Background: 

Establishing imaging registries for large patient cohorts is challenging because manual labeling is tedious and relying solely on DICOM (digital imaging and communications in medicine) metadata can result in errors. We endeavored to establish an automated hip and pelvic radiography registry of total hip arthroplasty (THA) patients by utilizing deep-learning pipelines. The aims of the study were (1) to utilize these automated pipelines to identify all pelvic and hip radiographs with appropriate annotation of laterality and presence or absence of implants, and (2) to automatically measure acetabular component inclination and version for THA images.

Methods: 

We retrospectively retrieved 846,988 hip and pelvic radiography DICOM files from 20,378 patients who underwent primary or revision THA performed at our institution from 2000 to 2020. Metadata for the files were screened followed by extraction of imaging data. Two deep-learning algorithms (an EfficientNetB3 classifier and a YOLOv5 object detector) were developed to automatically determine the radiographic appearance of all files. Additional deep-learning algorithms were utilized to automatically measure the acetabular angles on anteroposterior pelvic and lateral hip radiographs. Algorithm performance was compared with that of human annotators on a random test sample of 5,000 radiographs.

Results: 

Deep-learning algorithms enabled appropriate exclusion of 209,332 DICOM files (24.7%) as misclassified non-hip/pelvic radiographs or having corrupted pixel data. The final registry was automatically curated and annotated in <8 hours and included 168,551 anteroposterior pelvic, 176,890 anteroposterior hip, 174,637 lateral hip, and 117,578 oblique hip radiographs. The algorithms achieved 99.9% accuracy, 99.6% precision, 99.5% recall, and a 99.6% F1 score in determining the radiograph appearance.

Conclusions: 

We developed a highly accurate series of deep-learning algorithms to rapidly curate and annotate THA patient radiographs. This efficient pipeline can be utilized by other institutions or registries to construct radiography databases for patient care, longitudinal surveillance, and large-scale research. The stepwise approach for establishing a radiography registry can further be utilized as a workflow guide for other anatomic areas.

Level of Evidence: 

Diagnostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

中文翻译：

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应用深度学习建立全髋关节置换术放射照相登记处：逐步方法

背景： 

为大型患者群体建立影像登记具有挑战性，因为手动标记非常繁琐，并且仅依赖 DICOM（医学数字成像和通信）元数据可能会导致错误。我们致力于利用深度学习管道建立全髋关节置换术 (THA) 患者的自动化髋关节和骨盆放射照相登记处。该研究的目的是 (1) 利用这些自动化管道来识别所有骨盆和髋部 X 光片，并适当注释偏侧性和是否存在植入物，以及 (2) 自动测量髋臼组件的倾斜度和 THA 图像的版本。

方法： 

我们回顾性检索了 20,378 名在我们机构于 2000 年至 2020 年间接受初次或翻修 THA 的患者的 846,988 个髋部和骨盆 X 线摄影 DICOM 文件。筛选文件的元数据，然后提取影像数据。开发了两种深度学习算法（EfficientNetB3 分类器和 YOLOv5 对象检测器）来自动确定所有文件的放射线外观。利用额外的深度学习算法自动测量骨盆前后位和髋关节侧位X光片上的髋臼角度。在 5,000 张射线照片的随机测试样本上，将算法性能与人类注释者的性能进行了比较。

结果： 

深度学习算法能够将 209,332 个 DICOM 文件 (24.7%) 适当排除为错误分类的非髋部/骨盆 X 光照片或像素数据损坏的文件。最终登记在 8 小时内自动整理和注释，包括 168,551 个骨盆前后位、176,890 个髋关节前后位、174,637 个髋关节侧位和 117,578 个倾斜髋位 X 光片。该算法在确定射线照片外观方面实现了 99.9% 的准确度、99.6% 的精确度、99.5% 的召回率和 99.6% 的 F1 分数。

结论： 

我们开发了一系列高度准确的深度学习算法来快速整理和注释 THA 患者的 X 光照片。其他机构或注册机构可以利用这种高效的管道来构建放射线摄影数据库，用于患者护理、纵向监测和大规模研究。建立放射照相登记的逐步方法可以进一步用作其他解剖区域的工作流程指南。

证据级别： 

诊断级别 IV。有关证据级别的完整描述，请参阅作者须知。