Abstract
There is a critical unmet need in the clinical implementation of valid preventative
and therapeutic strategies for patients with articular cartilage pathology based on
the significant gap in understanding of the relationships between diagnostic data,
disease progression, patient-related variables, and symptoms. In this article, the
current state of classification and categorization for articular cartilage pathology
is discussed with particular focus on machine learning methods and the authors propose
a bedside–bench–bedside approach with highly quantitative techniques as a solution
to these hurdles. Leveraging computational learning with available data toward articular
cartilage pathology patient phenotyping holds promise for clinical research and will
likely be an important tool to identify translational solutions into evidence-based
clinical applications to benefit patients. Recommendations for successful implementation
of these approaches include using standardized definitions of articular cartilage,
to include characterization of depth, size, location, and number; using measurements
that minimize subjectivity or validated patient-reported outcome measures; considering
not just the articular cartilage pathology but the whole joint, and the patient perception
and perspective. Application of this approach through a multistep process by a multidisciplinary
team of clinicians and scientists holds promise for validating disease mechanism-based
phenotypes toward clinically relevant understanding of articular cartilage pathology
for evidence-based application to orthopaedic practice.
Keywords
machine learning - articular cartilage - knee - artificial intelligence
