TY - JOUR
T1 - Deep learning for computed tomography assessment of hepatic fibrosis and cirrhosis
T2 - a systematic review
AU - Kutaiba, Numan
AU - Dahan, Ariel
AU - Goodwin, Mark
AU - Testro, Adam
AU - Egan, Gary
AU - Lim, Ruth
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/12
Y1 - 2023/12
N2 - Studies were identified using deep learning artificial intelligence methods for the analysis of computed tomography images in the assessment of hepatic fibrosis and cirrhosis. A systematic review was conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies protocol to evaluate the accuracy of deep learning algorithms for this objective (PROSPERO CRD 42023366201). A literature search was conducted on Medline, Embase, Web of Science, and IEEE Xplore databases. The search was conducted with a timeline from January 1, 2000,through November 13, 2022. Our search resulted in 3877 studies for screening, which yielded 6 studies meeting our inclusion criteria. All studies were retrospective. Three studies performed internal validation, and 2 studies performed external validation. Four studies used image classification algorithms, whereas 2 studies used image segmentation algorithms to derive volumetric measurements of the liver and spleen. Accuracy of the algorithms was variable in diagnosing significant and advanced fibrosis and cirrhosis, with the area under the curve ranging from 0.63 to 0.97. Deep learning algorithms using computed tomography images have the potential to classify fibrosis stages. Heterogeneity in cohorts and methodologies limits the generalizability of these studies.
AB - Studies were identified using deep learning artificial intelligence methods for the analysis of computed tomography images in the assessment of hepatic fibrosis and cirrhosis. A systematic review was conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies protocol to evaluate the accuracy of deep learning algorithms for this objective (PROSPERO CRD 42023366201). A literature search was conducted on Medline, Embase, Web of Science, and IEEE Xplore databases. The search was conducted with a timeline from January 1, 2000,through November 13, 2022. Our search resulted in 3877 studies for screening, which yielded 6 studies meeting our inclusion criteria. All studies were retrospective. Three studies performed internal validation, and 2 studies performed external validation. Four studies used image classification algorithms, whereas 2 studies used image segmentation algorithms to derive volumetric measurements of the liver and spleen. Accuracy of the algorithms was variable in diagnosing significant and advanced fibrosis and cirrhosis, with the area under the curve ranging from 0.63 to 0.97. Deep learning algorithms using computed tomography images have the potential to classify fibrosis stages. Heterogeneity in cohorts and methodologies limits the generalizability of these studies.
UR - http://www.scopus.com/inward/record.url?scp=85205900824&partnerID=8YFLogxK
U2 - 10.1016/j.mcpdig.2023.08.008
DO - 10.1016/j.mcpdig.2023.08.008
M3 - Review Article
AN - SCOPUS:85205900824
SN - 2949-7612
VL - 1
SP - 574
EP - 585
JO - Mayo Clinic Proceedings: Digital Health
JF - Mayo Clinic Proceedings: Digital Health
IS - 4
ER -