Quantifying Patellofemoral Instability in a Sea of Metrics

Abstract

Patellofemoral instability is a spectrum of disorders in which the patella loses normal tracking within the trochlear groove, presenting as subluxation or dislocation. Accurate quantification is essential for guiding treatment, yet no unified clinical and imaging scoring system exists. Patient-reported outcome measures remain inconsistent, with frequent reliance on general knee scores rather than tools specifically designed for patellofemoral instability. Imaging assessment requires multiple parameters, including patellar height, tibial tuberosity-trochlear groove distance, and trochlear morphology, but these are limited by static measurement and interobserver variability. This review examines current challenges in the diagnosis and management of patellofemoral instability and also highlights how artificial intelligence and machine learning models may support clinicians by improving diagnostic accuracy, risk stratification, and personalized treatment strategies.