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Chinese Journal of Medical Ultrasound (Electronic Edition) ›› 2022, Vol. 19 ›› Issue (07): 649-655. doi: 10.3877/cma.j.issn.1672-6448.2022.07.010

• Ultrasound Quality Control • Previous Articles     Next Articles

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

Ying Tan1, Huaxuan Wen1, Guiyan Peng1, Dandan Luo1, Xin Wen1, Yao Jiang1, Wenlan Huang1, Shengli Li1,()   

  1. 1. Department of Ultrasound, Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, the First School of Clinical Medicine, Southern Medical University, Shenzhen 518028, China
  • Received:2021-12-14 Online:2022-07-01 Published:2022-07-29
  • Contact: Shengli Li

Abstract:

Objective

To assess the clinical value of online artificial intelligent quality control system in assessing obstetric ultrasound images.

Methods

A 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.

Results

The 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].

Conclusion

The 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.

Key words: Artificial intelligence, Obstetrics, Ultrasound, Quality control

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