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

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.

journal_name

Eur Radiol

journal_title

European radiology

authors

Ye Q,Shen Q,Yang W,Huang S,Jiang Z,He L,Gong X

doi

10.1007/s00330-020-06856-z

subject

Has Abstract

pub_date

2020-09-01 00:00:00

pages

4974-4984

issue

eissn

0938-7994

issn

1432-1084

pii

10.1007/s00330-020-06856-z

journal_volume

pub_type

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