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Convolutional neural network based on T-SPOT.TB assay promoting the discrimination between active tuberculosis and latent tuberculosis infection

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收录情况： ◇ SCIE

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单位： [1]Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China [2]Department of Immunology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China [3]Department of Epidemiology and Biostatistics, Key Laboratory of Environmental Health of Ministry of Education, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China [4]Department of Dermatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China [5]Eurofins Consumer Product Testing (Guangzhou) Co. Ltd., Guangzhou, China [6]Telecom Physique Strasbourg, Illkirch-Graffenstaden, France

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关键词： Convolutional neural network T-SPOT Active tuberculosis Latent tuberculosis infection Diagnosis

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The study aims to investigate the potential of convolutional neural network (CNN) based on spot image of T-SPOT assay for distinguishing active tuberculosis (ATB) from latent tuberculosis infection (LTBI).CNN was applied to recognize and classify T-SPOT spot image. Logistic regression was used to establish prediction model based on CNN.Areas under the receiver operating characteristic curve (AUCs) of early secreted antigenic target 6 (ESAT-6) CNN, culture filtrate protein 10 (CFP-10) CNN, and phytohemagglutinin (PHA) CNN were more than 0.7 in differentiating ATB from LTBI, while the performance of these indicators was significantly better than that of spot number. Furthermore, prediction model based on the combination of CNNs yielded an AUC of 0.898. The model presented a sensitivity of 85.76% and a specificity of 90.23%.The current study identified CNN based on T-SPOT spot image with the potential to serve as a tool for TB diagnostics.Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.

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出版当年[2022]版：

大类 | 4 区医学

小类 | 4 区微生物学 4 区传染病学

最新[2025]版：

大类 | 4 区医学

小类 | 4 区传染病学 4 区微生物学

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出版当年[2021]版：

Q3 INFECTIOUS DISEASES Q3 MICROBIOLOGY

最新[2023]版：

Q3 INFECTIOUS DISEASES Q3 MICROBIOLOGY

影响因子： 2.1 最新[2023版] 2 最新五年平均 2.983 出版当年[2021版] 2.61 出版当年五年平均 2.803 出版前一年[2020版] 2.9 出版后一年[2022版]

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第一作者单位： [1]Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

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