Predictive Refined Computational Modeling of ACL Tear Injury Patterns.

Area of Science:

• Biomechanics
• Orthopedic Surgery
• Computational Modeling

Background:

• Anterior cruciate ligament (ACL) ruptures are common knee injuries with significant healthcare costs.
• Current manual assessments may miss subtle partial ACL tears, leading to progression and surgical needs.

Purpose of the Study:

• To develop and utilize a computational finite element model (FEM) to investigate the biomechanical effects of partial ACL tears.
• To predict the relationship between partial ACL tear size, location, and anterior tibial translation (ATT) under Lachman test conditions.

Main Methods:

• A hyperelastic composite finite element model of the knee joint was created.
• Five distinct partial ACL tear types (I-V) were simulated, varying in location and size (25%, 55%, 85%).
• The model analyzed anterior tibial translation (ATT) under simulated Lachman test (GNRB® system) boundary conditions.

Main Results:

• Different partial ACL tear types and sizes produced varying degrees of ATT, some undetectable manually.
• Small tears (25%) most impacted stability when located centrally; moderate tears (55%) affected stability most in the proximal half.
• Severe tears (85%) showed increased instability from distal to proximal, particularly at insertion sites.

Conclusions:

• The FEM accurately characterizes partial ACL tears, showing distinct ATT patterns based on tear characteristics.
• This model can improve early diagnosis of partial ACL tears, potentially preventing complete ruptures.
• Findings may guide the development of targeted repair techniques for partial ACL injuries.