The Use of Artificial Intelligence in the Evaluation of Knee Pathology

被引：19
作者：
Garwood, Elisabeth R. [1 ,2 ]
Tai, Ryan [1 ,2 ]
Joshi, Ganesh [1 ,2 ]
Watts, George J. [1 ,2 ]
机构：
[1] Univ Massachusetts, Mem Med Ctr, Dept Radiol, Div Musculoskeletal Imaging & Intervent, Worcester, MA 01655 USA
[2] Univ Massachusetts, Med Sch, 55 Lake Ave North, Worcester, MA 01655 USA
来源：
SEMINARS IN MUSCULOSKELETAL RADIOLOGY | 2020年 / 24卷 / 01期
关键词：
artificial intelligence; magnetic resonance imaging; deep learning; knee; ANTERIOR CRUCIATE LIGAMENT; MENISCAL TEARS; OSTEOARTHRITIS; DIAGNOSIS; ARTHRITIS; INJURIES;
D O I：
10.1055/s-0039-3400264
中图分类号：
R8 [特种医学]; R445 [影像诊断学];
学科分类号：
1002 ; 100207 ; 1009 ;
摘要：
Artificial intelligence (AI) holds the potential to revolutionize the field of radiology by increasing the efficiency and accuracy of both interpretive and noninterpretive tasks. We have only just begun to explore AI applications in the diagnostic evaluation of knee pathology. Experimental algorithms have already been developed that can assess the severity of knee osteoarthritis from radiographs, detect and classify cartilage lesions, meniscal tears, and ligament tears on magnetic resonance imaging, provide automatic quantitative assessment of tendon healing, detect fractures on radiographs, and predict those at highest risk for recurrent bone tumors. This article reviews and summarizes the most current literature.
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收藏
页码：21 / 29
页数：9
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