Objective:To investigate the clinical diagnostic significance of peripheral blood T-cell test(T-spot test)for tuberculosis(TB)infection combined with erythrocyte sedimentation rate(ESR)in pulmonary TB.Methods:41 patie...Objective:To investigate the clinical diagnostic significance of peripheral blood T-cell test(T-spot test)for tuberculosis(TB)infection combined with erythrocyte sedimentation rate(ESR)in pulmonary TB.Methods:41 patients with a clinical diagnosis of TB during hospitalization from January 2020 to April 2023 in our hospital were selected as the experimental group,and 45 patients without TB(bronchopneumonia patients)were selected as the control group.The diagnostic specificity,sensitivity,and accuracy of the T-spot TB test,ESR test,and the combined test of the two were calculated respectively.Results:The sensitivity,specificity,and accuracy of the T-spot TB test combined with ESR for the diagnosis of TB in the experimental group were significantly higher than the individual results of the T-spot TB test and ESR test alone(P<0.05).Conclusion:The T-spot TB test combined with the ESR test for TB diagnosis has greater clinical value than carrying out the tests individually.展开更多
The COVID-19 pandemic continues to significantly impact people's lives worldwide, emphasizing the critical need for effective detection methods. Many existing deep learning-based approaches for COVID-19 detection ...The COVID-19 pandemic continues to significantly impact people's lives worldwide, emphasizing the critical need for effective detection methods. Many existing deep learning-based approaches for COVID-19 detection offer high accuracy but demand substantial computing resources, time, and energy. In this study, we introduce an optical diffractive neural network(ODNN-COVID), which is characterized by low power consumption, efficient parallelization, and fast computing speed for COVID-19 detection. In addition, we explore how the physical parameters of ODNN-COVID affect its diagnostic performance. We identify the F number as a key parameter for evaluating the overall detection capabilities. Through an assessment of the connectivity of the diffractive network, we established an optimized range of F number, offering guidance for constructing optical diffractive neural networks. In the numerical simulations, a three-layer system achieves an impressive overall accuracy of 92.64% and 88.89% in binary-and threeclassification diagnostic tasks. For a single-layer system, the simulation accuracy of 84.17% and the experimental accuracy of 80.83% can be obtained with the same configuration for the binary-classification task, and the simulation accuracy is 80.19% and the experimental accuracy is 74.44% for the three-classification task. Both simulations and experiments validate that the proposed optical diffractive neural network serves as a passive optical processor for effective COVID-19 diagnosis, featuring low power consumption, high parallelization, and fast computing capabilities. Furthermore, ODNN-COVID exhibits versatility, making it adaptable to various image analysis and object classification tasks related to medical fields owing to its general architecture.展开更多
文摘Objective:To investigate the clinical diagnostic significance of peripheral blood T-cell test(T-spot test)for tuberculosis(TB)infection combined with erythrocyte sedimentation rate(ESR)in pulmonary TB.Methods:41 patients with a clinical diagnosis of TB during hospitalization from January 2020 to April 2023 in our hospital were selected as the experimental group,and 45 patients without TB(bronchopneumonia patients)were selected as the control group.The diagnostic specificity,sensitivity,and accuracy of the T-spot TB test,ESR test,and the combined test of the two were calculated respectively.Results:The sensitivity,specificity,and accuracy of the T-spot TB test combined with ESR for the diagnosis of TB in the experimental group were significantly higher than the individual results of the T-spot TB test and ESR test alone(P<0.05).Conclusion:The T-spot TB test combined with the ESR test for TB diagnosis has greater clinical value than carrying out the tests individually.
基金National Natural Science Foundation of China(12274092)Natural Science Foundation of Shanghai Municipality (21ZR1405200)。
文摘The COVID-19 pandemic continues to significantly impact people's lives worldwide, emphasizing the critical need for effective detection methods. Many existing deep learning-based approaches for COVID-19 detection offer high accuracy but demand substantial computing resources, time, and energy. In this study, we introduce an optical diffractive neural network(ODNN-COVID), which is characterized by low power consumption, efficient parallelization, and fast computing speed for COVID-19 detection. In addition, we explore how the physical parameters of ODNN-COVID affect its diagnostic performance. We identify the F number as a key parameter for evaluating the overall detection capabilities. Through an assessment of the connectivity of the diffractive network, we established an optimized range of F number, offering guidance for constructing optical diffractive neural networks. In the numerical simulations, a three-layer system achieves an impressive overall accuracy of 92.64% and 88.89% in binary-and threeclassification diagnostic tasks. For a single-layer system, the simulation accuracy of 84.17% and the experimental accuracy of 80.83% can be obtained with the same configuration for the binary-classification task, and the simulation accuracy is 80.19% and the experimental accuracy is 74.44% for the three-classification task. Both simulations and experiments validate that the proposed optical diffractive neural network serves as a passive optical processor for effective COVID-19 diagnosis, featuring low power consumption, high parallelization, and fast computing capabilities. Furthermore, ODNN-COVID exhibits versatility, making it adaptable to various image analysis and object classification tasks related to medical fields owing to its general architecture.
