Epidemiology of Post-traumatic Osteoarthritis of the Lower Extremity: Premature Aging of Youthful Joints

Arant, Kaetlyn R.; Katz, Jeffrey N.

doi:10.1007/978-3-030-79485-9_4

Cited by 1 publication

(1 citation statement)

References 62 publications

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“…Post‐traumatic osteoarthritis (PTOA) or osteoarthritis developed after injury, accounts for 12% of the nearly 600 million osteoarthritis cases worldwide 1,2 . The mean age of patients with PTOA is 15 to 20 years younger than patients with osteoarthritis from other etiologies, and early intervention is critical to prevent young patients from developing “old knees.” 3 Patients after anterior cruciate ligament injury have a high risk of developing PTOA despite anterior cruciate ligament reconstruction (ACLR) 4,5 . Thigh muscle (e.g., quadriceps and hamstrings) weakness, atrophy, and impaired neuromuscular function following anterior cruciate ligament injury and ACLR have been well documented and are suggested as predisposing factors for PTOA development after ACLR 6–8 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration

Gaj

Eck

Xie

et al. 2023

Magnetic Resonance in Med

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Purpose Fast and accurate thigh muscle segmentation from MRI is important for quantitative assessment of thigh muscle morphology and composition. A novel deep learning (DL) based thigh muscle and surrounding tissues segmentation model was developed for fully automatic and reproducible cross‐sectional area (CSA) and fat fraction (FF) quantification and tested in patients at 10 years after anterior cruciate ligament reconstructions. Methods A DL model combining UNet and DenseNet was trained and tested using manually segmented thighs from 16 patients (32 legs). Segmentation accuracy was evaluated using Dice similarity coefficients (DSC) and average symmetric surface distance (ASSD). A UNet model was trained for comparison. These segmentations were used to obtain CSA and FF quantification. Reproducibility of CSA and FF quantification was tested with scan and rescan of six healthy subjects. Results The proposed UNet and DenseNet had high agreement with manual segmentation (DSC >0.97, ASSD < 0.24) and improved performance compared with UNet. For hamstrings of the operated knee, the automated pipeline had largest absolute difference of 6.01% for CSA and 0.47% for FF as compared to manual segmentation. In reproducibility analysis, the average difference (absolute) in CSA quantification between scan and rescan was better for the automatic method as compared with manual segmentation (2.27% vs. 3.34%), whereas the average difference (absolute) in FF quantification were similar. Conclusions The proposed method exhibits excellent accuracy and reproducibility in CSA and FF quantification compared with manual segmentation and can be used in large‐scale patient studies.

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