A Decision Tree Model for Chondral Lesion Management in the Knee Based on Clinical and Mathematical Integration

Background:Focal chondral defects of the knee present a complex therapeutic challenge due to the heterogeneous nature of lesion characteristics and patient-specific factors.Despite multiple available treatment modalities, no universally accepted decision-making framework currently exists to guide individualized care.This study proposes and validates a clinically grounded, mathematically structured decision tree algorithm to stratify treatment options based on key anatomical, biomechanical, and demographic parameters. Methods:A comprehensive literature review was performed to define evidence-based thresholds for commonly used surgical techniques, including microfracture, autologous chondrocyte implantation (ACI), matrix-assisted autologous chondrocyte implantation (MACI), osteochondral autograft transplantation (OATS), osteochondral allograft transplantation (OCA), high tibial osteotomy (HTO), unicompartmental knee arthroplasty (UKA), and focal prosthetic resurfacing.Clinical variables were selected based on their consistent identification as prognostic or treatment-modifying factors in cartilage repair literature.Age, lesion size, and activity level are repeatedly associated with differential outcomes following marrow stimulation or cell-based therapies.Mechanical alignment influences joint load distribution and has been strongly linked to cartilage repair success.Lesion location, number of lesions, and associated meniscal or ligamentous pathology were included because they modify surgical feasibility and biomechanical environment.These variables were prioritized because they are consistently reported across clinical studies and consensus recommendations, although other potentially relevant factors-such as symptom duration, lesion morphology, prior cartilage procedures, or previous surgical failure-were not explicitly incorporated as independent nodes in the present model, although they may significantly influence treatment selection in clinical practice.The algorithm was tested against a cohort of 100 representative clinical cases, constructed to reflect real-world distributions of lesion size, patient age, alignment, activity level, and associated pathology.Internal consistency, decision path coherence, and robustness were assessed via deterministic modeling, univariate perturbation, and Monte Carlo simulation. Results:The decision tree demonstrated full internal convergence, with 100% of cases yielding consensus-concordant therapeutic recommendations.The distribution of assigned treatments aligned with lesion severity, age strata, and functional status.Perturbation analysis confirmed decision stability in 73% of cases, with clinically plausible reclassification in the remaining 27%.Monte Carlo simulations (n=1,000) further validated model robustness under probabilistic input variation, with entropy analysis supporting appropriate decision diversity. J o u r n a l P r e -p r o o f Conclusion:This decision tree model offers a reproducible, evidence-based framework for the management of focal chondral knee lesions.It enables consistent, transparent therapeutic stratification and may serve as a foundation for future clinical decisionsupport tools, prospective validation studies, and digital integration into surgical planning workflows.