MATERIALS AND METHODS: Eight rabbits were randomized into Giftbox and Krackow groups. Tenotomy was performed on the Achilles tendon of one side of the lower limb and repaired with the respective techniques. The contralateral limb served as control. Subjects were euthanized six weeks post-operative, and both repaired and control Achilles tendons were harvested for biomechanical tensile test.
RESULTS: The means of maximum load to rupture and tenacity in the Giftbox group (156.89 ± 38.49 N and 159.98 ± 39.25 gf/tex) were significantly different than Krackow's (103.55 ± 27.48 N and 104.91 ± 26.96 gf/tex, both p = 0.043).
CONCLUSION: The tendons repaired with Giftbox technique were biomechanically stronger than those repaired with Krackow technique after six weeks of tendon healing.
METHODS: Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used.
FINDINGS: Most studies (83.1%) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100% in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64%), while artificial neural network demonstrated the highest accuracy in eight studies.
INTERPRETATIONS: The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain. We identify three key challenges: sensor designs, the controllability of AI models, and the integration of AI-sensor models, providing valuable insights for future research.
METHODS: Twelve fresh-frozen cadaveric knees were used. Five components of the quadriceps and the iliotibial band were loaded physiologically with 175N and 30N, respectively. The force required to displace the patella 10mm laterally and medially at 0°, 20°, 30°, 60° and 90° knee flexion was measured. Patellofemoral contact points at these knee flexion angles were marked. The trochlea cartilage geometry at these flexion angles was visualized by Computed Tomography imaging of the femora in air with no overlying tissue. The sulcus, medial and lateral facet angles were measured. The facet angles were measured relative to the posterior condylar datum.
RESULTS: The lateral facet slope decreased progressively with flexion from 23°±3° (mean±S.D.) at 0° to 17±5° at 90°. While the medial facet angle increased progressively from 8°±8° to 36°±9° between 0° and 90°. Patellar lateral stability varied from 96±22N at 0°, to 77±23N at 20°, then to 101±27N at 90° knee flexion. Medial stability varied from 74±20N at 0° to 170±21N at 90°. There were significant correlations between the sulcus angle and the medial facet angle with medial stability (r=0.78, p<0.0001).
CONCLUSIONS: These results provide objective evidence relating the changes of femoral profile geometry with knee flexion to patellofemoral stability.