Andreassen, Thor E

Abstract Background/Purpose Diagnosing wrist ligament injuries is challenging; early detection and treatment are important to prevent osteoarthritis progression. Interosseous proximity maps, a proxy measure for joint space, can be generated from volumetric imaging data and may provide important information about wrist health. Artificial intelligence (AI) could enhance accuracy of noninvasive diagnosis based on imaging-derived metrics. This work demonstrates feasibility of AI training using synthetic proximity map data generated from finite element models (FEMs). Methods Personalized wrist FEMs for two asymptomatic participants were created from four-dimensional computed tomography-derived anatomic and kinematic data. Monte Carlo sampling varied 22 ligament material properties and simulated 7,500 unique injury scenarios generating 9,000,000 labeled red, green, and blue (RGB) images of interosseous proximity vector fields from FEM-derived motions. Images were associated with 17 descriptive metrics, including gross wrist angles and bone surface pairs, and used to develop mixed-input convolutional neural networks (CNNs). Model performance was evaluated for identifying specific ligament injuries. Results Average area under receiver operating characteristic curve (AUROC) for CNNs was 0.757 across all injury types and kinematics. In a subset with clinically-relevant functional angles, the average AUROC was 0.824. Best-performing individual ligament AUROCs ranged from 0.807 to 0.999. Sensitivities and specificities exceeded 0.99 for some ligament injury simulations under specific wrist angles and bone surface pairs. Conclusion This study demonstrates the feasibility of using synthetic data from FEMs to train AI models for classifying wrist ligament injuries. Proximity-based RGB images may be a relevant biomarker of ligamentous injury.

Abstract Background Computational modeling is a tool being deployed for orthopaedic solutions but its use in the hand and wrist remains limited. This work used a model to simulate a clinically relevant provocative scaphoid shift maneuver (SSM) with different levels of scapholunate interosseous ligament (SLIL) injuries to observe the effect on different metrics. Methods A personalized model simulated the full SSM motion cycle from ulnar deviation with extension to radial deviation with flexion informed by the participant’s motion obtained from dynamic computed tomography. Models repeated the SSM under different levels of SLIL injury and reported changes in joint kinematics, contact mechanics, and ligament forces. Results The fully injured model increased scaphoid dorsal translation, flexion, and radial deviation compared to the intact condition and caused a subluxation of the scaphoid. Radioscaphoid contact areas were approximately 200% greater in the fully injured model compared with all others and the fully injured model was the only condition where contact force decreased across the motion cycle. Ligament forces in the intact condition were on average 33.0 N and 54.2 N for the volar and dorsal SLIL, respectively. Lastly, the long radiolunate, an extrinsic stabilizer, had forces that increased following SLIL injury. Conclusions Computational models can successfully recreate clinically observed behaviors of an SSM, including scaphoid subluxation, while providing new insights via quantification of contact mechanics and ligament forces. Contact mechanics metrics may be important for understanding the long-term progression of untreated SLIL injuries to osteoarthritis. Additionally, ligament force metrics may explain the progression of SLIL injuries from volar SLIL to dorsal SLIL and highlight the importance of repairing extrinsic stabilizers of the joint, due to increased force sharing following SLIL injury. This work provides a pathway to future studies investigating the effects of SLIL injury and repair, both acutely and chronically.

Abstract Several complications are associated with total shoulder arthroplasty (TSA), including glenoid loosening and joint instability. These complications are linked to imbalances in glenohumeral muscle forces, which cause eccentric loading that occurs on the outer edge of the glenoid component, rather than in the center of the glenoid, and can be damaging to the implant and destabilizing the joint. The purpose of this study was to develop musculoskeletal models of TSA patients using their skeletal geometry acquired from CT and their kinematics acquired with high-speed stereo radiography (HSSR) to predict glenohumeral joint loading and muscle forces for abduction and flexion and describe how these loads differ from their native contralateral side. CT data was collected for both shoulders of 6 TSA patients (three anatomic and three reverse). Models were developed using CT reconstructions of each patient’s scapula and humerus, and placed implant geometry with CT-derived alignment. Mesh morphing was used to create personalized muscle attachment sites on the scapula. Simulation of abduction and flexion to quantify muscle and joint forces was driven with limb-specific HSSR kinematics. At 90 deg abduction, reverse TSA increased superior shear force by 11.2% bodyweight (BW) and decreased medial compressive force by 27.5%BW compared to each patient’s contralateral shoulder. Lateral deltoid elevation moment arms and stability ratios increased 9.61 mm and 0.58, respectively. At 90 deg abduction, anatomic TSA patients increased humeral internal rotation by 52.7 deg and decreased rotator cuff force by 9.4%BW. Patient-specific musculoskeletal models quantified differences in glenohumeral joint mechanics. This study revealed muscle and kinematic mechanisms that could lead to edge loading and glenoid loosening in TSA and can inform personalized surgical planning for implant alignment with the goal of improving patient outcomes.