Estimating time-to-total knee replacement on radiographs and MRI: a multimodal approach using self-supervised deep learning.

Purpose: Accurately predicting the expected duration of time until total knee replacement (time-to-TKR) is crucial for patient management and health care planning. Predicting when surgery may be needed, especially within shorter windows like 3 years, allows clinicians to plan timely interventions and health care systems to allocate resources more effectively. Existing models lack the precision for such time-based predictions.

Radiomics features outperform standard radiological measurements in detecting femoroacetabular impingement on three-dimensional magnetic resonance imaging.

Femoroacetabular impingement (FAI) is a cause of hip pain and can lead to hip osteoarthritis. Radiological measurements obtained from radiographs or magnetic resonance imaging (MRI) are normally used for FAI diagnosis, but they require time-consuming manual interaction, which limits accuracy and reproducibility. This study compares standard radiologic measurements against radiomics features automatically extracted from MRI for the identification of FAI patients versus healthy subjects.

Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI).

Imaging Biomarkers of Osteoarthritis.

Currently no disease-modifying osteoarthritis drug has been approved for the treatment of osteoarthritis (OA) that can reverse, hold, or slow the progression of structural damage of OA-affected joints. The reasons for failure are manifold and include the heterogeneity of structural disease of the OA joint at trial inclusion, and the sensitivity of biomarkers used to measure a potential treatment effect.This article discusses the role and potential of different imaging biomarkers in OA research.

Association of quantitative diffusion tensor imaging measures with time to return to sport and reinjury incidence following acute hamstring strain injury.

Hamstring strain injuries (HSI) are a common occurrence in athletics and complicated by limited prognostic indicators and high rates of reinjury. Assessment of injury characteristics at the time of injury (TOI) may be used to manage athlete expectations for time to return to sport (RTS) and mitigate reinjury risk. Magnetic resonance imaging (MRI) is routinely used in soft tissue injury management, but its prognostic value for HSI is widely debated.

Diffusion tensor imaging of hamstring muscles after acute strain injury and throughout recovery in collegiate athletes.

Objective: To identify the region of interest (ROI) to represent injury and observe between-limb diffusion tensor imaging (DTI) microstructural differences in muscle following hamstring strain injury.

Materials And Methods: Participants who sustained a hamstring strain injury prospectively underwent 3T-MRI of bilateral thighs using T1, T2, and diffusion-weighted imaging at time of injury (TOI), return to sport (RTS), and 12 weeks after RTS (12wks). ROIs were using the hyperintense region on a T2-weighted sequence: edema, focused edema, and primary muscle injured excluding edema (no edema).

How AI May Transform Musculoskeletal Imaging.

While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists.

Repeatability of Quantitative Knee Cartilage T, T, and T Mapping With 3D-MRI Fingerprinting.

Background: Three-dimensional MR fingerprinting (3D-MRF) techniques have been recently described for simultaneous multiparametric mapping of knee cartilage. However, investigation of repeatability remains limited.

Purpose: To assess the intra-day and inter-day repeatabilities of knee cartilage T, T, and T maps using a 3D-MRF sequence for simultaneous measurement.

Age and Gender-Dependence of Single-and Bi-Exponential T MR Parameters in Knee Ligaments.

Background: There is limited understanding of differences in the composition and structure of ligaments between healthy males and females, and individuals of different ages. Females present higher risk for ligament injuries than males and there are conflicting reports on its cause. This study looks into T parameters for an explanation as it relates to proteoglycan, collagen, and water content in these tissues.

Genicular Artery Embolization for Treatment of Knee Osteoarthritis: Interim Analysis of a Prospective Pilot Trial Including Effect on Serum Osteoarthritis-Associated Biomarkers.

Purpose: To characterize the safety, efficacy, and potential role of genicular artery embolization (GAE) as a disease-modifying treatment for symptomatic knee osteoarthritis (OA).

Materials And Methods: This is an interim analysis of a prospective, single-arm clinical trial of patients with symptomatic knee OA who failed conservative therapy for greater than 3 months. Sixteen patients who underwent GAE using 250-μm microspheres and had at least 1 month of follow-up were included.

A radiomics approach to the diagnosis of femoroacetabular impingement.

Introduction: Femoroacetabular Impingement (FAI) is a hip pathology characterized by impingement of the femoral head-neck junction against the acetabular rim, due to abnormalities in bone morphology. FAI is normally diagnosed by manual evaluation of morphologic features on magnetic resonance imaging (MRI). In this study, we assess, for the first time, the feasibility of using radiomics to detect FAI by automatically extracting quantitative features from images.

Age and gender differences in lumbar intervertebral disk strain using mechanical loading magnetic resonance imaging.

The objective of the current study was to investigate age- and gender-related differences in lumbar intervertebral disk (IVD) strain with the use of static mechanical loading and continuous three-dimensional (3D) golden-angle radial sparse parallel (GRASP) MRI. A continuous 3D-GRASP stack-of-stars trajectory of the lumbar spine was performed on a 3-T scanner with static mechanical loading. Compressed sensing reconstruction, motion deformation maps, and Lagrangian strain maps during loading and recovery in the X-, Y-, and Z-directions were calculated for segmented IVD segments from L1/L2 to L5/S1.

Prediction of total knee replacement using deep learning analysis of knee MRI.

Current methods for assessing knee osteoarthritis (OA) do not provide comprehensive information to make robust and accurate outcome predictions. Deep learning (DL) risk assessment models were developed to predict the progression of knee OA to total knee replacement (TKR) over a 108-month follow-up period using baseline knee MRI. Participants of our retrospective study consisted of 353 case-control pairs of subjects from the Osteoarthritis Initiative with and without TKR over a 108-month follow-up period matched according to age, sex, ethnicity, and body mass index.

Updates on Compositional MRI Mapping of the Cartilage: Emerging Techniques and Applications.

Osteoarthritis (OA) is a widely occurring degenerative joint disease that is severely debilitating and causes significant socioeconomic burdens to society. Magnetic resonance imaging (MRI) is the preferred imaging modality for the morphological evaluation of cartilage due to its excellent soft tissue contrast and high spatial resolution. However, its utilization typically involves subjective qualitative assessment of cartilage.

2D versus 3D MRI of osteoarthritis in clinical practice and research.

Accurately detecting and characterizing articular cartilage defects is critical in assessing patients with osteoarthritis. While radiography is the first-line imaging modality, magnetic resonance imaging (MRI) is the most accurate for the noninvasive assessment of articular cartilage. Multiple semiquantitative grading systems for cartilage lesions in MRI were developed.

Genicular artery embolization for treatment of knee osteoarthritis pain: Systematic review and meta-analysis.

Objective: Genicular artery embolization (GAE) is a novel, minimally invasive procedure for treatment of knee osteoarthritis (OA). This meta-analysis investigated the safety and effectiveness of this procedure.

Design: Outcomes of this systematic review with meta-analysis were technical success, knee pain visual analog scale (VAS; 0-100 scale), WOMAC Total Score (0-100 scale), retreatment rate, and adverse events.

Deep learning applications in osteoarthritis imaging.

Deep learning (DL) is one of the most exciting new areas in medical imaging. This article will provide a review of current applications of DL in osteoarthritis (OA) imaging, including methods used for cartilage lesion detection, OA diagnosis, cartilage segmentation, and OA risk assessment. DL techniques have been shown to have similar diagnostic performance as human readers for detecting and grading cartilage lesions within the knee on MRI.

Emerging Technology in Musculoskeletal MRI and CT.

This article provides a focused overview of emerging technology in musculoskeletal MRI and CT. These technological advances have primarily focused on decreasing examination times, obtaining higher quality images, providing more convenient and economical imaging alternatives, and improving patient safety through lower radiation doses. New MRI acceleration methods using deep learning and novel reconstruction algorithms can reduce scanning times while maintaining high image quality.