Optasia Medical Ltd, Cheadle, Cheshire, United Kingdom.
Spine (Phila Pa 1976). 2009 Oct 15;34(22):2437-43. doi: 10.1097/BRS.0b013e3181b2eb69.
Image analysis model development.
The objective of this study was to develop a novel clinical workflow tool that uses model-based shape recognition technology to allow efficient, semiautomated detailed annotation of each vertebra between T4 and L4 on plain lateral radiographs.
Identification of prevalent vertebral fractures, especially when not symptomatic, has been problematic despite their importance. There is a recognized need to increase the opportunities to detect vertebral fractures so that clinically beneficial therapeutic interventions can be initiated.
Radiographs obtained from 165 subjects in the Canadian Multicenter Osteoporosis Study (CaMos) were used to construct a vertebral shape model of the vertebral column from T4 to L4 using a statistical learning technique, as well as to estimate the accuracy and precision of this automated software tool for vertebral shape analysis. Radiographs showing scoliosis greater than 15 degrees were excluded.
Vertebral contours defined by 95 points per vertebra, represented by 79,895 points in total, were assessed on 841 individual vertebrae. The mean absolute accuracy error calculated over each vertebra in each test image was 1.06 +/- 1.2 mm. This value corresponded to an average 3.4% of vertebral height. The mean precision error, reflecting interobserver variability, per vertebra of the resulting annotations was 0.61 +/- 0.73 mm. This value corresponded to an average 2.3% of vertebral height. Accuracy and precision error estimates did not differ notably by vertebral level.
The results of the current study indicate that statistical modeling can provide a robust tool for the accurate and precise semiautomated annotation of vertebral body shape from T4 to L4 in patients who do not have scoliosis greater than 15 degrees . This method may prove useful as a clinical workflow tool to aid the physician in vertebral fracture assessment and might contribute to decision-making about pharmacologic treatment of osteoporosis.
图像分析模型开发。
本研究的目的是开发一种新的临床工作流程工具，该工具使用基于模型的形状识别技术，允许在 T4 至 L4 之间的普通侧位 X 光片上高效、半自动地详细标注每个椎体。
尽管椎体骨折很重要，但识别常见的椎体骨折，尤其是无症状的椎体骨折，一直存在问题。人们认识到需要增加发现椎体骨折的机会，以便能够开始进行有益的临床治疗干预。
使用来自加拿大多中心骨质疏松研究（CaMos）的 165 名受试者的 X 光片，使用统计学习技术构建 T4 至 L4 椎体的椎体形状模型，并评估该自动软件工具进行椎体形状分析的准确性和精密度。排除显示脊柱侧凸大于 15 度的 X 光片。
评估了 841 个个体椎体的每个椎体 95 个点定义的椎体轮廓，总共代表 79895 个点。在每个测试图像中的每个椎体上计算的平均绝对精度误差为 1.06 +/- 1.2 毫米。这个值对应于椎体高度的平均 3.4%。每个椎体标注结果的平均精度误差（反映观察者间的可变性）为 0.61 +/- 0.73 毫米。这个值对应于椎体高度的平均 2.3%。椎体水平的准确性和精度误差估计没有显著差异。
本研究的结果表明，统计建模可以为没有大于 15 度脊柱侧凸的患者从 T4 到 L4 准确、精确地半自动标注椎体形状提供一个强大的工具。这种方法可能作为一种临床工作流程工具很有用，可以帮助医生评估椎体骨折，并可能有助于决策是否进行骨质疏松症的药物治疗。
