. A specific branch of ML is known as Deep
Learning (DL), which includes multiple layers to recognize several features and patterns from large data .
[4]
In each layer, values are added to all extracted features. In the end, a model with the best prediction of
outcomes is achieved based on training and validation. The accuracy is described as the predictive
evaluation of these models on new unseen data. Neural Networks form the basis of DL models and are able
to recognize data patterns by using processing layers. Deep Learning functions similarly; however, these
models have more layers or depth than Neural Networks. Another group of AI includes Radiomics, which is
able to examine various medical images for the purpose of detecting features that are associated with the
[5]
disease or prognosis . An overview of AI terminology is shown in Table 1.

In clinical practice, ML has already been used for several purposes, such as diagnosis, treatment decisions,
[15]
and monitoring of patients . These purposes are especially used in medical specialties that use imaging,
such as radiology and pathology. Machine Learning has also been applied in general surgery to improve
surgical skill training and predict postoperative outcomes . In hepatopancreatobiliary (HPB) surgery,
[16]
several clinical challenges are still present, as high frequencies of postoperative complications, such as organ
failures, infections, and gastrointestinal tract bleedings, have been reported by surgeons . Additionally, the
[17]
overall prognosis is poor for malignancies in the hepatobiliary tract and pancreas. For patients with
hepatobiliary carcinomas, 5-year survival rates of up to 20% have been reported without upfront surgery,
whereas survival rates of 45% have been described with upfront surgery . For borderline-resectable
[18]
pancreatic carcinomas, 5-year survival rates were discovered to be close to 6% with upfront surgery,
although survival rates of 20,5% have been found for patients that have received neoadjuvant
[19]
chemoradiotherapy (nCRT) . To overcome these clinical challenges, ML models could preoperatively
predict disease progression, postoperative complications, and prognosis of patients undergoing HPB
surgery. Predicting postoperative complications with ML could provide the opportunity to take
prophylactic measures. Furthermore, surgeons could decide between upfront surgery or nCRT based on the
predicted response of tumors to nCRT.

Although ML algorithms have shown major potential in HPB surgery, the current status and progress of
ML within HPB surgery have not been systematically evaluated in recent literature. However, it is essential
to bridge this gap in order to understand the predictive capabilities of ML in HPB surgery properly.
Therefore, this systematic review aims to provide a comprehensive overview of ML applications within HPB
surgery.