Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms

Swiecicki, Albert; Said, Nicholas; O’Donnell, J; Buda, Mateusz; Li, Nianyi; Jiranek, William A.; Mazurowski, Maciej A.

doi:10.1117/12.2551377

Cited by 2 publications

(1 citation statement)

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“…Presymptomatic knee OA diagnosis also remains a big challenge as the radiographic pattern at early OA is insignificant and unnoticeable. Researchers have previously suggested a few advanced quantitative examination approaches, work by extracting diagnostic meaningful structural details [24], such as joint space width [25,26], cartilage thickness [27,28], meniscal thickness [29], and tibiofemoral angle [30], from various images. Although the proposed methods have demonstrated the quantification of joint structures correlated with osteoarthritic joint, the diagnostic precision is not validated.…”

Section: Potentials and Limitationsmentioning

confidence: 99%

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Teoh

Lai

Usman

et al. 2022

Journal of Healthcare Engineering

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Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren–Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.

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