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Войти в исследования Diagnostics (Basel, Switzerland) · 2023
Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review.
СтатьяОбзорНазвание и аннотация: Перевод в очереди
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Allaume Pierre, Rabilloud Noémie, Turlin Bruno, Bardou-Jacquet Edouard, Loréal Olivier, Calderaro Julien, Khene Zine-Eddine, Acosta Oscar, De Crevoisier Renaud, Rioux-Leclercq Nathalie, Pecot Thierry, Kammerer-Jacquet Solène-Florence
Аннотация
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.
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.