Scoliosis is a 3D-torsional rotation of the spine, but risk factors for initiation and progression are little understood. Research is hampered by lack of population-based research since radiographs cannot be performed on entire populations due to the relatively high levels of ionising radiation. Hence we have developed and validated a manual method for identifying scoliosis from total body dual energy X-ray absorptiometry (DXA) scans for research purposes. However, to allow full utilisation of population-based research cohorts, this needs to be automated. The purpose of this study was therefore to automate the identification of spinal curvature from total body DXA scans using machine learning techniques. To validate the automation, we assessed: (1) sensitivity, specificity and area under the receiver operator curve value (AUC) by comparison with 12,000 manually annotated images; (2) reliability by rerunning the automation on a subset of DXA scans repeated 2-6 weeks apart and calculating the kappa statistic; (3) validity by applying the automation to 5000 non-annotated images to assess associations with epidemiological variables. The final automated model had a sensitivity of 86.5%, specificity of 96.9% and an AUC of 0.80 (95%CI 0.74-0.87). There was almost perfect agreement of identification of those with scoliosis (kappa 0.90). Those with scoliosis identified by the automated model showed similar associations with gender, ethnicity, socioeconomic status, BMI and lean mass to previous literature. In conclusion, we have developed an accurate and valid automated method for identifying and quantifying spinal curvature from total body DXA scans.

中文翻译：

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在基于人群的队列中识别脊柱侧弯：基于全身双能 X 射线吸收测量扫描的验证方法的自动化。

脊柱侧弯是脊柱的 3D 扭转旋转，但对起始和进展的风险因素知之甚少。由于电离辐射水平相对较高，无法对整个人群进行射线照相，因此缺乏基于人群的研究阻碍了研究。因此，我们开发并验证了一种手动方法，用于从全身双能 X 射线吸收测定 (DXA) 扫描中识别脊柱侧弯，以用于研究目的。然而，为了充分利用基于人群的研究队列，这需要自动化。因此，本研究的目的是使用机器学习技术从全身 DXA 扫描中自动识别脊柱弯曲。为了验证自动化，我们评估了：（1）敏感性，通过与 12,000 个手动注释图像进行比较的特异性和接受者操作曲线下面积 (AUC)；(2) 通过在 DXA 扫描子集上重新运行自动化并重复 2-6 周并计算 kappa 统计数据来实现可靠性；(3) 通过将自动化应用于 5000 个未注释图像以评估与流行病学变量的关联的有效性。最终自动化模型的敏感性为 86.5%，特异性为 96.9%，AUC 为 0.80 (95%CI 0.74-0.87)。脊柱侧弯患者的识别几乎完全一致（kappa 0.90）。由自动化模型确定的脊柱侧弯患者与以前的文献显示出与性别、种族、社会经济地位、BMI 和瘦体重相似的关联。综上所述，