Qin Ye1, Qiang Shen1, Wei Yang1, Shuai Huang1, Zhiqiang Jiang2, Linyang He2, Xiangyang Gong3,4. 1. Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China. 2. Hangzhou Jianpei Technology Co., Ltd, Hangzhou, China. 3. Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China. cjr.gxy@hotmail.com. 4. Institute of Artificial Intelligence and Remote Imaging, Hangzhou Medical College, Hangzhou, China. cjr.gxy@hotmail.com.
Abstract
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. KEY POINTS: • Objective and reliable measurement of patellar height parameters is important for clinical diagnosis and the development of a treatment strategy. • Deep learning can be used to create an automatic patellar height measurement system based on knee radiographs. • The deep learning-based patellar height measurement system achieves comparable performance to radiologists in measuring ISI, CDI, and KI.
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. KEY POINTS: • Objective and reliable measurement of patellar height parameters is important for clinical diagnosis and the development of a treatment strategy. • Deep learning can be used to create an automatic patellar height measurement system based on knee radiographs. • The deep learning-based patellar height measurement system achieves comparable performance to radiologists in measuring ISI, CDI, and KI.