Department of Orthopaedic Surgery, Spine Division, Bone and Joint Research Center, Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Taoyuan, Taiwan, ROC.
aetherAI Co., Ltd., 9F., No.3-2, Yuanqu St., Nangang Dist., Taipei City, 115, Taiwan, ROC.
Sci Rep. 2021 Apr 7;11(1):7618. doi: 10.1038/s41598-021-87141-x.
Human spinal balance assessment relies considerably on sagittal radiographic parameter measurement. Deep learning could be applied for automatic landmark detection and alignment analysis, with mild to moderate standard errors and favourable correlations with manual measurement. In this study, based on 2210 annotated images of various spinal disease aetiologies, we developed deep learning models capable of automatically locating 45 anatomic landmarks and subsequently generating 18 radiographic parameters on a whole-spine lateral radiograph. In the assessment of model performance, the localisation accuracy and learning speed were the highest for landmarks in the cervical area, followed by those in the lumbosacral, thoracic, and femoral areas. All the predicted radiographic parameters were significantly correlated with ground truth values (all p < 0.001). The human and artificial intelligence comparison revealed that the deep learning model was capable of matching the reliability of doctors for 15/18 of the parameters. The proposed automatic alignment analysis system was able to localise spinal anatomic landmarks with high accuracy and to generate various radiographic parameters with favourable correlations with manual measurements.
人体脊柱平衡评估主要依赖于矢状面影像学参数测量。深度学习可应用于自动标记检测和对齐分析，具有轻微至中度的标准误差，并与手动测量具有良好的相关性。在这项研究中，基于 2210 张各种脊柱疾病病因的标注图像，我们开发了能够自动定位 45 个解剖标记并随后在整个脊柱侧位片上生成 18 个影像学参数的深度学习模型。在评估模型性能时，颈椎区域标记的定位准确性和学习速度最高，其次是腰骶部、胸腰椎和股骨区域。所有预测的影像学参数均与真实值显著相关（均 p<0.001）。人与人工智能的比较表明，深度学习模型能够匹配医生在 18 个参数中的 15 个的可靠性。所提出的自动对齐分析系统能够以高精度定位脊柱解剖标记，并生成与手动测量具有良好相关性的各种影像学参数。
