Artificial intelligence in medical imaging of the liver

被引:0
作者
Li-Qiang Zhou [1 ]
Jia-Yu Wang [1 ]
Song-Yuan Yu [2 ]
Ge-Ge Wu [1 ]
Qi Wei [1 ]
You-Bin Deng [1 ]
Xing-Long Wu [3 ]
Xin-Wu Cui [1 ]
Christoph F Dietrich [1 ,4 ]
机构
[1] Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
[2] Department of Ultrasound, Tianyou Hospital Affiliated to Wuhan University of Technology
[3] School of Mathematics and Computer Science, Wuhan Textitle University
[4] Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Würzburg
关键词
Liver; Imaging; Ultrasound; Artificial intelligence; Machine learning; Deep learning;
D O I
暂无
中图分类号
TP18 [人工智能理论]; R575 [肝及胆疾病];
学科分类号
081104 ; 0812 ; 0835 ; 1002 ; 100201 ; 1405 ;
摘要
Artificial intelligence(AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians’ workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.
引用
收藏
页码:672 / 682
页数:11
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