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Deep learning applications in osteoarthritis imaging

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Abstract

Deep learning (DL) is one of the most exciting new areas in medical imaging. This article will provide a review of current applications of DL in osteoarthritis (OA) imaging, including methods used for cartilage lesion detection, OA diagnosis, cartilage segmentation, and OA risk assessment. DL techniques have been shown to have similar diagnostic performance as human readers for detecting and grading cartilage lesions within the knee on MRI. A variety of DL methods have been developed for detecting and grading the severity of knee OA and various features of knee OA on X-rays using standardized classification systems with diagnostic performance similar to human readers. Multiple DL approaches have been described for fully automated segmentation of cartilage and other knee tissues and have achieved higher segmentation accuracy than currently used methods with substantial reductions in segmentation times. Various DL models analyzing baseline X-rays and MRI have been developed for OA risk assessment. These models have shown high diagnostic performance for predicting a wide variety of OA outcomes, including the incidence and progression of radiographic knee OA, the presence and progression of knee pain, and future total knee replacement. The preliminary results of DL applications in OA imaging have been encouraging. However, many DL techniques require further technical refinement to maximize diagnostic performance. Furthermore, the generalizability of DL approaches needs to be further investigated in prospective studies using large image datasets acquired at different institutions with different imaging hardware before they can be implemented in clinical practice and research studies.

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  1. Department of Radiology, New York University Grossman School of Medicine, 660 First Avenue, 3Rd Floor, New York, NY, 10016, USA

    Richard Kijowski, Jan Fritz & Cem M. Deniz

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Key points

• Deep learning techniques have similar diagnostic performance as human readers for detecting and grading cartilage lesions within the knee on MRI.

• Deep learning methods can detect and grade the severity of knee osteoarthritis and features of knee osteoarthritis on X-rays with similar diagnostic performance as human readers.

•Deep learning approaches for full-automated segmentation of cartilage and other knee tissue can achieve higher segmentation accuracy than currently used methods with substantial reductions in segmentation times.

•Deep learning models have high diagnostic performance for predicting osteoarthritis outcomes, including the incidence and progression of radiographic knee osteoarthritis, the presence and progression of knee pain, and future total knee replacement.

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Kijowski, R., Fritz, J. & Deniz, C.M. Deep learning applications in osteoarthritis imaging. Skeletal Radiol 52, 2225–2238 (2023). https://doi.org/10.1007/s00256-023-04296-6

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