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bioRxiv · 10.1101/849943

Ultrasound Echogenicity is Associated with Achilles Tendon Fatigue Damage in a Cadaveric Loading Model

Abstract

Achilles tendon disorders are among the most difficult sports-related injuries to predict with current diagnostic tools. The purpose of this study was to identify a clinically useful marker for early tendon damage. We hypothesized that alterations in mean echogenicity are linked with changes in vitro tendon mechanics. To test our hypothesis, we harvested Achilles tendons from 10 fresh-frozen cadaveric feet and cyclically fatigued them using a universal test frame while we continuously acquired ultrasound images. Throughout this fatigue protocol, we applied 2 stress tests every 500 loading cycles to quantify changes in ultrasound imaging echogenicity. We continued this fatigue protocol until each tendon either failed completely or survived 150,000 cycles. Tendons that failed during the fatigue loading (6/10) underwent greater changes in mean echogenicity compared to tendons that did not fail (P = 0.031). These tendons that failed during fatigue loading demonstrated greater changes in mean echogenicity that surpassed 1.0%; whereas survivor tendons exhibited less than 0.5% changes in mean echogenicity. We found that changes in mean echogenicity measured with ultrasound increased proportionally with increased tendon damage. The magnitude of these changes was relatively small (<1.5% change in mean echogenicity) but may be an effective predictor of tendon failure. Mean echogenicity is a promising marker for quantifying fatigue damage in cadaveric Achilles tendons during a stress test. Although these changes cannot be detected with the naked eye, computer-based predictive models may effectively assess risk of tendon damage in physically active adults. Level of evidenceControlled laboratory experiment

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Schmidt, E. C., Hullfish, T. J., O'Connor, K. M., Hast, M. W., Baxter, J. R.. 2019-11-21. Ultrasound Echogenicity is Associated with Achilles Tendon Fatigue Damage in a Cadaveric Loading Model. https://doi.org/10.1101/849943

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