Deep learning predicts total knee replacement from magnetic resonance images

Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United States. When diagnosed at early stages, lifestyle interventions such as exercise and weight loss can slow OA progression, but at later stages, only an invasive option is available: total knee replacement (TKR). Though a generally successful procedure, only 2/3 of patients who undergo the procedure report their knees feeling ''normal'' post-operation, and complications can arise that require revision. This necessitates a model to identify a population at higher risk of TKR, particularly at less advanced stages of OA, such that appropriate treatments can be implemented that slow OA progression and delay TKR. Here, we present a deep learning pipeline that leverages MRI images and clinical and demographic information to predict TKR with AUC $0.834 \pm 0.036$ (p < 0.05). Most notably, the pipeline predicts TKR with AUC $0.943 \pm 0.057$ (p < 0.05) for patients without OA. Furthermore, we develop occlusion maps for case-control pairs in test data and compare regions used by the model in both, thereby identifying TKR imaging biomarkers. As such, this work takes strides towards a pipeline with clinical utility, and the biomarkers identified further our understanding of OA progression and eventual TKR onset.Comment: 18 pages, 5 figures (4 in main article, 1 supplemental), 8 tables (5 in main article, 3 supplemental). Submitted to Scientific Reports and currently in revisio

Age- and Gender-Related Differences in the Geometric Properties and Biomechanical Significance of Intracortical Porosity in the Distal Radius and Tibia

Cortical bone contributes the majority of overall bone mass and bears the bulk of axial loads in the peripheral skeleton. Bone metabolic disorders often are manifested by cortical microstructural changes via osteonal remodeling and endocortical trabecularization. The goal of this study was to characterize intracortical porosity in a cross-sectional patient cohort using novel quantitative computational methods applied to high-resolution peripheral quantitative computed tomography (HR-pQCT) images of the distal radius and tibia. The distal radius and tibia of 151 subjects (57 male, 94 female; 47 ± 16 years of age, range 20 to 78 years) were imaged using HR-pQCT. Intracortical porosity (Ct.Po) was calculated as the pore volume normalized by the sum of the pore and cortical bone volume. Micro–finite element analysis (µFE) was used to simulate 1% uniaxial compression for two scenarios per data set: (1) the original structure and (2) the structure with intracortical porosity artificially occluded. Differential biomechanical indices for stiffness (ΔK), modulus (ΔE), failure load (ΔF), and cortical load fraction (ΔCt.LF) were calculated as the difference between original and occluded values. Regression analysis revealed that cortical porosity, as depicted by HR-pQCT, exhibited moderate but significant age-related dependence for both male and female cohorts (radius ρ = 0.7; tibia ρ = 0.5; p < .001). In contrast, standard cortical metrics (Ct.Th, Ct.Ar, and Ct.vBMD) were more weakly correlated or not significantly correlated with age in this population. Furthermore, differential µFE analysis revealed that the biomechanical deficit (ΔK) associated with cortical porosity was significantly higher for postmenopausal women than for premenopausal women (p < .001). Finally, porosity-related measures provided the only significant decade-wise discrimination in the radius for females in their fifties versus females in their sixties (p < .01). Several important conclusions can be drawn from these results. Age-related differences in cortical porosity, as detected by HR-pQCT, are more pronounced than differences in standard cortical metrics. The biomechanical significance of these structural differences increases with age for men and women and provides discriminatory information for menopause-related bone quality effects. © 2010 American Society for Bone and Mineral Research

X-ray detection of structural orientation in human articular cartilage

AbstractObjective: To determine the feasibility of detecting the structural orientation in cartilage with Diffraction Enhanced X-Ray Imaging.Design: Human tali and femoral head specimens were Diffraction Enhanced X-Ray Imaged (DEI) at the SYRMEP beamline at Elettra at various energy levels to detect the architectural arrangement of collagen within cartilage. DEI utilizes a monochromatic and highly collimated beam, with an analyzer crystal that selectively weights out photons according to the angle they have been deviated with respect to the original direction. This provides images of very high contrast, and with the rejection of X-ray scatter.Results: DEI allowed the visualization of articular cartilage and a structural orientation, resembling arcades, within.Conclusion: Our diffraction enhanced images represent the first radiographic detection of the structural orientation in cartilage. Our data are in line with previous studies on the structural organization of joint cartilage. They confirm the model of a vaulting system of collagen fiber bundles interrupted by proteoglycan aggregates

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Similarities in network segmentations were evaluated using pairwise Dice correlations. Articular cartilage thickness was computed per-scan and longitudinally. Correlation between thickness error and segmentation metrics was measured using Pearson's coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. Results: Six teams (T1-T6) submitted entries for the challenge. No significant differences were observed across all segmentation metrics for all tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice correlations between network pairs were high (>0.85). Per-scan thickness errors were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal bias (<0.03mm). Low correlations (<0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top performing networks (p=1.0). Empirical upper bound performances were similar for both combinations (p=1.0). Conclusion: Diverse networks learned to segment the knee similarly where high segmentation accuracy did not correlate to cartilage thickness accuracy. Voting ensembles did not outperform individual networks but may help regularize individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo