{"title":"解剖学表面拓扑参数在显示脊柱侧凸方面的准确性如何？","authors":"Adrian Gardner, Fiona Berryman, Paul Pynsent","doi":"10.1097/BRS.0000000000004990","DOIUrl":null,"url":null,"abstract":"<p><strong>Study design: </strong>Retrospective analysis of a longitudinal cohort.</p><p><strong>Objective: </strong>To identify the presence of scoliosis from surface data.</p><p><strong>Summary of background data: </strong>Identifying AIS can be difficult. Screening is not universal for reasons including high false positive and negative rates. These difficulties can lead to some adolescents missing out on bracing.</p><p><strong>Methods: </strong>Logistic regression analysis of ISIS2 surface topography images only was performed. The x,y positions of the shoulders (Sh), axillae (Ax), waist (waist) and the x,y,z positions of the most prominent points over the posterior torso (scap) were used for the thoracic, thoracolumbar/lumbar and whole spine. The models were used to identify the presence of a 20-degree or larger scoliosis. Differences in the position of the landmarks were analyzed comparing left and right, with the suffix \"Ht\" representing a difference in the y coordinate, \"Off\" the x coordinate, and \"Depth,\" the z coordinate. Model accuracy was assessed as both percentages and ROC curves with the coefficients as odds ratios.</p><p><strong>Results: </strong>There were 1283 images (1015 females and 268 males) all with a diagnosis of AIS. The models identified scoliosis in the thoracic spine with an 83% accuracy (AUC 0.91), thoracolumbar/lumbar spine with 74% accuracy (AUC 0.76), and whole spine with 80% accuracy (AUC 0.88). Significant parameters were AxDiffHt, AxDiffOff, WaistDiffHt, ScapDiffOff, and ScapDiffHt for the thoracic curves, AxDiffHt, AxDiffOff, WaistDiffHt for the thoracolumbar/lumbar curves, and AxDiffHt, AxDiffOff, WaistDiffHt and ScapDiffHt for the whole spine.</p><p><strong>Conclusions: </strong>The use of fixed anatomical points around the torso, analyzed using logistic regression, has a high accuracy for identifying curves in the thoracic, thoracolumbar/lumbar, and whole spines. While coming from surface topography images, the results raise the future use of digital photography as a tool for the identification of small scoliosis without using other imaging techniques.</p>","PeriodicalId":22193,"journal":{"name":"Spine","volume":" ","pages":"1645-1651"},"PeriodicalIF":2.6000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How Accurate Are Anatomical Surface Topography Parameters in Indicating the Presence of a Scoliosis?\",\"authors\":\"Adrian Gardner, Fiona Berryman, Paul Pynsent\",\"doi\":\"10.1097/BRS.0000000000004990\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Study design: </strong>Retrospective analysis of a longitudinal cohort.</p><p><strong>Objective: </strong>To identify the presence of scoliosis from surface data.</p><p><strong>Summary of background data: </strong>Identifying AIS can be difficult. Screening is not universal for reasons including high false positive and negative rates. These difficulties can lead to some adolescents missing out on bracing.</p><p><strong>Methods: </strong>Logistic regression analysis of ISIS2 surface topography images only was performed. The x,y positions of the shoulders (Sh), axillae (Ax), waist (waist) and the x,y,z positions of the most prominent points over the posterior torso (scap) were used for the thoracic, thoracolumbar/lumbar and whole spine. The models were used to identify the presence of a 20-degree or larger scoliosis. Differences in the position of the landmarks were analyzed comparing left and right, with the suffix \\\"Ht\\\" representing a difference in the y coordinate, \\\"Off\\\" the x coordinate, and \\\"Depth,\\\" the z coordinate. Model accuracy was assessed as both percentages and ROC curves with the coefficients as odds ratios.</p><p><strong>Results: </strong>There were 1283 images (1015 females and 268 males) all with a diagnosis of AIS. The models identified scoliosis in the thoracic spine with an 83% accuracy (AUC 0.91), thoracolumbar/lumbar spine with 74% accuracy (AUC 0.76), and whole spine with 80% accuracy (AUC 0.88). Significant parameters were AxDiffHt, AxDiffOff, WaistDiffHt, ScapDiffOff, and ScapDiffHt for the thoracic curves, AxDiffHt, AxDiffOff, WaistDiffHt for the thoracolumbar/lumbar curves, and AxDiffHt, AxDiffOff, WaistDiffHt and ScapDiffHt for the whole spine.</p><p><strong>Conclusions: </strong>The use of fixed anatomical points around the torso, analyzed using logistic regression, has a high accuracy for identifying curves in the thoracic, thoracolumbar/lumbar, and whole spines. While coming from surface topography images, the results raise the future use of digital photography as a tool for the identification of small scoliosis without using other imaging techniques.</p>\",\"PeriodicalId\":22193,\"journal\":{\"name\":\"Spine\",\"volume\":\" \",\"pages\":\"1645-1651\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Spine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/BRS.0000000000004990\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/3/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/BRS.0000000000004990","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/3/20 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
How Accurate Are Anatomical Surface Topography Parameters in Indicating the Presence of a Scoliosis?
Study design: Retrospective analysis of a longitudinal cohort.
Objective: To identify the presence of scoliosis from surface data.
Summary of background data: Identifying AIS can be difficult. Screening is not universal for reasons including high false positive and negative rates. These difficulties can lead to some adolescents missing out on bracing.
Methods: Logistic regression analysis of ISIS2 surface topography images only was performed. The x,y positions of the shoulders (Sh), axillae (Ax), waist (waist) and the x,y,z positions of the most prominent points over the posterior torso (scap) were used for the thoracic, thoracolumbar/lumbar and whole spine. The models were used to identify the presence of a 20-degree or larger scoliosis. Differences in the position of the landmarks were analyzed comparing left and right, with the suffix "Ht" representing a difference in the y coordinate, "Off" the x coordinate, and "Depth," the z coordinate. Model accuracy was assessed as both percentages and ROC curves with the coefficients as odds ratios.
Results: There were 1283 images (1015 females and 268 males) all with a diagnosis of AIS. The models identified scoliosis in the thoracic spine with an 83% accuracy (AUC 0.91), thoracolumbar/lumbar spine with 74% accuracy (AUC 0.76), and whole spine with 80% accuracy (AUC 0.88). Significant parameters were AxDiffHt, AxDiffOff, WaistDiffHt, ScapDiffOff, and ScapDiffHt for the thoracic curves, AxDiffHt, AxDiffOff, WaistDiffHt for the thoracolumbar/lumbar curves, and AxDiffHt, AxDiffOff, WaistDiffHt and ScapDiffHt for the whole spine.
Conclusions: The use of fixed anatomical points around the torso, analyzed using logistic regression, has a high accuracy for identifying curves in the thoracic, thoracolumbar/lumbar, and whole spines. While coming from surface topography images, the results raise the future use of digital photography as a tool for the identification of small scoliosis without using other imaging techniques.
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Recognized internationally as the leading journal in its field, Spine is an international, peer-reviewed, bi-weekly periodical that considers for publication original articles in the field of Spine. It is the leading subspecialty journal for the treatment of spinal disorders. Only original papers are considered for publication with the understanding that they are contributed solely to Spine. The Journal does not publish articles reporting material that has been reported at length elsewhere.
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