Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.
Sci Rep. 2024 May 23;14(1):11768. doi: 10.1038/s41598-024-60355-5.
Accurate selection of sampling positions is critical in renal artery ultrasound examinations, and the potential of utilizing deep learning (DL) for assisting in this selection has not been previously evaluated. This study aimed to evaluate the effectiveness of DL object detection technology applied to color Doppler sonography (CDS) images in assisting sampling position selection. A total of 2004 patients who underwent renal artery ultrasound examinations were included in the study. CDS images from these patients were categorized into four groups based on the scanning position: abdominal aorta (AO), normal renal artery (NRA), renal artery stenosis (RAS), and intrarenal interlobular artery (IRA). Seven object detection models, including three two-stage models (Faster R-CNN, Cascade R-CNN, and Double Head R-CNN) and four one-stage models (RetinaNet, YOLOv3, FoveaBox, and Deformable DETR), were trained to predict the sampling position, and their predictive accuracies were compared. The Double Head R-CNN model exhibited significantly higher average accuracies on both parameter optimization and validation datasets (89.3 ± 0.6% and 88.5 ± 0.3%, respectively) compared to other methods. On clinical validation data, the predictive accuracies of the Double Head R-CNN model for all four types of images were significantly higher than those of the other methods. The DL object detection model shows promise in assisting inexperienced physicians in improving the accuracy of sampling position selection during renal artery ultrasound examinations.
在肾动脉超声检查中，准确选择采样位置至关重要，而利用深度学习（DL）辅助选择的潜力尚未得到评估。本研究旨在评估 DL 目标检测技术在彩色多谱勒超声（CDS）图像中辅助采样位置选择的有效性。共纳入 2004 名接受肾动脉超声检查的患者。根据扫描位置，将这些患者的 CDS 图像分为四组：腹主动脉（AO）、正常肾动脉（NRA）、肾动脉狭窄（RAS）和肾内小叶间动脉（IRA）。训练了七种目标检测模型，包括三种两阶段模型（Faster R-CNN、Cascade R-CNN 和 Double Head R-CNN）和四种一阶段模型（RetinaNet、YOLOv3、FoveaBox 和 Deformable DETR），以预测采样位置，并比较它们的预测准确性。与其他方法相比，Double Head R-CNN 模型在参数优化和验证数据集上的平均准确率均显著更高（分别为 89.3±0.6%和 88.5±0.3%）。在临床验证数据中，Double Head R-CNN 模型对所有四种类型图像的预测准确率均显著高于其他方法。DL 目标检测模型有望帮助经验不足的医生提高肾动脉超声检查中采样位置选择的准确性。
