Galbusera Fabio, Bassani Tito, Panico Matteo, Sconfienza Luca Maria, Cina Andrea
Spine Center, Schulthess Clinic, Zurich, Switzerland.
IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
Front Bioeng Biotechnol. 2022 Jul 15;10:863054. doi: 10.3389/fbioe.2022.863054. eCollection 2022.
We developed and used a deep learning tool to process biplanar radiographs of 9,832 non-surgical patients suffering from spinal deformities, with the aim of reporting the statistical distribution of radiological parameters describing the spinal shape and the correlations and interdependencies between them. An existing tool able to automatically perform a three-dimensional reconstruction of the thoracolumbar spine has been improved and used to analyze a large set of biplanar radiographs of the trunk. For all patients, the following parameters were calculated: spinopelvic parameters; lumbar lordosis; mismatch between pelvic incidence and lumbar lordosis; thoracic kyphosis; maximal coronal Cobb angle; sagittal vertical axis; T1-pelvic angle; maximal vertebral rotation in the transverse plane. The radiological parameters describing the sagittal alignment were found to be highly interrelated with each other, as well as dependent on age, while sex had relatively minor but statistically significant importance. Lumbar lordosis was associated with thoracic kyphosis, pelvic incidence and sagittal vertical axis. The pelvic incidence-lumbar lordosis mismatch was found to be dependent on the pelvic incidence and on age. Scoliosis had a distinct association with the sagittal alignment in adolescent and adult subjects. The deep learning-based tool allowed for the analysis of a large imaging database which would not be reasonably feasible if performed by human operators. The large set of results will be valuable to trigger new research questions in the field of spinal deformities, as well as to challenge the current knowledge.
我们开发并使用了一种深度学习工具来处理9832名患有脊柱畸形的非手术患者的双平面X线片，目的是报告描述脊柱形状的放射学参数的统计分布以及它们之间的相关性和相互依赖性。一种能够自动对胸腰椎进行三维重建的现有工具得到了改进，并用于分析大量的躯干双平面X线片。对于所有患者，计算了以下参数：脊柱骨盆参数；腰椎前凸；骨盆倾斜度与腰椎前凸的不匹配；胸椎后凸；最大冠状面Cobb角；矢状垂直轴；T1-骨盆角；横平面内最大椎体旋转度。发现描述矢状位排列的放射学参数彼此高度相关，并且还取决于年龄，而性别具有相对较小但在统计学上具有显著意义。腰椎前凸与胸椎后凸、骨盆倾斜度和矢状垂直轴相关。发现骨盆倾斜度-腰椎前凸不匹配取决于骨盆倾斜度和年龄。在青少年和成年受试者中，脊柱侧弯与矢状位排列有明显关联。基于深度学习的工具允许对一个大型影像数据库进行分析，如果由人工操作则不太可行。大量的结果对于引发脊柱畸形领域的新研究问题以及挑战当前的知识将是有价值的。
