Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review

Abstract

Background: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field.

Objective: The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields.

Results: 42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias.

Conclusions: DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.

Keywords: artificial intelligence; deep learning; digital pathology; hepatology; liver; performance metrics.

Figure 1

Example of a 2 × 2 contingency table used for calculating performance metrics such as precision, recall and fallout. Histological images are courtesy of the CHU Pontchaillou, Rennes, France.

Figure 2

Flowchart of literature search.

Figure 3

Global overview of reviewed studies, including evaluation of risk of bias through the QUADAS-2 tool for the “tumoral” (A) and “non tumoral” (B) fields.

Figure 3

Global overview of reviewed studies, including evaluation of risk of bias through the QUADAS-2 tool for the “tumoral” (A) and “non tumoral” (B) fields.