Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs

Abstract Human spinal balance assessment relies considerably on sagittal radiographic parameter measurement. Deep learning could be applied for automatic landmark detection and alignment analysis, with mild to moderate standard errors and favourable correlations with manual measurement. In this stud...

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Autores principales:	Yu-Cheng Yeh, Chi-Hung Weng, Yu-Jui Huang, Chen-Ju Fu, Tsung-Ting Tsai, Chao-Yuan Yeh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/81f7b934fe4245148f554804ed105461
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spelling	oai:doaj.org-article:81f7b934fe4245148f554804ed1054612021-12-02T14:15:53ZDeep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs10.1038/s41598-021-87141-x2045-2322https://doaj.org/article/81f7b934fe4245148f554804ed1054612021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87141-xhttps://doaj.org/toc/2045-2322Abstract Human spinal balance assessment relies considerably on sagittal radiographic parameter measurement. Deep learning could be applied for automatic landmark detection and alignment analysis, with mild to moderate standard errors and favourable correlations with manual measurement. In this study, based on 2210 annotated images of various spinal disease aetiologies, we developed deep learning models capable of automatically locating 45 anatomic landmarks and subsequently generating 18 radiographic parameters on a whole-spine lateral radiograph. In the assessment of model performance, the localisation accuracy and learning speed were the highest for landmarks in the cervical area, followed by those in the lumbosacral, thoracic, and femoral areas. All the predicted radiographic parameters were significantly correlated with ground truth values (all p < 0.001). The human and artificial intelligence comparison revealed that the deep learning model was capable of matching the reliability of doctors for 15/18 of the parameters. The proposed automatic alignment analysis system was able to localise spinal anatomic landmarks with high accuracy and to generate various radiographic parameters with favourable correlations with manual measurements.Yu-Cheng YehChi-Hung WengYu-Jui HuangChen-Ju FuTsung-Ting TsaiChao-Yuan YehNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yu-Cheng Yeh Chi-Hung Weng Yu-Jui Huang Chen-Ju Fu Tsung-Ting Tsai Chao-Yuan Yeh Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs
description	Abstract Human spinal balance assessment relies considerably on sagittal radiographic parameter measurement. Deep learning could be applied for automatic landmark detection and alignment analysis, with mild to moderate standard errors and favourable correlations with manual measurement. In this study, based on 2210 annotated images of various spinal disease aetiologies, we developed deep learning models capable of automatically locating 45 anatomic landmarks and subsequently generating 18 radiographic parameters on a whole-spine lateral radiograph. In the assessment of model performance, the localisation accuracy and learning speed were the highest for landmarks in the cervical area, followed by those in the lumbosacral, thoracic, and femoral areas. All the predicted radiographic parameters were significantly correlated with ground truth values (all p < 0.001). The human and artificial intelligence comparison revealed that the deep learning model was capable of matching the reliability of doctors for 15/18 of the parameters. The proposed automatic alignment analysis system was able to localise spinal anatomic landmarks with high accuracy and to generate various radiographic parameters with favourable correlations with manual measurements.
format	article
author	Yu-Cheng Yeh Chi-Hung Weng Yu-Jui Huang Chen-Ju Fu Tsung-Ting Tsai Chao-Yuan Yeh
author_facet	Yu-Cheng Yeh Chi-Hung Weng Yu-Jui Huang Chen-Ju Fu Tsung-Ting Tsai Chao-Yuan Yeh
author_sort	Yu-Cheng Yeh
title	Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs
title_short	Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs
title_full	Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs
title_fullStr	Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs
title_full_unstemmed	Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs
title_sort	deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/81f7b934fe4245148f554804ed105461
work_keys_str_mv	AT yuchengyeh deeplearningapproachforautomaticlandmarkdetectionandalignmentanalysisinwholespinelateralradiographs AT chihungweng deeplearningapproachforautomaticlandmarkdetectionandalignmentanalysisinwholespinelateralradiographs AT yujuihuang deeplearningapproachforautomaticlandmarkdetectionandalignmentanalysisinwholespinelateralradiographs AT chenjufu deeplearningapproachforautomaticlandmarkdetectionandalignmentanalysisinwholespinelateralradiographs AT tsungtingtsai deeplearningapproachforautomaticlandmarkdetectionandalignmentanalysisinwholespinelateralradiographs AT chaoyuanyeh deeplearningapproachforautomaticlandmarkdetectionandalignmentanalysisinwholespinelateralradiographs
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Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs

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