Abstract：Objective To construct an artificial intelligence automatic quality control system based on the application of artificial
intelligence image recognition technology in medical image field. Methods Using U-Net framework and VGG16 architecture to
complete the semantic analysis of digital radiography (DR), combined with the established artificial intelligence film evaluation
quality control standards, artificial intelligence automatic quality control system was constructed, and the application test was carried
out. Results The prediction results of U-Net were extremely similar to those of manually labeled areas (96.73% of lung field,
98.02% of scapula, 98.71% of clavicle). The final test accuracy of VGG16 architecture for foreign object resolution was 87.58%.
In the 300 data of centralized quality control evaluation, quality management team (232 of first-level tablets, 62 of second-level
tablets, 6 of third-level tablets and 0 of rejected tablets), artificial intelligence quality control system (228 of first-level tablets, 67 of
second-level tablets, 5 of third-level tablets and 0 of rejected tablets), and according to the score results, the coefficient of consistency
between machine quality control and manual quality control showed strong consistency (Kappa=0.901, P<0.001). Conclusion The
artificial intelligence automatic quality control system model of chest position-image can realize automatic intelligent quality control
of image efficiently, accurately and objectively, and the front-end quality control improves the quality of the shot. And centralized
quality control can complete image quality control work in batches and save personnel cost.
胡君花，辛小燕，唐堂，于芷轩，胡安宁. 胸部正位片人工智能自动质控系统
模型的研究与应用[J]. 中国医疗设备, 2023, 38(12): 63-68.
HU Junhua, XIN Xiaoyan, TANG Tang, YU Zhixuan, HU Anning. Research and Application of Artificial Intelligence Automatic Auality Control System Model
for Chest Orthography. China Medical Devices, 2023, 38(12): 63-68.
