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Temporal Trends in Cervical Spine Curvature of South Korean Adults Assessed by Deep Learning System Segmentation, 2006-2018

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dc.contributor.author이영한-
dc.contributor.author한경화-
dc.date.accessioned2021-05-21T17:02:01Z-
dc.date.available2021-05-21T17:02:01Z-
dc.date.issued2020-10-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/182661-
dc.description.abstractImportance: The loss of the physiologic cervical lordotic curve is a common degenerative disorder known to be associated with abnormal spinal alignment. However, the changing trends among sex and age groups has not yet been well established. Objective: To analyze the temporal trends in cervical curvature across sex and age groups using an automated deep learning system (DLS). Design, setting, and participants: A retrospective cross-sectional study was conducted using lateral cervical radiographs of 13 691 individuals from January 1, 2006, to December 31, 2018. The degree of anterior vertical curvature was approximated by a DLS approach and convexity measurement method. This population-based study used the Yonsei University College of Medicine Severance Hospital, Seoul, South Korea, cohort database to identify 13 691 consecutive adults (≥18 years of age) who underwent standing lateral radiography in inpatient and outpatient settings. Main outcomes and measures: The prevalence of kyphotic and straight cervical curve as well as the trends of degree of cervical curvature in 2006 to 2018 among sex and age groups were determined. The DLS performance was validated with quantitative metrics and compared with interobserver and intraobserver variations. Results: Automatic cervical spine segmentation was identified from lateral radiographs of 13 691 individuals (mean [SD] age, 49.9 [15.3] years; 8051 women [58.8%]). From 2006 to 2018, the decrease in the lordotic curve was significant across both sexes and age groups younger than 70 years, with the decrease more pronounced in women and successively younger generations (female, -0.05; 95% CI, -0.06 to -0.04; 18-29 years of age, -0.06; 95% CI, -0.08 to -0.04; 30-39 years of age, -0.06; 95% CI, -0.08 to -0.04; and 40-49 years of age, -0.05; 95% CI, -0.06 to -0.03; all P < .001). The prevalence of straight and kyphotic curvature had a significant increasing trend for both sexes and young generations, in which individuals 18 to 29 years of age generally had the highest prevalence rates during the study cycle (in 2018, kyphosis, 16.7%; 95% CI, 10.8%-22.5%; straight, 45.5%; 95% CI, 37.7%-53.3%). Similar trends were observed with longitudinal analysis of repeated measures of individuals, with more pronounced decreases in lordotic curvature observed among women and young adults. Conclusions and relevance: This study suggests a significant, increasing loss of normal cervical lordotic curvature for both sexes and young adults that is greater in progressively younger cohorts and women. Further research is necessary to evaluate associations between neck pain and loss of cervical curvature and address the need for active promotion and practical interventions aimed at neck posture correction.-
dc.description.statementOfResponsibilityopen-
dc.formatapplication/pdf-
dc.languageEnglish-
dc.publisherAmerican Medical Association-
dc.relation.isPartOfJAMA NETWORK OPEN-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAdult-
dc.subject.MESHAge Factors-
dc.subject.MESHCervical Vertebrae / diagnostic imaging*-
dc.subject.MESHCervical Vertebrae / physiopathology-
dc.subject.MESHCross-Sectional Studies-
dc.subject.MESHDeep Learning*-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHKyphosis / diagnostic imaging*-
dc.subject.MESHKyphosis / physiopathology-
dc.subject.MESHLordosis / diagnostic imaging*-
dc.subject.MESHLordosis / physiopathology-
dc.subject.MESHMale-
dc.subject.MESHMiddle Aged-
dc.subject.MESHRepublic of Korea-
dc.subject.MESHRetrospective Studies-
dc.subject.MESHSex Factors-
dc.subject.MESHSpinal Curvatures / diagnostic imaging-
dc.subject.MESHYoung Adult-
dc.titleTemporal Trends in Cervical Spine Curvature of South Korean Adults Assessed by Deep Learning System Segmentation, 2006-2018-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorYiRang Shin-
dc.contributor.googleauthorKyunghwa Han-
dc.contributor.googleauthorYoung Han Lee-
dc.identifier.doi10.1001/jamanetworkopen.2020.20961-
dc.contributor.localIdA02967-
dc.relation.journalcodeJ03719-
dc.identifier.eissn2574-3805-
dc.identifier.pmid33057644-
dc.contributor.alternativeNameLee, Young Han-
dc.contributor.affiliatedAuthor이영한-
dc.citation.volume3-
dc.citation.number10-
dc.citation.startPagee20200961-
dc.identifier.bibliographicCitationJAMA NETWORK OPEN, Vol.3(10) : e20200961, 2020-10-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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