Titre
Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review.
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Martinino, A.
Auteure/Auteur
Aloulou, M.
Auteure/Auteur
Chatterjee, S.
Auteure/Auteur
Scarano Pereira, J.P.
Auteure/Auteur
Singhal, S.
Auteure/Auteur
Patel, T.
Auteure/Auteur
Kirchgesner, T.P.
Auteure/Auteur
Agnes, S.
Auteure/Auteur
Annunziata, S.
Auteure/Auteur
Treglia, G.
Auteure/Auteur
Giovinazzo, F.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
2077-0383
Statut éditorial
Publié
Date de publication
2022-10-28
Volume
11
Numéro
21
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Journal Article ; Review
Publication Status: epublish
Publication Status: epublish
Résumé
Hepatocellular carcinoma ranks fifth amongst the most common malignancies and is the third most common cause of cancer-related death globally. Artificial Intelligence is a rapidly growing field of interest. Following the PRISMA reporting guidelines, we conducted a systematic review to retrieve articles reporting the application of AI in HCC detection and characterization. A total of 27 articles were included and analyzed with our composite score for the evaluation of the quality of the publications. The contingency table reported a statistically significant constant improvement over the years of the total quality score (p = 0.004). Different AI methods have been adopted in the included articles correlated with 19 articles studying CT (41.30%), 20 studying US (43.47%), and 7 studying MRI (15.21%). No article has discussed the use of artificial intelligence in PET and X-ray technology. Our systematic approach has shown that previous works in HCC detection and characterization have assessed the comparability of conventional interpretation with machine learning using US, CT, and MRI. The distribution of the imaging techniques in our analysis reflects the usefulness and evolution of medical imaging for the diagnosis of HCC. Moreover, our results highlight an imminent need for data sharing in collaborative data repositories to minimize unnecessary repetition and wastage of resources.
PID Serval
serval:BIB_AA761346068F
PMID
Open Access
Oui
Date de création
2023-04-03T07:42:50.378Z
Date de création dans IRIS
2025-05-20T22:46:02Z
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Nom
jcm-11-06368.pdf
Version du manuscrit
published
Licence
https://creativecommons.org/licenses/by/4.0
Taille
962.74 KB
Format
Adobe PDF
PID Serval
serval:BIB_AA761346068F.P001
URN
urn:nbn:ch:serval-BIB_AA761346068F0
Somme de contrôle
(MD5):26130d40d6782a9cf09317808f0f9f59