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

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Automatic identification of liver CT contrast-enhanced phases based on residual network

VernacularTitle:基于残差网络自动识别肝脏增强CT期相的研究
Author: Qianhe LIU ^{1
,

2} ; Jiahui JIANG ; Hui XU ; Kewei WU ; Yan ZHANG ; Nan SUN ; Jiawen LUO ; Te BA ; Aiqing LÜ ; Chuan'e LIU ; Yiyu YIN ; Zhenghan YANG
Author Information

1. 陕西中医药大学医学技术学院,陕西咸阳 712046
2. 首都医科大学附属北京友谊医院放射科,北京 100050
Keywords: liver; deep learning; computed tomography; quality control
From: Journal of Practical Radiology 2024;40(4):572-576
CountryChina
Language:Chinese
Abstract: 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.