Development of automatic measurement for patellar height based on deep learning and knee radiographs

Objectives To develop and evaluate the performance of a deep learning-based system for automatic patellar height measurements using knee radiographs. Methods The deep learning-based algorithm was developed with a data set consisting of 1018 left knee radiographs for the prediction of patellar height parameters, specifically the Insall-Salvati index (ISI), Caton-Deschamps index (CDI), modified Caton-Deschamps index (MCDI), and Keerati index (KI). The performance and generalizability of the algorithm were tested with 200 left knee and 200 right knee radiographs, respectively. The intra-class correlation coefficient (ICC), Pearson correlation coefficient, mean absolute difference (MAD), root mean square (RMS), and Bland-Altman plots for predictions by the system were evaluated in comparison with manual measurements as the reference standard. Results Compared with the reference standard, the deep learning-based algorithm showed high accuracy in predicting the ISI, CDI, and KI (left knee ICC = 0.91-0.95, r = 0.84-0.91, MAD = 0.02-0.05, RMS = 0.02-0.07; right knee ICC = 0.87-0.96, r = 0.78-0.92, MAD = 0.02-0.06, RMS = 0.02-0.10), but not the MCDI (left knee ICC = 0.65, r = 0.50, MAD = 0.14, RMS = 0.18; right knee ICC = 0.62, r = 0.47, MAD = 0.15, RMS = 0.20). The performance of the algorithm met or exceeded that of manual determination of ISI, CDI, and KI by radiologists. Conclusions In its current state, the developed system can predict the ISI, CDI, and KI for both left and right knee radiographs as accurately as radiologists. Training the system further with more data would increase its utility in helping radiologists measure patellar height in clinical practice.