Borisov A A, Semenov S S, Kirpichev Yu S, Arzamasov K M, Omelyanskaya O V, Vladzymyrskyy A V, Vasilev Yu A
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow, Russia.
Pirogov Russian National Research Medical University, Moscow, Russia.
Int J Comput Assist Radiol Surg. 2025 Jun 18. doi: 10.1007/s11548-025-03468-0.
During radiography, irregularities occur, leading to decrease in the diagnostic value of the images obtained. The purpose of this work was to develop a system for automated quality assurance of patient positioning in chest radiographs, with detection of suboptimal contrast, brightness, and metadata errors.
The quality assurance system was trained and tested using more than 69,000 X-rays of the chest and other anatomical areas from the Unified Radiological Information Service (URIS) and several open datasets. Our dataset included studies regardless of a patient's gender and race, while the sole exclusion criterion being age below 18 years. A training dataset of radiographs labeled by expert radiologists was used to train an ensemble of modified deep convolutional neural networks architectures ResNet152V2 and VGG19 to identify various quality deficiencies. Model performance was accessed using area under the receiver operating characteristic curve (ROC-AUC), precision, recall, F1-score, and accuracy metrics.
Seven neural network models were trained to classify radiographs by the following quality deficiencies: failure to capture the target anatomic region, chest rotation, suboptimal brightness, incorrect anatomical area, projection errors, and improper photometric interpretation. All metrics for each model exceed 95%, indicating high predictive value. All models were combined into a unified system for evaluating radiograph quality. The processing time per image is approximately 3 s.
The system supports multiple use cases: integration into an automated radiographic workstations, external quality assurance system for radiology departments, acquisition quality audits for municipal health systems, and routing of studies to diagnostic AI models.
在放射成像过程中，会出现不规则情况，导致所获取图像的诊断价值降低。本研究的目的是开发一种用于胸部X光片患者体位自动质量保证的系统，能够检测对比度欠佳、亮度欠佳以及元数据错误。
使用来自统一放射信息服务（URIS）和几个开放数据集的69000多张胸部及其他解剖区域的X光片对质量保证系统进行训练和测试。我们的数据集包括了各种研究，不论患者的性别和种族，唯一的排除标准是年龄低于18岁。由专家放射科医生标注的X光片训练数据集用于训练经过修改的深度卷积神经网络架构ResNet152V2和VGG19的集成模型，以识别各种质量缺陷。使用接收器操作特征曲线下面积（ROC-AUC）、精确率、召回率、F1分数和准确率指标来评估模型性能。
训练了七个神经网络模型，以根据以下质量缺陷对X光片进行分类：未能捕捉到目标解剖区域、胸部旋转、亮度欠佳、解剖区域错误、投影误差和光度解释不当。每个模型的所有指标均超过95%，表明具有较高的预测价值。所有模型被整合到一个统一的系统中，用于评估X光片质量。每张图像的处理时间约为3秒。
该系统支持多种用例：集成到自动放射成像工作站、放射科外部质量保证系统、市政卫生系统的采集质量审核以及将研究结果路由到诊断人工智能模型。
