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AI MSK clinical applications: cartilage and osteoarthritis

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Abstract

The advancements of artificial intelligence (AI) for osteoarthritis (OA) applications have been rapid in recent years, particularly innovations of deep learning for image classification, lesion detection, cartilage segmentation, and prediction modeling of future knee OA development. This review article focuses on AI applications in OA research, first describing machine learning (ML) techniques and workflow, followed by how these algorithms are used for OA classification tasks through imaging and non-imaging-based ML models. Deep learning applications for OA research, including analysis of both radiographs for automatic detection of OA severity, and MR images for detection of cartilage/meniscus lesions and cartilage segmentation for automatic T2 quantification will be described. In addition, information on ML models that identify individuals at high risk of OA development will be provided. The future vision of machine learning applications in imaging of OA and cartilage hinges on implementation of AI for optimizing imaging protocols, quantitative assessment of cartilage, and automated analysis of disease burden yielding a faster and more efficient workflow for a radiologist with a higher level of reproducibility and precision. It may also provide risk assessment tools for individual patients, which is an integral part of precision medicine.

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Funding

This study was funded by NIH R01-AR064771 and NIH R01-AR078917.

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Authors and Affiliations

  1. Department of Radiology and Biomedical Imaging, University of California, 185 Berry St, Suite 350, San Francisco, CA, 94158, USA

    Gabby B. Joseph, Jae Ho Sohn, Valentina Pedoia, Sharmila Majumdar & Thomas M. Link

  2. Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA

    Charles E. McCulloch

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  1. Gabby B. Joseph
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Correspondence to Gabby B. Joseph.

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Summary statement:

This review article focuses on machine learning (ML) applications for osteoarthritis (OA), with an emphasis on techniques and workflow, followed by applications of deep learning, classical ML, and ensemble ML models for OA and cartilage classification tasks.

Important concepts:

▪ The advancements of artificial intelligence for osteoarthritis applications have been rapid in recent years, particularly innovations of deep learning for image classification, lesion detection, and cartilage segmentation.

▪ Deep learning is a catalyst that can enable cartilage T2 quantification as a short add-on to a routine clinical MR protocol, complementing standard clinical sequences for morphological assessment by providing information on localized cartilage biochemical composition.

▪ The future vision of machine learning applications in clinical radiology hinges on implementation of artificial intelligence for optimizing image protocols, clinical assessment of disease burden, and automatic reporting yielding a faster and more efficient workflow for a radiologist, and a higher level of reproducibility and precision.

▪ By integrating a wide variety of data, from genetics and biochemical serum markers to imaging and clinical factors for a comprehensive assessment of future OA prediction, AI can be used to improve disease prevention as well as identify patients eligible for drug-development clinical trials.

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Joseph, G.B., McCulloch, C.E., Sohn, J.H. et al. AI MSK clinical applications: cartilage and osteoarthritis. Skeletal Radiol 51, 331–343 (2022). https://doi.org/10.1007/s00256-021-03909-2

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