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©The Author(s) 2022.
World J Gastroenterol. Jun 14, 2022; 28(22): 2494-2508
Published online Jun 14, 2022. doi: 10.3748/wjg.v28.i22.2494
Published online Jun 14, 2022. doi: 10.3748/wjg.v28.i22.2494
Figure 5 Receiver operating characteristic analysis on histopathology blinded test group.
A and B: Receiver operating characteristic curves of the deep learning model for diagnosing hepatic steatosis grades on histopathology blinded test group (HP-T) when using all scanners and only the Siemens/Toshiba/Philips premium scanners, respectively; C and D: Only select for histopathology blinded test group studies with FibroScan diagnoses, corresponding to the performance of the deep learning algorithm and FibroScan, respectively. All receiver operating characteristic curves were measured against a histopathological gold standard. AUCROC: Area under the curve of the receiver operating characteristic.
- Citation: Li B, Tai DI, Yan K, Chen YC, Chen CJ, Huang SF, Hsu TH, Yu WT, Xiao J, Le L, Harrison AP. Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images via scalable deep learning. World J Gastroenterol 2022; 28(22): 2494-2508
- URL: https://www.wjgnet.com/1007-9327/full/v28/i22/2494.htm
- DOI: https://dx.doi.org/10.3748/wjg.v28.i22.2494