Background: Anterior cruciate ligament (ACL) reconstruction is a widely performed procedure for ACL injury, but there are several factors which may lead to re-rupture or clinical failure. An intercondylar notch (or fossa) that is narrower may increase the likelihood of injury. Traditional two-dimensional assessments are limited, and three-dimensional (3D) volume analysis may offer more detailed insights. This study employs deep learning and statistical shape modeling (SSM) to enhance 3D modeling of the intercondylar notch, aiming to gain a deeper understanding of this complex 3D anatomical region.
Methods: A methodology was developed to generate accurate 3D models of the intercondylar fossa within seconds. The variability of the intercondylar notch in ACL-injured samples was analyzed using SSM techniques, focusing on its principal components. Additionally, gender differences in notch volume were examined using t-tests.
Results: The best deep learning method for automatic segmentation of the notch was SegResNet, which achieved a Dice similarity coefficient of over 0.88 and a Hausdorff distance of 0.73 mm. The small volume-related relative error (0.06) illustrates the goodness of the result. Three principal components accounted for 72.59% of the variation, including notch volume, shape, width, and height. Females had statistically significant smaller notch compared with males with ACL injury (P < 0.001).
Conclusion: By examining notch volume and its variability in ACL-injured patients, it is possible to understand the complex anatomy of the intercondylar notch and tailor ACL reconstructions accordingly.
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