Algorithm-Based Surgical Decision-Making for Focal Knee Cartilage Defects: A Pragmatic Clinical Framework with Illustrative Cases

Purpose To evaluate the early clinical and radiological outcomes of an algorithm-based surgical decision-making framework for focal knee cartilage defects using reproducible, cost-effective techniques in routine clinical practice. Materials and Methods This retrospective case series included five consecutive patients with ICRS Grade 3–4 chondral or osteochondral lesions. Treatment followed a structured decision-making algorithm based on fragment viability, lesion size, and patient-specific factors. Surgical procedures included bone marrow stimulation (BMS, n=2), osteochondral autograft transplantation (OAT, n=1), and osteochondral fragment refixation for osteochondritis dissecans (OCD, n=2). Clinical outcomes were assessed using the Visual Analogue Scale (VAS) and the Knee Injury and Osteoarthritis Outcome Score (KOOS) at 1, 3, and 6 months. Structural repair was evaluated at 6 months using MRI-based MOCART 2.0 scoring. Results All patients demonstrated progressive clinical improvement. Mean VAS scores decreased from 6.6 preoperatively to 1.2 at 6 months. KOOS scores improved across all subscales, with the greatest gains observed in activities of daily living. MRI evaluation demonstrated variable structural maturation (mean MOCART 2.0 score: 60, range 45–75), with superior outcomes observed in patients undergoing fragment refixation. Even in an older patient with extensive pathology, bone marrow stimulation resulted in satisfactory short-term outcomes. Conclusion The proposed algorithm-based approach provides a practical and reproducible framework for selecting surgical treatment in focal knee cartilage defects. Despite the limited sample size, the consistent clinical improvement supports the feasibility and applicability of this decision-making model, particularly in real-world and resource-constrained settings.