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The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review

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Specialty Radiology
Date 2024 Feb 24
PMID 38396427
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Abstract

Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.

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References
1.
Shi J, Li Y, Zhu J, Sun H, Cai Y . Joint sparse coding based spatial pyramid matching for classification of color medical image. Comput Med Imaging Graph. 2014; 41:61-6. DOI: 10.1016/j.compmedimag.2014.06.002. View

2.
Jain D, Torres R, Celli R, Koelmel J, Charkoftaki G, Vasiliou V . Evolution of the liver biopsy and its future. Transl Gastroenterol Hepatol. 2021; 6:20. PMC: 7829074. DOI: 10.21037/tgh.2020.04.01. View

3.
. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013; 45(6):580-5. PMC: 4010069. DOI: 10.1038/ng.2653. View

4.
Yarbakht M, Pradhan P, Kose-Vogel N, Bae H, Stengel S, Meyer T . Nonlinear Multimodal Imaging Characteristics of Early Septic Liver Injury in a Mouse Model of Peritonitis. Anal Chem. 2019; 91(17):11116-11121. DOI: 10.1021/acs.analchem.9b01746. View

5.
Hwang J, Lim M, Han G, Park H, Kim Y, Park J . Preparing pathological data to develop an artificial intelligence model in the nonclinical study. Sci Rep. 2023; 13(1):3896. PMC: 9994413. DOI: 10.1038/s41598-023-30944-x. View