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.

The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review / Grignaffini, Flavia; Barbuto, Francesco; Troiano, Maurizio; Piazzo, Lorenzo; Simeoni, Patrizio; Mangini, Fabio; De Stefanis, Cristiano; ONETTI MUDA, Andrea; Frezza, Fabrizio; Alisi, Anna. - In: DIAGNOSTICS. - ISSN 2075-4418. - 14:4(2024), pp. 1-26. [10.3390/diagnostics14040388]

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

Flavia Grignaffini;Maurizio Troiano;Lorenzo Piazzo;Patrizio Simeoni;Fabio Mangini;Andrea Onetti Muda;Fabrizio Frezza;Anna Alisi
2024

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.
2024
liver; biopsy; histology; histological images; computer-aided diagnostics; artificial intelligence; machine learning; deep learning; convolutional neural networks
01 Pubblicazione su rivista::01a Articolo in rivista
The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review / Grignaffini, Flavia; Barbuto, Francesco; Troiano, Maurizio; Piazzo, Lorenzo; Simeoni, Patrizio; Mangini, Fabio; De Stefanis, Cristiano; ONETTI MUDA, Andrea; Frezza, Fabrizio; Alisi, Anna. - In: DIAGNOSTICS. - ISSN 2075-4418. - 14:4(2024), pp. 1-26. [10.3390/diagnostics14040388]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1702615
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