| | Publication reference | Year | Scope of review | Modality |
| | Pedoia and Majumdar [24] | 2018 | Advances in assessment (image processing and deep learning techniques), quantitative imaging, multidimensional data analysis of knee and hip OA | MRI, X-ray (plain radiography) | | Hayashiet al. [10] | 2019 | MRI techniques on knee OA assessment: development of new concept and techniques, hybrid imaging, artificial intelligence application | MRI | | Chaudhari et al. [25] | 2019 | Existing development in OA diagnosis using magnetic resonance images: Morphological imaging, compositional imaging, rapid biomarker extraction, hardware improvements | MRI | | Garwood et al. [13] | 2020 | AI application on knee pathologies: cartilages (osteoarthritis), ligaments, meniscus, tendons, musculoskeletal ultrasound, bone tumors, fractures | MRI, X-ray (plain radiography) | | Kaur et al. [41] | 2020 | Knee cartilage and bone segmentation approaches: thresholding-based, partial differential equation-based, graph-based, atlas-based, model-based, machine learning-based (includes deep learning) | MRI, CT | | Gan et al. [8] | 2020 | Knee bone and cartilage segmentation approaches: region-based; deformable model-based, atlas-based, graph-based, classical machine learning-based, deep learning-based | MRI | | Evaluation of computational models, brief discussion of classification models | | Ebrahimkhani et al. [28] | 2020 | Knee articular cartilage segmentation approaches: conventional methods, active contour models, active shape and active appearance models, graph-based, atlas-based, learning-based (5 publications on deep learning) | MRI | | Eckstein et al. [42] | 2021 | Imaging studies on OA research between January 2019 and April 2020: models of early knee OA, structure modification in established OA, deep learning approaches in image analysis | MRI, X-ray (plain radiography) | | Kijowski et al. [43] | 2019 | Imaging studies on OA research between April 1, 2018, and March 30, 2019: risk factors of OA, OA disease evaluation or treatment response, technical advances, and deep learning in OA imaging | MRI, ultrasound, X-ray (plain radiography), CT, positron emission tomography (PET), dual energy X-ray absorptiometry (DXA) | | Nieminen et al. [44] | 2018 | Imaging studies on OA research between 1 April 2017 and 31 March 2018: cross-sectional studies, prediction, prognostic and progression studies of different modalities and deep learning | MRI, radiography, CT, ultrasound, nuclear medicine | | Saini et al. [45] | 2021 | Knee OA severity classification: distinct feature quantification-based, and composite grading-based | X-ray (plain radiography) | | Knee segmentation approaches: manual, semiautomatic, automatic methods |
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