Artificial Intelligence Has Varied Diagnostic and Predictive Performance in Diagnosing Patellofemoral Osteoarthritis, Trochlear Dysplasia, and Patellofemoral Tracking Abnormalities: A Systematic Review
Purpose To systematically review and evaluate the diagnostic efficacy and predictive power of artificial intelligence (AI) models in detecting patellofemoral (PF) compartment pathology and to compare their performance against ground-truth human clinical experts when applicable. Methods In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, the PubMed, Ovid/MEDLINE, and Cochrane Library databases were searched from inception through May 2024 for studies on AI methods for diagnosing trochlear dysplasia, PF osteoarthritis, or PF instability and tracking abnormalities on cross-sectional imaging. AI model choice, knee pathology, input/output data, performance metrics (accuracy, area under the curve [AUC], precision-recall curve average precision, sensitivity, specificity, positive predictive value, and negative predictive value), sample sizes of datasets, image modalities, and limitations were recorded. Results Of 68 studies screened, 17 met the inclusion criteria. Ten studies investigated AI diagnostics for PF osteoarthritis; four, PF tracking and/or instability; and three, trochlear dysplasia. Various deep learning architectures and machine learning algorithms were used. Input data included computed tomography scans, magnetic resonance imaging scans, and radiographs. Output data included anatomic landmark identification and diagnostic predictions. AUC values ranged from 0.664 to 0.990, and accuracy ranged from 74% to 99%. Model performance was moderate to excellent, with AI models consistently surpassing traditional methods in processing times. Common limitations included small sample size, single-center datasets, limited generalizability, and bias due to imbalanced datasets. Conclusions AI models showed variable diagnostic performance in identifying PF pathologies and predicting disease progression, with reported AUCs ranging from 0.664 to 0.990 and accuracies between 74% and 99%. Although some studies suggested that AI outperformed traditional diagnostic methods such as interpretation by musculoskeletal radiologists, manual segmentation, or arthroscopy, the degree of superiority was inconsistent and influenced by significant heterogeneity in model architectures, imaging modalities, and reference standards. Given the broad scope of this review and variability across studies, caution is warranted in interpreting these findings, and specific clinical recommendations cannot be made at this time. Clinical Relevance AI-based diagnostic tools show promise in supporting the evaluation of PF joint pathologies by potentially improving efficiency and consistency in image interpretation. However, because of the heterogeneity in current models and study designs, the clinical applicability of these tools remains limited. Further refinement and external validation of AI algorithms are needed before their integration into routine clinical decision making can be fully endorsed.
