The Use of Artificial Intelligence in the Evaluation of Knee Pathology

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The Use of Artificial Intelligence in the Evaluation of Knee Pathology

Overview This article reviews and summarizes the most current literature to study the experimental algorithms 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.

 

Authors Elisabeth R. Garwood, Ryan Tai, Ganesh Joshi, George J. Watts V.

 

Journal Seminars in Musculoskeletal Radiology Vol. 24 No. 1/2020
Recommendation/Comment This article is of interest to sonographers expert in ultrasound MSK
Clinical implication Only institutions/entities with the resources to build and manage these data sets will be able to achieve substantial forward progress unless there is a push to make data sets publicly available or multi- institutional collaboration is encouraged.
Link (DOI) https://doi.org/10.1055/s-0039-3400264
Ultrasound speciality Musculoskeletal sonography – orthopaedics and traumatology

 

Abstract: 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.

Keywords: artificial intelligence, magnetic resonance, imaging, deep learning, knee.

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