A Study on the Morphometric Dimensions of the Intercondylar Notch of the Femur Using UTHSCA Image Tool Software and Its Clinical Significance
Year: 2025
Type: article
Abstract: Introduction: Anterior cruciate ligament (ACL) injuries are common among athletes, with intercondylar notch (ICN) anatomy playing a significant role in influencing risk. Understanding notch morphology aids orthopedic surgeons in optimizing ACL reconstruction techniques and developing effective injury prevention strategies. This study investigates ICN symmetry and morphology to evaluate anatomical risk factors relevant to ACL injuries and optimize surgical reconstruction strategies. Materials and Methods: A total of 134 ethically sourced dried femurs were analyzed, excluding specimens with deformities, abnormalities, or from infants. Standardized digital photographs were taken using a Sony DCR-W270 camera at a fixed distance of 10 cm. Two views were captured: (1) a frontal image to classify notch shape as inverted U or V, and (2) a lateral image to measure the notch roof angle. Additional parameters assessed included ICN width, notch depth, bicondylar width, and notch width index. All measurements were done using UTHSCA Image Tool Software. Results: The study provides baseline anatomical data useful for comparing with imaging findings (computed tomography/magnetic resonance imaging) in ACL-injured knees. Observations suggest that stenotic notches may correlate with increased susceptibility to ACL tears. These insights assist orthopedic surgeons in preoperative planning, graft selection, and refinement of surgical techniques, aiming to improve reconstruction outcomes and reduce injury recurrence. Conclusion: By elucidating the relationship between ICN morphology and ACL injury risk, this study provides actionable evidence to support injury prevention strategies and surgical precision in athletes.
Institution: Tamil Nadu Dr. M.G.R. Medical University
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7117697302