BIPON framework shows effectiveness on benchmark imaging assessment tasks in public knee MRI benchmarks

IntroductionInjury assessment and forensic decision support are pivotal challenges in sports medicine, requiring advanced methods to interpret complex biomechanical and medical imaging evidence under uncertainty. This study presents the Biomechanical Informed Predictive Optimization Network (BIPON), a machine learning framework designed to support evidence based injury and abnormality assessment, with a general structure that accommodates multimodal data sources, including visual, temporal, and auxiliary information.MethodsThe framework comprises three conceptual components: the Biomechanical Data Integration Module (BDIM), the Injury Risk Prediction Module (IRPM), and the Performance Optimization Module (POM). In this manuscript, BIPON is instantiated and empirically evaluated in an imaging based setting, focusing on exam level injury and abnormality assessment using public knee MRI benchmarks. The proposed model employs hierarchical feature fusion and adaptive biomechanical feature weighting to improve discrimination, calibration, and robustness of imaging based predictions, which are critical for forensic documentation and clinical decision support. While BIPON is formulated to support multimodal injury risk modeling and biomechanically constrained performance optimization, these components are included as formally specified extensions of the framework and are not claimed as empirically validated in the present study due to data availability constraints.Results and discussionExperimental results demonstrate the effectiveness of the proposed approach on benchmark based imaging assessment tasks, and the optimization module is described as a reproducible constrained formulation intended for future validation when datasets with controllable action variables and measurable performance outcomes become available. In a forensic context, injury risk assessment primarily concerns evidence based evaluation of injury presence, severity, and uncertainty at the time of examination, rather than prospective outcome forecasting.