1.Artificial intelligence knowledge graph and image classification for quality control of chest posterior-anterior position X-ray radiograph
Qian WANG ; Liangliang SONG ; Xiao HAN ; Ming LIU ; Biao ZHANG ; Shibo ZHAO ; Zongyun GU ; Lili HUANG ; Chuanfu LI ; Xiaohu LI ; Yongqiang YU
Chinese Journal of Medical Imaging Technology 2024;40(6):922-927
Objective To observe the value of artificial intelligence(AI)knowledge graph and image classification for quality control(QC)of chest posterior-anterior position X-ray radiograph(abbreviated as chest film).Methods Totally 9 236 chest films from 595 medical institutions in Anhui province imaging cloud platform were retrospectively enrolled.QC knowledge graph containing 21 classification labels were constructed.Firstly,QC of chest films based on the above knowledge graph were performed by 10 technicians for 2 rounds of single person and 1 round of multi person,and the results were recorded as A,B and C,respectively.Then AI algorithms were used to classify and evaluate based on knowledge graph,and the result was recorded as D.Finally,a QC expert reviewed results C and D to determine the final QC results and taken those as references to analyze the efficiency of the above 4 QC.Results The area under the curve(AUC)of AI algorithm for QC of chest films were all ≥0.780,with an average value of 0.939.The average precision of QC for chest films of A,B,C and D was 81.15％,85.47％,91.65％and 92.21％,respectively.Conclusion AI knowledge graph and image classification technology could be effectively used for QC of chest posterior-anterior position X-ray radiograph.
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