Computer-Aided Diagnosis for Determining Sagittal Spinal Curvatures Using Deep Learning and Radiography
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Hyo Min | - |
dc.contributor.author | Kim, Young Jae | - |
dc.contributor.author | Cho, Je Bok | - |
dc.contributor.author | Jeon, Ji Young | - |
dc.contributor.author | Kim, Kwang Gi | - |
dc.date.accessioned | 2022-09-22T23:40:05Z | - |
dc.date.available | 2022-09-22T23:40:05Z | - |
dc.date.created | 2022-04-06 | - |
dc.date.issued | 2022-08 | - |
dc.identifier.issn | 0897-1889 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/85497 | - |
dc.description.abstract | Analyzing spinal curvatures manually is time-consuming and tedious for clinicians, and intra-observer and inter-observer variability can affect manual measurements. In this study, we developed and evaluated the performance of an automated deep learning-based computer-aided diagnosis (CAD) tool for measuring the sagittal alignment of the spine from X-ray images. The CAD system proposed here performs two functions: deep learning-based lateral spine segmentation and automatic analysis of thoracic kyphosis and lumbar lordosis angles. We utilized 322 datasets with data augmentation for learning and fivefold cross-validation. The segmentation model was based on U-Net, which has multiple applications in medical image processing. Here, we utilized parameter equations and trigonometric functions to design spinal angle measurement algorithms. The kyphosis (T4-T12) and lordosis angle (L1-S1, L1-L5) were automatically measured to help diagnose kyphosis and lordosis. The segmentation model had precision, sensitivity, and dice similarity coefficient values of 90.53 +/- 4.61%, 89.53 +/- 1.8%, and 90.22 +/- 0.62%, respectively. The performance of the CAD algorithm was also verified with the Pearson correlation, Bland-Altman, and intra-class correlation coefficient (ICC) analysis. The proposed angle measurement algorithm exhibited high similarity and reliability during verification. Therefore, CAD can help clinicians in reaching a diagnosis by analyzing the sagittal spinal curvatures while reducing observer-based variability and the required time or effort. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.relation.isPartOf | JOURNAL OF DIGITAL IMAGING | - |
dc.title | Computer-Aided Diagnosis for Determining Sagittal Spinal Curvatures Using Deep Learning and Radiography | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000767751400001 | - |
dc.identifier.doi | 10.1007/s10278-022-00592-0 | - |
dc.identifier.bibliographicCitation | JOURNAL OF DIGITAL IMAGING, v.35, no.4, pp.846 - 859 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85126151366 | - |
dc.citation.endPage | 859 | - |
dc.citation.startPage | 846 | - |
dc.citation.title | JOURNAL OF DIGITAL IMAGING | - |
dc.citation.volume | 35 | - |
dc.citation.number | 4 | - |
dc.contributor.affiliatedAuthor | Lee, Hyo Min | - |
dc.contributor.affiliatedAuthor | Kim, Young Jae | - |
dc.contributor.affiliatedAuthor | Cho, Je Bok | - |
dc.contributor.affiliatedAuthor | Jeon, Ji Young | - |
dc.contributor.affiliatedAuthor | Kim, Kwang Gi | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Segmentation | - |
dc.subject.keywordAuthor | Computer-aided diagnosis | - |
dc.subject.keywordAuthor | Thoracic kyphosis | - |
dc.subject.keywordAuthor | Lumbar lordosis | - |
dc.subject.keywordPlus | LOW-BACK-PAIN | - |
dc.subject.keywordPlus | QUALITY-OF-LIFE | - |
dc.subject.keywordPlus | COBB-ANGLE | - |
dc.subject.keywordPlus | SCOLIOSIS | - |
dc.subject.keywordPlus | INTRAOBSERVER | - |
dc.subject.keywordPlus | INTEROBSERVER | - |
dc.subject.keywordPlus | DEFORMITY | - |
dc.subject.keywordPlus | KYPHOSIS | - |
dc.subject.keywordPlus | RELIABILITY | - |
dc.subject.keywordPlus | PARAMETERS | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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