基于AI技术的腰椎X射线图像质量控制模型的构建与应用

Construction and application of lumbar X-ray image quality control model based on AI technology

目的 采用深度学习算法建立腰椎X射线摄影图像质量控制模型,通过该模型实时和回顾性评估临床图像。方法选取2018年1月至2021年2月在温州医科大学附属第一医院接受检查的1389例患者,搜集患者的正位、侧位和斜位腰椎X射线摄影图像。采用基于U-Net的全卷积神经网络对腰椎X射线图像中的解剖结构进行分割,利用该分割算法建立一种自动评价模型检测不合格图像。采用Dice相似系数(Dice similarity coefficient,DSC)评价模型性能,并对模型投入应用后的腰椎X射线摄影图像进行统计评价。结果 模型在验证集上的准确性为0.971~0.990(0.98±0.10)、敏感度为0.714~0.933(0.86±0.13)、特异性为0.995~1.000(0.99±0.12)。质控模型在2022年腰椎X射线摄影的优秀率为28.8%,中等率54.8%,不合格率16.4%。结论 基于人工智能的腰椎X射线图像质控模型实现腰椎解剖结构的精准分割,可对图像质量作出准确评价,有利于保证技师对腰椎X射线摄影操作的规范性。

Objective To establish a lumbar radiography image quality control model by using the deep learning algorithm and evaluate clinical images in real time and retrospectively based on the developed model.Methods The anteroposterior,lateral and oblique lumbar radiographs of 1389 patients collected between January 2018 to February 2021 at the The First Affiliated Hospital of Wenzhou Medical University were analyzed.The anatomical structures in the lumbar X-ray images were segmented using a full convolutional neural network based on U-Net,and the segmentation algorithm was utilized to establish an automatic evaluation model to detect substandard images.Dice similarity coefficient(DSC)was used to evaluate the performance of the model,and the lumbar radiography images were statistically evaluated after the application of the model.Results The accuracy of the model on the validation set was 0.971-0.990(0.98±0.10),the sensitivity was 0.714-0.933(0.86±0.13),and the specificity was 0.995-1.000(0.99±0.12).The quality control model had an excellent rate of 28.8%,an intermediate rate of 54.8%,and a failure rate of 16.4%for lumbar spine radiography in 2022.Conclusion The lumbar spine X-ray image quality control model based on artificial intelligence realizes accurate segmentation of lumbar spine anatomical structures and makes accurate evaluation of image quality,which is helpful to ensure the standardization of lumbar spine X-ray radiography operation by technicians.