Objective To evaluate the clinical efficacy of artificial intelligence(AI)-assisted imaging diagnostic trials using multi-reader multi-case(MRMC)design,so as to provide a scientific basis for clinical evaluation of imaging diagnostic trials.Methods The MRMC design,widely used in imaging diagnostic trials,was adopted in this study.The Obuchowski-Rockette(OR)method of MRMC design was detailed,including model construction and test methods.A case study was conducted,collecting imaging data of 200 subjects from 3 hospitals,with 133 cases of rib fractures and 68 cases of non-rib fractures.Three radiologists reviewed all CT images of the subjects.The area under curve(AUC)value,sensitivity and specificity in detecting rib fractures between 2 reading modalities(radiologists with AI assistance vs radiologists reading independently)were compared.Results The AI-assisted reading group had an AUC value of 0.958,while the radiologist-independent reading group had an AUC value of 0.902,showing a significant difference(P＜0.001).The overall sensitivity and specificity of the AI-assisted reading group were 0.970 and 0.946,respectively;while the sensitivity and specificity of the radiologist-independent reading group were 0.838 and 0.966,respectively.The difference of sensitivity between groups was 0.131(95％confidence interval[CI]0.091-0.171),and the difference of specificity was-0.020(95％CI-0.059-0.020),indicating a significant difference in sensitivity but not in specificity between AI-assisted and radiologist-independent reading groups.Both groups had positive likelihood ratios(+LR)greater than 10 and negative likelihood ratios(-LR)less than 0.2,with positive predictive values approaching 1,suggesting that the diagnostic accuracy of the AI-assisted imaging diagnostic trials was high.Conclusion The AI-assisted reading method demonstrates a significant advantage in enhancing diagnostic efficiency,not only improving the diagnostic accuracy and detection rate of rib fractures,but also improving the work efficiency of radiologists and optimizing hospital services.

Objective To estimate the sample size of radiological diagnostic tests in multireader multicase(MRMC)design,and to explore the numbers of readers and cases under 3 different inference situations:random-reader random-case,fixed-reader random-case,and random-reader fixed-case.Methods The images of 114 participants(45 cases diagnosed as aortic coarctation by the gold standard)in the Van Dyke dataset were used,and 5 radiologists read these images under 2 different magnetic resonance imaging(MRI)sequences(Spin-echo and Cine MRI)to obtain the pre-experiment data.Then Obuchowski-Rockette method was used to estimate sample size.Results The mean area under curve(AUC)of aortic coarctation determined by radiologists was 0.941(95%confidence interval[CI]0.899-0.983)with the Spin-echo MRI sequence,and 0.897(95%CI 0.837-0.957)with the Cine MRI sequence.When the effect size was 0.044 and the number of readers was 5,we needed 337 participants for random-reader random-case,162 participants for fixed-reader random-case,and 282 participants for random-reader fixed-case.Conclusion In MRMC design,we need both the number of readers and cases;the larger the number of readers,the smaller the cases required.We need more samples under the situation of random-reader random-case,when the number of readers is≥5.

Objective:To survey the knowledge levels about diagnosis and management of asthma among general practitioners in community health centers in Shanghai and to analyze influencing factors.Methods:A questionnaire survey was conducted from May to June 2022 among general practitioners from 80 community health care centers in Shanghai. The questionnaire contained the basic information of the responders; knowledge about the diagnosis of asthma (symptoms, diagnostic methods, interpretation of bronchodilation and fractional exhaled nitric oxide, FeNO tests); and treatment of asthma (choosing and usage of therapeutic drugs). Logistic regression was used to analyze.Results:A total 324 general practitioners completed the questionnaire survey. Among them 221 (68.21%) had bachelor degree; 200 (61.73%) were attending doctors; the median working duration was 11 years; 174 (53.70%) had training on asthma knowledge in last 3 years. The survey results showed that 55 (16.98%) responders misinterpreted results of bronchodilation test, and 90(5.86%)misinterpreted results of FeNO test for diagnosis of asthma; 244 responders (75.31%) suspected implication of long-term cough for diagnosis of asthma; 277(85.49%)would order bronchodilation text to suspected patients; 273(84.26%)prescribed inhaled corticosteroid plus long-acting beta-2 agonist to asthma patients. For mild asthma patients, 144 responders(44.44%)suggested to use budesonide formoterol as needed; for moderate to severe asthma, 174(53.70%)suggested to use budesonide formoterol regularly and as needed, 100(30.86%)suggested to use salmeterol fluticasone regularly and salbutamol as needed. Logistic analysis showed that longer working duration was correlated with higher misinterpretation rate of bronchodilation and FeNO tests( OR=0.798,95% CI:0.694-0.918, P=0.002; OR=0.859,95% CI:0.739-0.998, P=0.047). The accuracy rate of all the questions was significantly higher in general practitioners who had training on asthma knowledge than those who didn′t receive training in last 3 years(all P<0.05). Conclusions:Most general practitioners in community health service centers in Shanghai have relatively high levels of knowledge about diagnosis and treatment of asthma. Years of work experience and training experience can affect the levels of asthma knowledge among community general practitioners.