From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Li, Lin-Sheng; Yang, Ling; Zhuang, Li; Ye, Zhao-Yang; Zhao, Wei-Guo; Gong, Wen-Ping

doi:10.1186/s40779-023-00490-8

Military Medical Research

Table 3 List of studies on ML methods based on transcriptomics technology in the differential diagnosis of LTBI and ATB

From: From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Study	Sample size	Countries	Methods^ψ	Biomarkers	ML methods	Sensitivity	Specificity	Accuracy	AUC
Lee et al. [238]	TB (n = 15), LTBI (n = 17), HCs (n = 15)	Taiwan, China	Microarray	PTPRC + ASUN + DHX29 + NEMF	Decision tree, random forest, support vector machine, Bayesian best	97.9% with PTPRC + ASUN + DHX29 under the Bayesian model	Unknown	97.8%	0.979
Lu et al. [239]	Discovery cohort, TB (n = 4), LTBI (n = 4), HCs (n = 4); qPCR validation cohort, TB (n = 25), LTBI (n = 36), HCs (n = 22); additional validation cohort, TB (n = 17), LTBI (n = 19)	China	Microarray	CXCL10, ATP10A, and TLR6 combination	Decision trees and unsupervised cluster analysis	71% in the additional validation cohort	89% in additional validation cohort	Unknown	Unknown
Wang et al. [240]	Identification cohort, ATB (n = 28), LTBI (n = 25), HCs (n = 31); validation cohort, ATB (n = 51), LTBI (n = 44), HCs (n = 35)	China	RNA-seq	TNFRSF10C, IFNG, PGM5, EBF3, A2ML1	Decision trees and unsupervised cluster analysis	86.2% with a combination of TNFRSF10C, EBF3, and A2ML1	94.9%	87.8%	Unknown
Maertzdorf et al. [241]	ATB patients (n = 120), LTBI (n = 60), HCs (n = 20); external cohorts, from the Gambia (n = 75), from the Uganda (n = 62)	Africa	RT-PCR	GBP1, IFITM3, P2RY14, and ID3	Random forest, decision tree	Using a cutoff of 0.8, Uganda: 73%, Gambia: 85%; using a cutoff of 0.6, Uganda: 87%, Gambia: 88%	Using a cutoff of 0.8, Uganda: 78%, Gambia: 76%; using a cutoff of 0.6, Uganda: 75%, Gambia: 68%	82% in Uganda and 89% in Gambia	AUC = 0.89 in Gambia and AUC = 0.82 in Uganda
Bayaa et al. [242]	ATB patients (n = 141), LTBI (n = 26), HCs (n = 71)	Multiple countries	qRT-PCR	RISK6	No	90.9%	88.5%	Unknown	0.930
Gong et al. [243]	ATB patients (n = 51), lung cancer (n = 30), INFLA (n = 30), HCs (n = 15)	China	qRT-PCR	SERPING1, BATF2, UBE2L6, and VAMP5	No	88%	78%	Unknown	0.840

^ψMethods used for screening and identification of biomarkers
A2ML1 alpha-2-macroglobulin like protein 1, ASUN asunder spermatogenesis regulator, ATB active tuberculosis, ATP10A ATPase phospholipid transporting 10A, AUC area under curve, BATF2 basic leucine zipper transcription factor, CXCL10 chemokine (C-X-C motif) ligand 10, DHX29 DEAH (Asp-Glu-Ala-His) box polypeptide 29, EBF3 early B cell factor 3, GBP1 guanylate binding protein 1, HCs health controls, ID3 inhibitor of DNA binding 3, IFITM3 interferon-induced transmembrane protein 3, IFNG interferon-γ gene, INFLA patients with pneumonia, LTBI latent tuberculosis infection, ML machine learning, NEMF nuclear export mediator factor, P2RY14 UDP-glucose-specific G(i) protein-coupled P2Y receptor, RT-PCR reverse transcription-polymerase chain reaction, PGM5 phosphoglucomutase 5, PTPRC protein tyrosine phosphatase receptor type C, qRT-PCR quantitative real-time PCR, RNA-seq RNA-sequencing, RISK6 six whole blood gene transcriptomic signature, SERPING1 serpin peptidase inhibitor C1 inhibitor member 1, TNFRSF10C TNF receptor superfamily member 10C, TLR Toll-like receptor, TB tuberculosis, UBE2L6 ubiquitin-conjugating enzyme E2L6, VAMP5 vesicle-associated membrane protein 5

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ISSN: 2054-9369

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