| Title: | An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection |
| Authors: | Kokabu, Terufumi Browse this author |
| Kanai, Satoshi Browse this author |
| Kawakami, Noriaki Browse this author |
| Uno, Koki Browse this author |
| Kotani, Toshiaki Browse this author |
| Suzuki, Teppei Browse this author |
| Tachi, Hiroyuki Browse this author |
| Abe, Yuichiro Browse this author |
| Iwasaki, Norimasa Browse this author |
| Sudo, Hideki Browse this author →KAKEN DB |
| Keywords: | 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 |
| Issue Date: | Jun-2021 |
| Publisher: | Elsevier |
| Journal Title: | The spine journal |
| Volume: | 21 |
| Issue: | 6 |
| Start Page: | 980 |
| End Page: | 987 |
| Publisher DOI: | 10.1016/j.spinee.2021.01.022 |
| Abstract: | 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. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/82228 |
| Appears in Collections: | 医学院・医学研究院 (Graduate School of Medicine / Faculty of Medicine) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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