| Autor: |
Garwood, Elisabeth R., Tai, Ryan, Joshi, Ganesh, Watts V, George J. |
| Předmět: |
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| Zdroj: |
Seminars in Musculoskeletal Radiology; 2020, Vol. 24 Issue 1, p21-29, 9p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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