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PubMed Publications| Keyword search (4,164 papers available) |  |
"Tang A" Authored Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Simulating federated learning for steatosis detection using ultrasound images | Qi Y; Vianna P; Cadrin-Chênevert A; Blanchet K; Montagnon E; Belilovsky E; Wolf G; Mullie LA; Cloutier G; Chassé M; Tang A; | 38858500 ENCS |
| 2 | Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis | Vianna P; Calce SI; Boustros P; Larocque-Rigney C; Patry-Beaudoin L; Luo YH; Aslan E; Marinos J; Alamri TM; Vu KN; Murphy-Lavallée J; Billiard JS; Montagnon E; Li H; Kadoury S; Nguyen BN; Gauthier S; Therien B; Rish I; Belilovsky E; Wolf G; Chassé M; Cloutier G; Tang A; | 37787678 ENCS |
| Title: | Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis | ||||
| Authors: | Vianna P, Calce SI, Boustros P, Larocque-Rigney C, Patry-Beaudoin L, Luo YH, Aslan E, Marinos J, Alamri TM, Vu KN, Murphy-Lavallée J, Billiard JS, Montagnon E, Li H, Kadoury S, Nguyen BN, Gauthier S, Therien B, Rish I, Belilovsky E, Wolf G, Chassé M, Cloutier G, Tang A | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/37787678/ | ||||
| DOI: | 10.1148/radiol.230659 | ||||
| Publication: | Radiology | ||||
| Keywords: | |||||
| PMID: | 37787678 | Category: | Date Added: | 2023-10-03 | |
| Dept Affiliation: | ENCS | ||||
Description: |
Background Screening for nonalcoholic fatty liver disease (NAFLD) is suboptimal due to the subjective interpretation of US images. Purpose To evaluate the agreement and diagnostic performance of radiologists and a deep learning model in grading hepatic steatosis in NAFLD at US, with biopsy as the reference standard. Materials and Methods This retrospective study included patients with NAFLD and control patients without hepatic steatosis who underwent abdominal US and contemporaneous liver biopsy from September 2010 to October 2019. Six readers visually graded steatosis on US images twice, 2 weeks apart. Reader agreement was assessed with use of ? statistics. Three deep learning techniques applied to B-mode US images were used to classify dichotomized steatosis grades. Classification performance of human radiologists and the deep learning model for dichotomized steatosis grades (S0, S1, S2, and S3) was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. Results The study included 199 patients (mean age, 53 years ± 13 [SD]; 101 men). On the test set (n = 52), radiologists had fair interreader agreement (0.34 [95% CI: 0.31, 0.37]) for classifying steatosis grades S0 versus S1 or higher, while AUCs were between 0.49 and 0.84 for radiologists and 0.85 (95% CI: 0.83, 0.87) for the deep learning model. For S0 or S1 versus S2 or S3, radiologists had fair interreader agreement (0.30 [95% CI: 0.27, 0.33]), while AUCs were between 0.57 and 0.76 for radiologists and 0.73 (95% CI: 0.71, 0.75) for the deep learning model. For S2 or lower versus S3, radiologists had fair interreader agreement (0.37 [95% CI: 0.33, 0.40]), while AUCs were between 0.52 and 0.81 for radiologists and 0.67 (95% CI: 0.64, 0.69) for the deep learning model. Conclusion Deep learning approaches applied to B-mode US images provided comparable performance with human readers for detection and grading of hepatic steatosis. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tuthill in this issue. |
