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

Allaume, Pierre; Rabilloud, Noémie; Turlin, Bruno; Bardou-Jacquet, Édouard; Loréal, Olivier; Caldéraro, Julien; Khene, Z.; Acosta, Oscar; Crevoisier, R. de; Rioux‐Leclercq, Nathalie; Pécot, Thierry; Kammerer‐Jacquet, Solène‐Florence

doi:10.3390/diagnostics13101799

Cited by 8 publications

(5 citation statements)

References 74 publications

(143 reference statements)

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“…These reasons have led to the increased demand for analysis using CAD systems, which require some steps for the inspection of histopathological images, including the division of WSI's into patches of reduced size (with or without overlap), the subsequent steps of pre-processing, segmentation, features extraction, and application of ML or DL approaches. Of note, a recent article provided a comprehensive, systematic, and updated discussion of the literature evidence on the application of DL approaches on WSIs obtained via liver histopathology [98]. In particular, the authors evaluated the selected articles by highlighting their performance and bias through a useful tool known as the Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2).…”

Section: State-of-the-artmentioning

confidence: 99%

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

Grignaffini,

Barbuto,

Troiano

et al. 2024

Diagnostics

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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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Section: State-of-the-artmentioning

confidence: 99%

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

Grignaffini,

Barbuto,

Troiano

et al. 2024

Diagnostics

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“…Similarly in breast cancer, Krithiga et al examined studies where image analysis techniques were used to detect, segment and classify disease, with reported accuracies ranging from 77 to 98% 17 . Other reviews have examined applications in liver pathology, skin pathology and kidney pathology with evidence of high diagnostic accuracy from some AI models 18 – 20 . Additionally, Rodriguez et al performed a broader review of AI applied to WSIs and identified 26 studies for inclusion with a focus on slide level diagnosis 21 .…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy

McGenity,

Clarke,

Jennings

et al. 2024

npj Digit. Med.

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Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model, with additional subgroup analyses also performed. Of 2976 identified studies, 100 were included in the review and 48 in the meta-analysis. Studies were from a range of countries, including over 152,000 whole slide images (WSIs), representing many diseases. These studies reported a mean sensitivity of 96.3% (CI 94.1–97.7) and mean specificity of 93.3% (CI 90.5–95.4). There was heterogeneity in study design and 99% of studies identified for inclusion had at least one area at high or unclear risk of bias or applicability concerns. Details on selection of cases, division of model development and validation data and raw performance data were frequently ambiguous or missing. AI is reported as having high diagnostic accuracy in the reported areas but requires more rigorous evaluation of its performance.

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“…There are several recent reviews on the use of artificial intelligence in liver diseases [ 11 , 12 , 13 ]. It is now well-established that modern machine learning methods can perform extremely well in image classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Quantum Image Classification and Federated Learning for Hepatic Steatosis Diagnosis

Lusnig,

Sagingalieva,

Surmach

et al. 2024

Diagnostics

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In the realm of liver transplantation, accurately determining hepatic steatosis levels is crucial. Recognizing the essential need for improved diagnostic precision, particularly for optimizing diagnosis time by swiftly handling easy-to-solve cases and allowing the expert time to focus on more complex cases, this study aims to develop cutting-edge algorithms that enhance the classification of liver biopsy images. Additionally, the challenge of maintaining data privacy arises when creating automated algorithmic solutions, as sharing patient data between hospitals is restricted, further complicating the development and validation process. This research tackles diagnostic accuracy by leveraging novel techniques from the rapidly evolving field of quantum machine learning, known for their superior generalization abilities. Concurrently, it addresses privacy concerns through the implementation of privacy-conscious collaborative machine learning with federated learning. We introduce a hybrid quantum neural network model that leverages real-world clinical data to assess non-alcoholic liver steatosis accurately. This model achieves an image classification accuracy of 97%, surpassing traditional methods by 1.8%. Moreover, by employing a federated learning approach that allows data from different clients to be shared while ensuring privacy, we maintain an accuracy rate exceeding 90%. This initiative marks a significant step towards a scalable, collaborative, efficient, and dependable computational framework that aids clinical pathologists in their daily diagnostic tasks.

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

Cited by 8 publications

References 74 publications

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

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

Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy

Hybrid Quantum Image Classification and Federated Learning for Hepatic Steatosis Diagnosis

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