Journal ArticleDOI
Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry
Andrew M Schmidt,Arjun D. Desai,Lauren E. Watkins,Hollis A. Crowder,Marianne S. Black,Valentina Mazzoli,Elka B Rubin,Quin Lu,James W. MacKay,Robert D. Boutin,Feliks Kogan,Garry E. Gold,Brian A. Hargreaves,Akshay S. Chaudhari +13 more
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TLDR
TheqDESS-trained model may generalize well to independent qDESS datasets regardless of MR scanner, acquisition parameters, and subject population, and the generalizability of DL methods to new datasets without fine-tuning is evaluated.Abstract:
Deep learning (DL)‐based automatic segmentation models can expedite manual segmentation yet require resource‐intensive fine‐tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine‐tuning is not well characterized.read more
Citations
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Agreement and accuracy of fully automated morphometric femorotibial cartilage analysis in radiographic knee osteoarthritis
TL;DR: In this paper , the authors examined the performance of automated convolutional neuronal network cartilage segmentation in knees with radiographic osteoarthritis (ROA), and its dependence on the OA status of the training set and MRI sequences.
Journal ArticleDOI
Editorial for “Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and Relaxometry”
TL;DR: In this article , the authors evaluated the generalizability of deep learning-based automatic cartilage segmentation models on four different datasets and found that they performed better than the OAI DESS trained model.
References
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Journal ArticleDOI
Osteoarthritis: A Disease of the Joint as an Organ
TL;DR: This research presents a novel and scalable approach to personalized medicine that addresses the underlying cause of inflammation in patients withumatoid arthritis and shows real-world implications for treatment and prognosis.
Journal ArticleDOI
Evolution of semi-quantitative whole joint assessment of knee OA: MOAKS (MRI Osteoarthritis Knee Score)
David J. Hunter,Ali Guermazi,Grace H. Lo,Grace H. Lo,Andrew J. Grainger,Philip G. Conaghan,Robert M. Boudreau,Frank W. Roemer,Frank W. Roemer +8 more
TL;DR: The iteratively developed magnetic resonance imaging Osteoarthritis Knee Score shows very good to excellent reliability for the large majority of features assessed and further iterative development and research will include assessment of its validation and responsiveness.
Journal ArticleDOI
Epidemiology of osteoarthritis: literature update.
Ernest R. Vina,C. Kent Kwoh +1 more
TL;DR: This review highlights recent studies of osteoarthritis epidemiology, including research on prevalence, disease impact, and potential risk factors, which may allow us to develop innovative strategies and novel therapies for preventing new disease onset and minimizing disease progression.
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Human articular cartilage: influence of aging and early symptomatic degeneration on the spatial variation of T2--preliminary findings at 3 T.
TL;DR: Preliminary results indicate this diffuse increase in T2 in senescent cartilage is different in appearance than the focally increased T2 observed in damaged articular cartilage.
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Spatial variation of T2 in human articular cartilage
TL;DR: There is a reproducible pattern of increasing T2 that is proportional to the known spatial variation in cartilage water and is inversely proportionalto the distribution of proteoglycans, and the authors postulate that these regional T2 differences are secondary to the restricted mobility of Cartilage water within an anisotropic solid matrix.