Computer Science Department, Brigham Young University, Campus Dr, Provo, UT, 3361 TMCB84604, USA.
Department of Orthopedic Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA.
J Digit Imaging. 2022 Dec;35(6):1494-1505. doi: 10.1007/s10278-022-00671-2. Epub 2022 Jul 6.
Leg length discrepancies are common orthopedic problems with the potential for poor functional outcomes. These are frequently assessed using bilateral leg length radiographs. The objective was to determine whether an artificial intelligence (AI)-based image analysis system can accurately interpret long leg length radiographic images. We built an end-to-end system to analyze leg length radiographs and generate reports like radiologists, which involves measurement of lengths (femur, tibia, entire leg) and angles (mechanical axis and pelvic tilt), describes presence and location of orthopedic hardware, and reports laterality discrepancies. After IRB approval, a dataset of 1,726 extremities (863 images) from consecutive examinations at a tertiary referral center was retrospectively acquired and partitioned into train/validation and test sets. The training set was annotated and used to train a fasterRCNN-ResNet101 object detection convolutional neural network. A second-stage classifier using a EfficientNet-D0 model was trained to recognize the presence or absence of hardware within extracted joint image patches. The system was deployed in a custom web application that generated a preliminary radiology report. Performance of the system was evaluated using a holdout 220 image test set, annotated by 3 musculoskeletal fellowship trained radiologists. At the object detection level, the system demonstrated a recall of 0.98 and precision of 0.96 in detecting anatomic landmarks. Correlation coefficients between radiologist and AI-generated measurements for femur, tibia, and whole-leg lengths were > 0.99, with mean error of < 1%. Correlation coefficients for mechanical axis angle and pelvic tilt were 0.98 and 0.86, respectively, with mean absolute error of < 1°. AI hardware detection demonstrated an accuracy of 99.8%. Automatic quantitative and qualitative analysis of leg length radiographs using deep learning is feasible and holds potential in improving radiologist workflow.
肢体长度差异是常见的骨科问题，可能导致不良的功能结果。这些通常通过双侧下肢长度 X 射线片进行评估。目的是确定基于人工智能 (AI) 的图像分析系统是否可以准确解释长腿长度 X 射线图像。我们构建了一个端到端系统来分析下肢长度 X 射线图像并生成与放射科医生类似的报告，包括测量长度（股骨、胫骨、整个腿部）和角度（机械轴和骨盆倾斜）、描述骨科硬件的存在和位置，以及报告偏侧性差异。在获得机构审查委员会 (IRB) 批准后，回顾性地从一家三级转诊中心的连续检查中获取了 1726 个肢体（863 张图像）数据集，并将其分为训练/验证集和测试集。使用更快的 RCNN-ResNet101 目标检测卷积神经网络对训练集进行注释和训练。使用 EfficientNet-D0 模型训练了一个第二阶段分类器，以识别提取的关节图像斑块中硬件的存在或不存在。该系统部署在一个自定义 Web 应用程序中，该应用程序生成初步的放射学报告。使用由 3 名肌肉骨骼研究员培训的放射科医生注释的 220 张保留图像测试集评估系统的性能。在目标检测级别，系统在检测解剖学标记方面表现出 0.98 的召回率和 0.96 的精度。放射科医生和 AI 生成的股骨、胫骨和整个腿部长度测量值之间的相关系数>0.99，平均误差<1%。机械轴角度和骨盆倾斜的相关系数分别为 0.98 和 0.86，平均绝对误差<1%。AI 硬件检测的准确率为 99.8%。使用深度学习对下肢长度 X 射线进行自动定量和定性分析是可行的，并有可能改善放射科医生的工作流程。
