Recent OAI Publications

Objective: This cross-sectional study aimed to examine the association between recreational volleyball participation and knee osteoarthritis (KOA) risk in a community-based population, focusing on participation frequency, cumulative exposure periods, and clinical outcomes. Methods: Utilizing data from the Osteoarthritis Initiative (OAI) cohort (n = 2539; ages 45-79), volleyball engagement was assessed via the Historical Physical Activity Survey Instrument across four age periods (12-18, 19-34,...

CONCLUSION: Self-reported knee pain during stair ambulation was significantly associated with ipsilateral strength deficits and muscle strength asymmetry. These findings suggest that asymmetrical self-reported knee pain may be related to underlying strength imbalances in individuals with knee osteoarthritis and warrant further investigation regarding its potential clinical relevance.

CONCLUSION: This study highlights the potential of deep learning models, which integrate clinical and radiological data, to predict the need for knee arthroplasty. The robust performance and generalizability of the developed model could streamline clinical pathways and predict local demand for surgery during the next three years. This will facilitate resource planning for providers and accurate and timely access to surgical interventions for patients.

CONCLUSIONS: Longitudinal change in ESV was associated with concurrent imaging-based and symptomatic osteoarthritis progression over 48 months. Compared with changes in MOAKS scores, ΔESV showed stronger associations with imaging-based OA outcomes. These findings underscore the potential of MRI-based ΔESV as an osteoarthritis imaging biomarker, while further studies are needed to establish its predictive and clinical utility.

CONCLUSION: The unadjusted and age-adjusted z-score for disease activity developed using a synthetic reference population improves both the statistical and clinical usefulness of the disease activity metric. This new composite metric improves comparability across studies, clinical interpretability, and statistical modeling utility.

CONCLUSION: Inconsistent knee positioning affects JSW measurement. Poor but consistent positioning has a minimal effect on fJSW responsiveness but a larger impact on mJSW.

CONCLUSION: Pain types in OA were associated with MRI structural morphotypes. The probability of the hypertrophic morphotype can be used to distinguish between IP and CP subgroups. A predictive model that incorporates the bone morphotype, inflammatory morphotype, and hypertrophic morphotype can effectively predict the presence or absence of pain.

Accelerating musculoskeletal magnetic resonance imaging (MRI) while preserving diagnostic detail remains challenging because acquiring fully‑isotropic ground‑truth volumes is clinically costly. In routine practice, anisotropic scans with reduced through-plane resolution degrade multiplanar visualization and slice-by-slice review in reformatted planes, obscure subtle abnormalities spanning only a few slices, and limit automated three-dimensional (3D) analyses that assume comparable spatial...

CONCLUSION: Explainable machine learning using quantitative MRI enable interpretable prediction of KOA progression. This is the first study in the FNIH/OAI cohort to integrate longitudinal quantitative MRI features with model-agnostic explanations across multiple classifiers.

CONCLUSIONS: These cross-sectional findings suggest that higher levels of MVPA are associated with more favorable metabolic profiles among individuals with or at risk for knee osteoarthritis. Key Points • Replacing 10 min/day of stationary time with moderate-to-vigorous physical activity was associated with lower odds of type 2 diabetes, and reductions in BMI and abdominal circumference in individuals with or at risk for knee osteoarthritis • Substituting light physical activity for stationary...

Background/Objectives: Accurate evaluation of knee osteoarthritis (KOA) severity is critical for optimal patient care, yet manual radiographic grading remains subject to observer variability. This study aims to evaluate the performance of a fine-tuned contrastive language-image pre-training (CLIP) framework designed to assist clinicians in grading KOA severity in plain radiographs using the Kellgren-Lawrence (KL) classification system (Grades 0-4). Methods: The model operates by projecting...

CONCLUSIONS: A leakage-controlled multi-task radiograph model achieved strong KL grading agreement and moderate discrimination of MRI-defined cartilage and meniscal pathology in knees with early radiographic osteoarthritis, supporting a potential role as an assistive triage signal to identify patients who may benefit from earlier MRI evaluation. However, clinical correlation remains essential; MRI referral decisions should always be made in the context of the individual patient's symptoms,...

CONCLUSIONS: A DL-based model for automated quantification of knee ESV on MRI was developed and validated. While our results demonstrate the potential of ESV as a scalable imaging biomarker in osteoarthritis research, validation in independent cohorts is necessary to confirm its utility.

Objective : To assess early and longitudinal post-traumatic osteoarthritis (POTA) changes in the femoral bone and cartilage following anterior cruciate ligament reconstruction (ACLR). Methods : ACLR and contralateral knees of 17 subjects (11M, 38±11years), and 17 matched healthy controls underwent 3T-MRI at 3-weeks, 3, 9, 18, and 30 months post-ACLR. Two neural shape models (NSMs): B-NSM (bone-only) and BC-NSM (bone+cartilage), trained on the Osteoarthritis Initiative (OAI), encoded femoral bone...

OBJECTIVE: The accurate segmentation of bone and cartilage is important for knee joint assessment. However, the complex shapes of both tissues make segmentation challenging. In this study, a conditional generative adversarial network incorporating dual attention mechanisms is proposed to enhance the segmentation accuracy of bone and cartilage.