Fully Automated Measurement of the Insall-Salvati Ratio with Artificial Intelligence

AbstractPatella alta (PA) and patella baja (PB) affect 1 –2% of the world population, but are often underreported, leading to potential complications like osteoarthritis. The Insall-Salvati ratio (ISR) is commonly used to diagnose patellar height abnormalities. Artificial intelligence (AI) keypoint models show promising accuracy in measuring and detecti ng these abnormalities.An AI keypoint model is developed and validated to study the Insall-Salvati ratio on a random population sample of lateral knee radiographs. A keypoint model was trained and internally validated with 689 lateral knee radiographs from five sites in a multi-hospital urban healthcare system after IRB approval. A total of 116 lateral knee radiographs from a sixth site were used for external validation. Distance error (mm), Pearson correlation, and Bland –Altman plots were used to evaluate model performance. On a random sample of 2647 different lateral knee radiographs, mean and standard deviation were used to calculate the normal distribution of ISR. A keypoint detection model had mean distance error of 2.57 ± 2.44 mm on internal validation data and 2.73 ± 2.86 mm on external validation data. Pearson correlation between labeled and predicted Insall-Salvati ratios was 0.82 [95% CI 0.76–0.86] on internal validation and 0.75 [0.66–0.82] on external validation. For the population sample of 2647 patients, there was mean ISR of 1. 11 ± 0.21. Patellar height abnormalities were underreported...
Source: Journal of Digital Imaging - Category: Radiology Source Type: research