1.Automatic identification of liver CT contrast-enhanced phases based on residual network
Qianhe LIU ; Jiahui JIANG ; Hui XU ; Kewei WU ; Yan ZHANG ; Nan SUN ; Jiawen LUO ; Te BA ; Aiqing LÜ ; Chuan'e LIU ; Yiyu YIN ; Zhenghan YANG
Journal of Practical Radiology 2024;40(4):572-576
Objective To develop and validate a deep learning model for automatic identification of liver CT contrast-enhanced phases.Methods A total of 766 patients with liver CT contrast-enhanced images were retrospectively collected.A three-phase classification model and an arterial phase(AP)classification model were developed,so as to automatically identify liver CT contrast-enhanced phases as early arterial phase(EAP)or late arterial phase(LAP),portal venous phase(PVP),and equilibrium phase(EP).In addition,221 patients with liver CT contrast-enhanced images in 5 different hospitals were used for external validation.The annotation results of radiologists were used as a reference standard to evaluate the model performances.Results In the external validation datasets,the accuracy in identifying each enhanced phase reached to 90.50%-99.70%.Conclusion The automatic identification model of liver CT contrast-enhanced phases based on residual network may provide an efficient,objective,and unified image quality control tool.
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