Although the orientations of the hand and forearm vary for different wrist rehabilitation protocols, their effect on muscle forces has not been quantified. Physiologic simulators enable a biomechanical evaluation of the joint by recreating functional motions in cadaveric specimens. Control strategies used to actuate joints in physiologic simulators usually employ position or force feedback alone to achieve optimum load distribution across the muscles. After successful tests on a phantom limb, unique combinations of position and force feedback – hybrid control and cascade control – were used to simulate multiple cyclic wrist motions of flexion-extension, radioulnar deviation, dart thrower’s motion, and circumduction using six muscles in ten cadaveric specimens. Low kinematic errors and coefficients of variation of muscle forces were observed for planar and complex wrist motions using both novel control strategies. The effect of gravity was most pronounced when the hand was in the horizontal orientation, resulting in higher extensor forces (p < 0.017) and higher out-of-plane kinematic errors (p < 0.007), as compared to the vertically upward or downward orientations. Muscle forces were also affected by the direction of rotation during circumduction. The peak force of flexor carpi radialis was higher in clockwise circumduction (p = 0.017), while that of flexor carpi ulnaris was higher in anticlockwise circumduction (p = 0.013). Thus, the physiologic wrist simulator accurately replicated cyclic planar and complex motions in cadaveric specimens. Moreover, the dependence of muscle forces on the hand orientation and the direction of circumduction could be vital in the specification of such parameters during wrist rehabilitation.
Artificial intelligence (AI) in hand surgery is an emerging and evolving field that will likely play a large role in the future care of our patients. However, there remain several challenges to makes this technology meaningful, acceptable and usable at scale. In this review article, we discuss basic concepts in AI, including challenges and key considerations, provide an update on how AI is being used in hand and wrist surgery and propose potential future applications. The aims are to equip clinicians and researchers with the basic knowledge needed to understand and explore the incorporation of AI in hand surgery within their own practice and recommends further reading to develop knowledge in this emerging field.
Background: Fractures of the metacarpals and phalanges account for more than half of all upper extremity fractures sustained by competitive athletes. Purpose: To determine which management strategy is best for expediting return to preinjury levels of competition in adult athletes with metacarpal and/or phalangeal fractures. Study Design: Systematic review; Level of evidence, 4. Methods: A methodology compliant with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was used. A custom search strategy was designed and applied to MEDLINE and In-Process, Embase, EMCARE, and CINAHL. Results: Overall, 3135 records were identified, of which 8 met full inclusion criteria. All patients returned to preinjury levels of competition, at a mean of 30.6 days for phalangeal fractures and 21.9 days for metacarpal fractures. Meta-analysis demonstrated delayed return-to-sport time for operatively managed metacarpal fractures as compared with nonoperatively managed ones (28.5 vs 22.0 days). All studies were of fair or poor quality, and none were randomized. Conclusion: Optimal management strategies for athletes with metacarpal and phalangeal fractures remain equivocal. Injury, treatment, and sport-specific factors may confound results and preclude accurate estimation of optimal treatment strategies at present.
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