Editorial Commentary: Traveling to A New Dimension in Anterior Cruciate Ligament Injury Risk Stratification: Holistic 3-Dimensional Knee Phenotyping via Automated Artificial Intelligence Pipelines.

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Abstract

Morphologic risk stratification for anterior cruciate ligament injury has historically relied upon isolated two-dimensional radiographic parameters (e.g., posterior tibial slope, notch width, lateral femoral condyle ratio, etc.). Although informative, these measures are limited by inter observer measurement variability, workflow burden, inconsistently reported threshold values, and their use in isolation. Emerging artificial intelligence-enabled pipelines paired with three-dimensional modeling may allow automated, scalable quantification of curvature, inclinations, and joint congruity across multiple intra-articular structures from routine magnetic resonance imaging. By considering the interactions among numerous anatomic features, rather than single-slice surrogates, such approaches may improve risk characterization and ultimately support screening and targeted deployment of preventive interventions. However, important challenges remain, including distinguishing pre-injury morphologic phenotypes from post-injury sequelae when imaging is obtained after injury, ensuring robust model development and reporting in small datasets and showing external validity and value. Prospective validation and thoughtful integration of anatomic, clinical, and exposure-related variables will be essential to translate automated three-dimensional morphologic profiling from retrospective association to clinically actionable predictions.

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