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European Radiology

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23.04.2020 | Imaging Informatics and Artificial Intelligence

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

verfasst von: Qin Ye, Qiang Shen, Wei Yang, Shuai Huang, Zhiqiang Jiang, Linyang He, Xiangyang Gong

Erschienen in: European Radiology | Ausgabe 9/2020

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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.

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Titel: Development of automatic measurement for patellar height based on deep learning and knee radiographs
verfasst von: Qin Ye
Qiang Shen
Wei Yang
Shuai Huang
Zhiqiang Jiang
Linyang He
Xiangyang Gong
Publikationsdatum: 23.04.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 9/2020
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-06856-z

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