An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection

Kokabu, Terufumi; Kanai, Satoshi; Kawakami, Noriaki; Uno, Koki; Kotani, Toshiaki; Suzuki, Teppei; Tachi, Hiroyuki; Abe, Yuichiro; Iwasaki, Norimasa; Sudo, Hideki

An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection

Kokabu, Terufumi; Kanai, Satoshi; Kawakami, Noriaki et al.

The spine journal, 2021, 21(6), 980-987

Permalink : https://hdl.handle.net/2115/82228

このアイテムのアクセス数:512件（2026-08-26 04:23 集計）

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アクセス権	open access
URI	https://hdl.handle.net/2115/82228
タイトル	An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection
著者	Kokabu, Terufumi ; Kanai, Satoshi ; Kawakami, Noriaki ; Uno, Koki ; Kotani, Toshiaki ; Suzuki, Teppei ; Tachi, Hiroyuki ; Abe, Yuichiro ; Iwasaki, Norimasa ; Sudo, Hideki
著者名 Kokabu, Terufumi
著者名 Kanai, Satoshi
著者名 Kawakami, Noriaki
著者名 Uno, Koki
著者名 Kotani, Toshiaki
著者名 Suzuki, Teppei
著者名 Tachi, Hiroyuki
著者名 Abe, Yuichiro
著者名 Iwasaki, Norimasa
e-Rad_Researcher 30374367 著者名 Sudo, Hideki
言語	英語
キーワード	Accuracy
	Adolescent idiopathic scoliosis
	Cobb angle
	Convolutional neural network for regression
	Correlation coefficient analyses
	Deep learning algorithm
	Mean absolute error
	Noncontact and noninvasive system
	Three-dimensional depth sensor
発行日	2021-06
出版者	Elsevier
収録物名	The spine journal
巻(号)	21(6)
ページ	980-987
抄録	BACKGROUND CONTEXT: Timely intervention in growing individuals, such as brace treatment, relies on early detection of adolescent idiopathic scoliosis (AIS). To this end, several screening methods have been implemented. However, these methods have limitations in predicting the Cobb angle. PURPOSE: This study aimed to evaluate the performance of a three-dimensional depth sensor imaging system with a deep learning algorithm, in predicting the Cobb angle in AIS. STUDY DESIGN: Retrospective analysis of prospectively collected, consecutive, nonrandomized series of patients at five scoliosis centers in Japan. PATIENT SAMPLE: One hundred and-sixty human subjects suspected to have AIS were included. OUTCOME MEASURES: Patient demographics, radiographic measurements, and predicted Cobb angle derived from the deep learning algorithm were the outcome measures for this study. METHODS: One hundred and sixty data files were shuffled into five datasets with 32 data files at random (dataset 1, 2, 3, 4, and 5) and five-fold cross validation was performed. The relationships between the actual and predicted Cobb angles were calculated using Pearson's correlation coefficient analyses. The prediction performances of the network models were evaluated using mean absolute error and root mean square error between the actual and predicted Cobb angles. The shuffling into five datasets and five-fold cross validation was conducted ten times. There were no study-specific biases related to conflicts of interest. RESULTS: The correlation between the actual and the mean predicted Cobb angles was 0.91. The mean absolute error and root mean square error were 4.0 degrees and 5.4 degrees, respectively. The accuracy of the mean predicted Cobb angle was 94% for identifying a Cobb angle of >= 10 degrees and 89% for that of >= 20 degrees. CONCLUSIONS: The three-dimensional depth sensor imaging system with its newly innovated convolutional neural network for regression is objective and has significant ability to predict the Cobb angle in children and adolescents. This system is expected to be used for screening scoliosis in clinics or physical examination at schools. (C) 2021 The Authors. Published by Elsevier Inc.
資源タイプ	学術雑誌論文
出版タイプ	Version of Record
PISSN	1529-9430
関連情報 (isIdenticalTo)	出版社版DOI https://doi.org/10.1016/j.spinee.2021.01.022