Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review.
Diagnostics (Basel)
; 13(10)2023 May 19.
Article
de En
| MEDLINE
| ID: mdl-37238283
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.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Type d'étude:
Prognostic_studies
/
Systematic_reviews
Langue:
En
Journal:
Diagnostics (Basel)
Année:
2023
Type de document:
Article
Pays d'affiliation:
France