在线产科超声图像智能质量控制系统的临床应用价值

目的探讨产科超声图像智能质量控制系统的临床应用价值。 方法选取2021年1月1日至6月30日深圳市及重庆市137家医院998位医师的15 640个产科超声病例共374 191张图像，应用在线人工智能质量控制系统对图像进行质量评估。基于每张图像的智能质量控制结果，统计所有切面标准率、基本标准率及非标准率；统计申诉结果以观察系统检测的正确率；应用配对样本t检验比较智能质量控制与人工质量控制所耗费时间的差异，评估智能质量控制系统的工作效率。 结果图像总体标准率为81.18%，基本标准率为12.06%，非标准率为6.76%；医师申诉图像285张（0.076%），经权威专家审核后，126张（44.21%）图像维持人工智能判断，159张（55.79%）图像经专家修改，系统检测正确率达99.96%（374 032/374 191）。智能质量控制每100张图像平均耗时（32.7±5.1）s，比2位医师人工质量控制耗时［（705.3±37.2）s、（724.6±40.4）s］明显降低，差异具有统计学意义（t=62.667、56.396，P均＜0.001）。 结论产科超声图像智能质量控制系统能客观、准确、高效地完成图像智能质量控制，对指导图像质量的提高有重大意义。

Clinical value of online artificial intelligent quality control system in assessing obstetric ultrasound images

ObjectiveTo assess the clinical value of online artificial intelligent quality control system in assessing obstetric ultrasound images. MethodsA total of 374 191 images of 15 640 obstetric ultrasound cases from 998 doctors in 137 hospitals in Shenzhen and Chongqing were selected from January 1 to June 30, 2021, and the quality of the images was evaluated using online artificial intelligent quality control system. Based on each image quality assessment result, the proportion of different standard levels of all planes was calculated. The appeal results were also recorded to observe the accuracy of the system. To survey the efficiency of the system, paired sample t test was used to compare the time spent by intelligent quality control and manual quality control. ResultsThe overall standard rate, substandard rate, and nonstandard rate were 81.18%, 12.06%, and 6.76%, respectively. A total of 285 appealed images (0.076%) were reviewed by authoritative experts, who confirmed the initial diagnosis in 126 images (44.21%), and did not support the initial diagnosis in 159 images (55.79%); the accuracy of the system reached 99.96% (374 032/374 191). The average time spent by intelligent quality control for 100 images was (32.7±5.1) s, significantly shorter than that spent by manual quality control by two ultrasound physicians [(705.3±37.2) s and (724.6±40.4) s, t=62.667 and 56.396, respectively, P＜0.001]. ConclusionThe intelligent quality control system of obstetric ultrasound images allows the quality control to be performed objectively, accurately, and efficiently, which is of great significance to guide the improvement of image quality.