Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 2324 2022-11-25 01:52:43 |
2 Reference format revised. + 6 word(s) 2330 2022-11-25 03:16:20 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Huang, Y.;  Ai, L.;  Wang, X.;  Sun, Z.;  Wang, F. Diagnosis of Tuberculosis. Encyclopedia. Available online: (accessed on 25 June 2024).
Huang Y,  Ai L,  Wang X,  Sun Z,  Wang F. Diagnosis of Tuberculosis. Encyclopedia. Available at: Accessed June 25, 2024.
Huang, Yi, Lin Ai, Xiaochen Wang, Ziyong Sun, Feng Wang. "Diagnosis of Tuberculosis" Encyclopedia, (accessed June 25, 2024).
Huang, Y.,  Ai, L.,  Wang, X.,  Sun, Z., & Wang, F. (2022, November 25). Diagnosis of Tuberculosis. In Encyclopedia.
Huang, Yi, et al. "Diagnosis of Tuberculosis." Encyclopedia. Web. 25 November, 2022.
Diagnosis of Tuberculosis

Mycobacterium tuberculosis (Mtb), one of the most successful pathogens in the world, has co-existed with humans for one thousand years and remains a major public health threat, causing over 2 million deaths annually. An early diagnosis and effective treatment are the keys to controlling TB. Methods based on the detection of Mycobacterium tuberculosis (Mtb) are insufficiently sensitive, methods based on the detection of Mtb-specific immune responses cannot always differentiate active disease from latent infection, and some of the serological markers of infection with Mtb are insufficiently specific to differentiate tuberculosis from other inflammatory diseases. New tools based on technologies such as flow cytometry, mass spectrometry, high-throughput sequencing, and artificial intelligence have the potential to solve this dilemma. 

tuberculosis diagnosis Mtb

1. Introduction

Tuberculosis (TB) is a leading global public health problem, with high morbidity and mortality in humans. Until the COVID-19 pandemic, TB was still the leading cause of death from a single infectious agent, ranking above HIV/acquired immune deficiency syndrome [1]. The number of people newly diagnosed with TB fell from 7.1 million in 2019 to 5.8 million in 2020, and reduced access to TB diagnosis and treatment has resulted in an increase in TB deaths [1]. Generally, although progress has been made in reducing the TB burden worldwide, this has been insufficient to reach the first milestone of the End TB Strategy.
Drug-resistant tuberculosis is another threat to the global control of the disease. The emergence of multi-drug resistant tuberculosis (MDR-TB) and extensively drug resistant tuberculosis (XDR-TB) is increasing in some regions of the world. Globally in 2020, 71% (2.1/3.0 million) of people diagnosed with bacteriologically confirmed pulmonary TB were tested for rifampicin resistance, with considerable variation among countries [1]. Among them, 132,222 cases of MDR-TB and 25,681 cases of pre-XDR-TB or XDR-TB were detected [1]. The increase of drug-resistant TB demands accurate and reproducible drug susceptibility testing (DST) methods.

2. Mycobacterium tuberculosis (Mtb) Diagnosis

2.1. Microscopy

Sputum smear microscopy still remains one of the basic methods for identifying Mtb in developing countries. The most regular practice is acid-fast staining using carbol fuschin solution. The lipid-rich cell wall of mycobacterium tuberculosis (Mtb) resists decolorization with acid-containing reagents, which means that acid-fast organisms can be visualized on microscopic examination of smears prepared from sputum, alveolar lavage fluid, or other specimens. The major limitation of smear microscopy is a lack of sensitivity, which varies widely (20 to 80%) in different studies and is particularly poor in paucibacillary TB, including child TB, extrapulmonary TB, or HIV coinfected TB [2][3][4][5][6][7]. Specificity is likely to vary considerably depending on the local prevalence of infections with nontuberculous mycobacteria (NTM). In regions with a low incidence of NTM, the specificity of smear microscopy can reach up to 98% [8].
Fluorescence microscopy can save manpower and improve work efficiency [2], as well as increasing the sensitivity of smear microscopy [9]. The possibility of false-positive results is a potential shortcoming of fluorescence microscopy, because of non-specific fluorochrome dye incorporation [10]. The instability of fluorescent staining has been reported [11]. Unlike conventional microscopy using conventional artificial light, fluorescence microscopy uses an intense light source, such as a halogen or high-pressure mercury vapor lamp, which is expensive and vulnerable [12]. However, in comparison to intense light sources, light-emitting diodes (LEDs) are more robust, sustainable, and have a longer lasting battery life expectancy, and these qualities make LED microscopy feasible for use in resource-limited settings. Accordingly, the WHO recommends that conventional fluorescence microscopy can be replaced by LED microscopy [9].
Fluorescein diacetate (FDA) is a new stain solution where only living cells actively convert the nonfluorescent FDA into the green fluorescent compound following enzymatic activity [13]. FDA staining can be used to detect the viable Mtb and simply predict the quantitative culture results within 1 h, indicating whether patients are responding to TB therapy [14]. “TBDx” is an innovative smear microscopy system that automatically loads slides onto a microscope, focuses and digitally captures images, and then classifies smears as positive or negative using computerized algorithms [15]. Despite showing potential for detection of Mtb, these new microscopy methods still need more validation of their performance in clinical practice.

2.2. Culture

2.2.1. Solid and Liquid Culture

Culture is still the WHO-recommended gold standard for the diagnosis of TB, as Mtb isolation is not only important for disease diagnosis but also permits the detection of drug resistances. Traditional Mtb culture can be performed on either a solid (e.g., Lowenstein–Jensen) or liquid (e.g., Middlebrook 7H9) medium. Notably, solid culture is less expensive than liquid culture and less prone to contamination by other bacteria or fungi, but liquid culture is faster, more sensitive, and convenient (growth is detected automatically) [16][17].

2.2.2. Rapid Identification from Positive Cultures

Rapid identification assays capable of distinguishing between Mtb complex and NTM after positive cultures are the basis for initiating early anti-TB therapy. Traditional biochemical assays are slow and have a long turnaround time (2–3 weeks) [18][19]. Mtb protein 64 (MPT-64) is one of the Mtb-specific antigens secreted during bacterial growth. Immunochromatographic (ICT) assays are based on the principle of a double-sandwich enzyme-linked immunosorbent assay, which detects MPT-64 antigen.

2.2.3. Phenotypic Tests for DST

Testing on solid agar using the proportion method is still regarded as the reference standard method for DST of Mtb, which is performed by counting the number of Mtb colonies that grow on agar with or without antibiotics. The absolute concentration method is based on the comparison of growth intensity in the presence of cutoff concentrations and on drugfree controls. Commercial automated liquid culture systems (e.g., the mycobacteria growth indicator tube system) use a modification of the proportion method and offer reliable results for two important first-line drugs (isoniazid (INH) and rifampin (RIF)), while the testing for resistance to second-line drugs is less reliable and reproducible [8][20].

2.3. Molecular Tests

2.3.1. Xpert MTB/RIF

Xpert MTB/RIF (Cepheid), an automated molecular test for detection of Mtb and RIF resistance directly from clinical specimens, is one of the most commonly used molecular tests for diagnosis of TB worldwide. It is based on a hemi-nested real-time polymerase-chain-reaction (PCR) assay to amplify an Mtb-specific sequence of the rpoB gene. The turnaround time of this assay is short (2–3 h), and the problem of cross-contamination is eliminated because of self-contained cartridges [21]. It uses three specific primers and five unique molecular probes to ensure a high degree of specificity, and NTM does not confound testing [22].
The sensitivity of Xpert MTB/RIF is 99.8% for smear- and culture-positive cases and 90.2% for smear-negative but culture-positive cases, and the estimated specificity is 99.2% for a single direct MTB/RIF test [23]. As compared with phenotypic DST, the MTB/RIF test correctly identifies RIF resistance with 97.6% sensitivity and 98.1% specificity [23]. Notably, there is some proportion of Xpert MTB/RIF positive results in culture-negative cases, which still should be diagnosed as TB because of the high specificity of this assay [24][25][26][27].
Xpert Ultra, the next generation of Xpert MTB/RIF, has a larger chamber for DNA amplification than Xpert MTB/RIF. This new assay has two multicopy amplification targets for TB, namely, IS6110 and IS1081, and therefore allows a lower detection limit than Xpert MTB/RIF [28]. These modifications have increased Ultra’s overall sensitivity from 83% to 88%, with a slight decrease in specificity from 98% to 96% [29].

2.3.2. Loop-Mediated Isothermal Amplification (LAMP)

LAMP is an isothermal PCR amplification technique, and the reaction process proceeds at a constant temperature using an auto-cycling strand displacement reaction targeted at the six regions of the gyrB and 16S rRNA genes [30]. The sensitivity of TB-LAMP is slightly lower than that of Xpert MTB/RIF, while the specificity of the two methods is comparable [31]. Nevertheless, TB-LAMP does not require sophisticated laboratory equipment and can be performed in peripheral settings, contributing to its use as a simple, rapid, specific, and cost-effective nucleic acid amplification method [30]. Currently, the TB-LAMP assay is recommended by the WHO as a potential replacement for smear microscopy, due to its superior diagnostic performance [32].

2.3.3. Line Probe Assay (LPA)

LPA detects TB DNA and genetic mutations associated with drug resistance, after DNA extraction and PCR amplification. The basis of the LPA is that the pre-labeled amplification product is captured by the DNA probe solidified on the membrane strip and detected by colorimetry, and the results of LPA appear as a linear band [33]. LPA can detect drug resistance to first-line TB drugs (INH and RIF), and there are different versions of commercial products, including GenoType MTBDRplus 1.0 (Hain Lifescience) and INNO-LiPA Rif TB kit (Innogenetics) [33][34]. The newer generation of LPA, GenoType MTBDRplus 2.0, is more sensitive for the detection of Mtb strains from smear-positive and smear-negative specimens [34]. Genotype MTBDRsl (Hain Lifescience) can detect mutations associated with fluoroquinolones and second-line drugs such as kanamycin, amikacin, and capreomycin [35][36].

2.3.4. Micro Real-Time PCR

Truenat MTB, Truenat MTB Plus, and Truenat MTB-Rif Dx (Molbio Diagnostics) are micro real-time PCR-based assays for Mtb detection that produce results in 1 h [37][38]. Truenat MTB and Truenat MTB Plus detect Mtb bacilli in sputum after DNA extraction, and Truenat MTB-Rif Dx has an optional add-on chip for sequential RIF resistance detection [38]. In 2019, the WHO reported that the Truenat MTB series displayed comparable sensitivities and specificities with Xpert MTB/RIF and Xpert MTB/RIF Ultra for the detection of TB and RIF resistance [39].

3. Immunological Diagnosis

3.1. Antibody Detection

Serologic tests rely on antibody recognition of Mtb antigens by the humoral immune response. Owing to the poor diagnostic sensitivity and specificity, the WHO does not recommend any commercial serologic assays for the diagnosis of TB, in case of misdiagnosis and resource waste [40].

3.2. Antigen Detection

The presence of circulating Mtb antigens can be detected from clinical specimens such as sputum, serum, and urine, based on the principle of sandwich enzyme-linked immunosorbent assay. Lipoarabinomannan (LAM) is a specific component of the cell envelope of Mtb and can be a potential biomarker for TB diagnosis [41].

3.3. Tuberculin Skin Testing (TST)

TST is a classical method based on detection of type IV hypersensitivity using purified protein derivative (PPD) of tuberculin. Mtb-infected patients can produce sensitized T lymphocytes with the ability to recognize Mtb antigens. When the sensitized T lymphocytes are stimulated by Mtb antigens again, a variety of soluble lymphokines are released to increase the vascular permeability, local redness, swelling, and induration [42][43]. The average diameter of induration is measured after 72 h of PPD injection as the results of TST. An average diameter of induration <5 mm or no response is considered as negative; ≥5 mm is considered as positive [44].

3.4. Interferon-Gamma (IFN-γ) Release Assays (IGRAs)

IGRAs are based on secretion of IFN-γ by lymphocytes exposed to Mtb-specific antigens (TBAg), such as early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) [45]. IGRA results are not affected by previous BCG vaccination and most infections caused by NTM, leading to a higher specificity than TST in detection of Mtb infection [45]. The two most common commercially available IGRAs are T-SPOT.TB (T-SPOT; Oxford Immunotec) and Quanti FERON-TB (QFT; Qiagen) [46][47].

3.4.1. T-SPOT

T-SPOT assay, based on the enzyme-linked immunospot (ELISPOT) method, detects the number of IFN-γ-producing cells after Mtb-specific antigen stimulation. Currently, T-SPOT assay has been widely used for the diagnosis of Mtb infection [48]. T-SPOT has proven useful, not only in detecting Mtb infection in children and HIV patients [49][50], but also in the assessment of risk for TB development in chronic inflammatory diseases, prior to anti-TNF treatment and screening for latent tuberculosis infection (LTBI) in immigrant groups, health care workers, and college students [51][52][53]. T-SPOT has also been reported to be a useful adjunct test for diagnosing extrapulmonary TB [54]. In spite of the significance of the T-SPOT assay in diagnosing Mtb infection, the most critical limitation of this assay is its inability to distinguish active TB from LTBI [55][56][57]. Thus, this limitation led a WHO expert group to discourage the use of T-SPOT for the diagnosis of active pulmonary TB in low- and middle-income countries, because of an unsatisfactory specificity [58].

3.4.2. QFT

A QFT assay is based on the enzyme linked immunosorbent assay to detect IFN-γ secreted into the supernatant of culture medium after Mtb-specific antigen stimulation. The advantages and limitations of QFT are similar to those of T-SPOT. The operational procedures of QFT are simpler than T-SPOT, as it does not require the isolation of peripheral blood mononuclear cells, but instead uses whole blood cells. However, the sensitivity of QFT for detecting Mtb infection is slightly lower than that of T-SPOT, and this is more pronounced in patients with immunocompromised conditions [59].
The fourth-generation QuantiFERON-TB Plus (QFT-Plus) assay, including another TB antigen tube that contains additional shorter peptides from ESAT-6 and CFP-10, is designed to detect both the CD4+ and CD8+ T cell responses [60]. It was developed with the hope of improving the detection of LTBI among immunocompromised hosts. However, studies comparing QFT-Plus to QFT currently do not support the superior performance of QFT-Plus in individuals with either active TB or LTBI [60][61][62][63].

4. New Techniques

4.1. Next-Generation Sequencing (NGS)

Next-generation sequencing is considered a promising method for performing DST of TB and it produces results much faster than phenotypic culture-based testing [64][65]. Unlike probe-based assays that are limited to probe-specific targets, NGS can provide detailed and accurate sequence information for whole genomes by using whole-genome sequencing or multiple gene region sequencing. The WHO has published guidance on the role of NGS technologies for detecting mutations associated with drug resistance in Mtb complex [66].

4.2. Mass Spectrometry

Routine matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) has proven to be useful for the identification of mycobacteria by direct analysis of deposits of a colony on MALDI-TOF-MS target [67][68]. Previous studies have shown that MALDI-TOF-MS is a fast and economical way to identify both Mtb complex and NTM species [69][70]. When an identification score of 1.3 is used, the positive predictive value of the identification of mycobacteria can reach up to 100% [71]. Given that MALDI-TOF-MS can provide results within a few hours and is faster than sequencing and hybridization-based techniques, it has potential as a rapid and reproducible method for the identification and typing of mycobacterium species.

4.3. Artificial Intelligence (AI)

One of the main uses of AI in TB is using machine learning to automate the diagnosis of disease. The common strategy is to establish expert systems using a machine learning method based on the clinical, radiological, and laboratory data of TB patients. Interestingly, machine learning has been reported to aid clinicians in diagnosing pulmonary TB or predicting drug-resistant TB [72][73][74][75][76][77][78][79].


  1. World Health Organization. Global Tuberculosis Report 2021. Available online: (accessed on 22 September 2022).
  2. Steingart, K.R.; Henry, M.; Ng, V.; Hopewell, P.C.; Ramsay, A.; Cunningham, J.; Urbanczik, R.; Perkins, M.; Aziz, M.A.; Pai, M. Fluorescence versus conventional sputum smear microscopy for tuberculosis: A systematic review. Lancet Infect. Dis. 2006, 6, 570–581.
  3. Steingart, K.R.; Ramsay, A.; Pai, M. Optimizing sputum smear microscopy for the diagnosis of pulmonary tuberculosis. Expert Rev. Anti Infect. Ther. 2007, 5, 327–331.
  4. Shingadia, D.; Novelli, V. Diagnosis and treatment of tuberculosis in children. Lancet Infect. Dis. 2003, 3, 624–632.
  5. Elliott, A.M.; Namaambo, K.; Allen, B.W.; Luo, N.; Hayes, R.J.; Pobee, J.O.; McAdam, K.P. Negative sputum smear results in HIV-positive patients with pulmonary tuberculosis in Lusaka, Zambia. Tuber. Lung Dis. 1993, 74, 191–194.
  6. Lombardi, G.; Di Gregori, V.; Girometti, N.; Tadolini, M.; Bisognin, F.; Dal Monte, P. Diagnosis of smear-negative tuberculosis is greatly improved by Xpert MTB/RIF. PLoS ONE 2017, 12, e0176186.
  7. Steingart, K.R.; Ng, V.; Henry, M.; Hopewell, P.C.; Ramsay, A.; Cunningham, J.; Urbanczik, R.; Perkins, M.D.; Aziz, M.A.; Pai, M. Sputum processing methods to improve the sensitivity of smear microscopy for tuberculosis: A systematic review. Lancet Infect. Dis. 2006, 6, 664–674.
  8. Pai, M.; Nicol, M.P.; Boehme, C.C. Tuberculosis Diagnostics: State of the Art and Future Directions. Microbiol. Spectr. 2016, 4, 361–378.
  9. World Health Organization. Fluorescent Light-Emitting Diode (LED) Microscopy for Diagnosis of Tuberculosis: Policy Statement. Available online: (accessed on 22 September 2022).
  10. Boyd, J.C.; Marr, J.J. Decreasing reliability of acid-fast smear techniques for detection of tuberculosis. Ann. Intern. Med. 1975, 82, 489–492.
  11. Minion, J.; Shenai, S.; Vadwai, V.; Tipnis, T.; Greenaway, C.; Menzies, D.; Ramsay, A.; Rodrigues, C.; Pai, M. Fading of auramine-stained mycobacterial smears and implications for external quality assurance. J. Clin. Microbiol. 2011, 49, 2024–2026.
  12. Foulds, J.; O’Brien, R. New tools for the diagnosis of tuberculosis: The perspective of developing countries. Int. J. Tuberc. Lung Dis. 1998, 2, 778–783.
  13. Kanade, S.; Nataraj, G.; Ubale, M.; Mehta, P. Fluorescein diacetate vital staining for detecting viability of acid-fast bacilli in patients on antituberculosis treatment. Int. J. Mycobacteriol. 2016, 5, 294–298.
  14. Datta, S.; Sherman, J.M.; Bravard, M.A.; Valencia, T.; Gilman, R.H.; Evans, C.A. Clinical evaluation of tuberculosis viability microscopy for assessing treatment response. Clin. Infect. Dis. 2015, 60, 1186–1195.
  15. Lewis, J.J.; Chihota, V.N.; van der Meulen, M.; Fourie, P.B.; Fielding, K.L.; Grant, A.D.; Dorman, S.E.; Churchyard, G.J. “Proof-of-concept” evaluation of an automated sputum smear microscopy system for tuberculosis diagnosis. PLoS ONE 2012, 7, e50173.
  16. Rageade, F.; Picot, N.; Blanc-Michaud, A.; Chatellier, S.; Mirande, C.; Fortin, E.; van Belkum, A. Performance of solid and liquid culture media for the detection of Mycobacterium tuberculosis in clinical materials: Meta-analysis of recent studies. Eur. J. Clin. Microbiol. Infect. Dis. 2014, 33, 867–870.
  17. Chien, H.P.; Yu, M.C.; Wu, M.H.; Lin, T.P.; Luh, K.T. Comparison of the BACTEC MGIT 960 with Löwenstein-Jensen medium for recovery of mycobacteria from clinical specimens. Int. J. Tuberc. Lung Dis. 2000, 4, 866–870.
  18. Procop, G.W. Laboratory Diagnosis and Susceptibility Testing for Mycobacterium tuberculosis. Microbiol. Spectr. 2016, 4, 45–58.
  19. Orikiriza, P.; Nyehangane, D.; Atwine, D.; Kisakye, J.J.; Kassaza, K.; Amumpaire, J.M.; Boum, Y., 2nd. Evaluation of the SD Bioline TB Ag MPT64 test for identification of Mycobacterium tuberculosis complex from liquid cultures in Southwestern Uganda. Afr. J. Lab. Med. 2017, 6, 383.
  20. Scarparo, C.; Ricordi, P.; Ruggiero, G.; Piccoli, P. Evaluation of the fully automated BACTEC MGIT 960 system for testing susceptibility of Mycobacterium tuberculosis to pyrazinamide, streptomycin, isoniazid, rifampin, and ethambutol and comparison with the radiometric BACTEC 460TB method. J. Clin. Microbiol. 2004, 42, 1109–1114.
  21. Venter, R.; Derendinger, B.; de Vos, M.; Pillay, S.; Dolby, T.; Simpson, J.; Kitchin, N.; Ruiters, A.; van Helden, P.D.; Warren, R.M.; et al. Mycobacterial genomic DNA from used Xpert MTB/RIF cartridges can be utilised for accurate second-line genotypic drug susceptibility testing and spoligotyping. Sci. Rep. 2017, 7, 14854.
  22. Lawn, S.D.; Nicol, M.P. Xpert® MTB/RIF assay: Development, evaluation and implementation of a new rapid molecular diagnostic for tuberculosis and rifampicin resistance. Future Microbiol. 2011, 6, 1067–1082.
  23. Boehme, C.C.; Nabeta, P.; Hillemann, D.; Nicol, M.P.; Shenai, S.; Krapp, F.; Allen, J.; Tahirli, R.; Blakemore, R.; Rustomjee, R.; et al. Rapid molecular detection of tuberculosis and rifampin resistance. N. Engl. J. Med. 2010, 363, 1005–1015.
  24. Kilaru, S.C.; Chenimilla, N.P.; Syed, U.; Momin, K.; Kilaru, H.; Patil, E.; Nerurkar, V. Role of Xpert MTB/RIF in Bronchoalveolar lavage fluid of sputum-scarce, suspected Pulmonary TB patients. J. Clin. Tuberc. Other Mycobact. Dis. 2019, 14, 7–11.
  25. Barnard, D.A.; Irusen, E.M.; Bruwer, J.W.; Plekker, D.; Whitelaw, A.C.; Deetlefs, J.D.; Koegelenberg, C.F. The utility of Xpert MTB/RIF performed on bronchial washings obtained in patients with suspected pulmonary tuberculosis in a high prevalence setting. BMC Pulm. Med. 2015, 15, 103.
  26. Liu, Q.; Ji, Y.; Martinez, L.; Lu, W.; Shi, X.; Wang, J.; Zeng, Y. Combined tests with Xpert MTB/RIF assay with bronchoalveolar lavage fluid increasing the diagnostic performance of smear-negative pulmonary tuberculosis in Eastern China. Epidemiol. Infect. 2020, 149, e5.
  27. Gowda, N.C.; Ray, A.; Soneja, M.; Khanna, A.; Sinha, S. Evaluation of Xpert(®)Mycobacterium tuberculosis/rifampin in sputum-smear negative and sputum-scarce patients with pulmonary tuberculosis using bronchoalveolar lavage fluid. Lung India Off. Organ Indian Chest Soc. 2018, 35, 295–300.
  28. Chakravorty, S.; Simmons, A.M.; Rowneki, M.; Parmar, H.; Cao, Y.; Ryan, J.; Banada, P.P.; Deshpande, S.; Shenai, S.; Gall, A.; et al. The New Xpert MTB/RIF Ultra: Improving Detection of Mycobacterium tuberculosis and Resistance to Rifampin in an Assay Suitable for Point-of-Care Testing. mBio 2017, 8, e00812-17.
  29. Horne, D.J.; Kohli, M.; Zifodya, J.S.; Schiller, I.; Dendukuri, N.; Tollefson, D.; Schumacher, S.G.; Ochodo, E.A.; Pai, M.; Steingart, K.R. Xpert MTB/RIF and Xpert MTB/RIF Ultra for pulmonary tuberculosis and rifampicin resistance in adults. Cochrane Database Syst. Rev. 2019, 6, Cd009593.
  30. Iwamoto, T.; Sonobe, T.; Hayashi, K. Loop-mediated isothermal amplification for direct detection of Mycobacterium tuberculosis complex, M. avium, and M. intracellulare in sputum samples. J. Clin. Microbiol. 2003, 41, 2616–2622.
  31. Phetsuksiri, B.; Klayut, W.; Rudeeaneksin, J.; Srisungngam, S.; Bunchoo, S.; Toonkomdang, S.; Wongchai, T.; Nakajima, C.; Suzuki, Y. The performance of an in-house loop-mediated isothermal amplification for the rapid detection of Mycobacterium tuberculosis in sputum samples in comparison with Xpert MTB/RIF, microscopy and culture. Rev. Inst. Med. Trop. Sao Paulo 2020, 62, e36.
  32. World Health Organization. The Use of Loop-Mediated Isothermal Amplification (TB-LAMP) for the Diagnosis of Pulmonary Tuberculosis: Policy Guidance. Available online: (accessed on 22 September 2022).
  33. Rossau, R.; Traore, H.; De Beenhouwer, H.; Mijs, W.; Jannes, G.; De Rijk, P.; Portaels, F. Evaluation of the INNO-LiPA Rif. TB assay, a reverse hybridization assay for the simultaneous detection of Mycobacterium tuberculosis complex and its resistance to rifampin. Antimicrob. Agents Chemother. 1997, 41, 2093–2098.
  34. Crudu, V.; Stratan, E.; Romancenco, E.; Allerheiligen, V.; Hillemann, A.; Moraru, N. First evaluation of an improved assay for molecular genetic detection of tuberculosis as well as rifampin and isoniazid resistances. J. Clin. Microbiol. 2012, 50, 1264–1269.
  35. Zeng, X.; Jing, W.; Zhang, Y.; Duan, H.; Huang, H.; Chu, N. Performance of the MTBDRsl Line probe assay for rapid detection of resistance to second-line anti-tuberculosis drugs and ethambutol in China. Diagn. Microbiol. Infect. Dis. 2017, 89, 112–117.
  36. Mao, X.; Ke, Z.; Shi, X.; Liu, S.; Tang, B.; Wang, J.; Huang, H. Diagnosis of Drug Resistance to Fluoroquinolones, Amikacin, Capreomycin, Kanamycin and Ethambutol with Genotype MTBDRsl Assay: A Meta-Analysis. Ann. Clin. Lab. Sci. 2015, 45, 533–544.
  37. Nikam, C.; Jagannath, M.; Narayanan, M.M.; Ramanabhiraman, V.; Kazi, M.; Shetty, A.; Rodrigues, C. Rapid diagnosis of Mycobacterium tuberculosis with Truenat MTB: A near-care approach. PLoS ONE 2013, 8, e51121.
  38. Georghiou, S.B.; Gomathi, N.S.; Rajendran, P.; Nagalakshmi, V.; Prabakaran, L.; Prem Kumar, M.M.; Macé, A.; Tripathy, S.; Ruhwald, M.; Schumacher, S.G.; et al. Accuracy of the Truenat MTB-RIF Dx assay for detection of rifampicin resistance-associated mutations. Tuberculosis 2021, 127, 102064.
  39. MacLean, E.; Kohli, M.; Weber, S.F.; Suresh, A.; Schumacher, S.G.; Denkinger, C.M.; Pai, M. Advances in Molecular Diagnosis of Tuberculosis. J. Clin. Microbiol. 2020, 58, e01582-19.
  40. Steingart, K.R.; Flores, L.L.; Dendukuri, N.; Schiller, I.; Laal, S.; Ramsay, A.; Hopewell, P.C.; Pai, M. Commercial serological tests for the diagnosis of active pulmonary and extrapulmonary tuberculosis: An updated systematic review and meta-analysis. PLoS Med. 2011, 8, e1001062.
  41. Strohmeier, G.R.; Fenton, M.J. Roles of lipoarabinomannan in the pathogenesis of tuberculosis. Microbes Infect. 1999, 1, 709–717.
  42. Scheynius, A.; Klareskog, L.; Forsum, U. In situ identification of T lymphocyte subsets and HLA-DR expressing cells in the human skin tuberculin reaction. Clin. Exp. Immunol. 1982, 49, 325–330.
  43. Kowalewicz-Kulbat, M.; Szpakowski, P.; Locht, C.; Biet, F.; Kaplonek, P.; Krawczyk, K.T.; Pestel, J.; Rudnicka, W. Tuberculin skin test reaction is related to memory, but not naive CD4(+) T cell responses to mycobacterial stimuli in BCG-vaccinated young adults. Vaccine 2018, 36, 4566–4577.
  44. Abubakar, I.; Drobniewski, F.; Southern, J.; Sitch, A.J.; Jackson, C.; Lipman, M.; Deeks, J.J.; Griffiths, C.; Bothamley, G.; Lynn, W.; et al. Prognostic value of interferon-γ release assays and tuberculin skin test in predicting the development of active tuberculosis (UK PREDICT TB): A prospective cohort study. Lancet Infect. Dis. 2018, 18, 1077–1087.
  45. Menzies, D.; Pai, M.; Comstock, G. Meta-analysis: New tests for the diagnosis of latent tuberculosis infection: Areas of uncertainty and recommendations for research. Ann. Intern. Med. 2007, 146, 340–354.
  46. Metcalfe, J.Z.; Everett, C.K.; Steingart, K.R.; Cattamanchi, A.; Huang, L.; Hopewell, P.C.; Pai, M. Interferon-γ release assays for active pulmonary tuberculosis diagnosis in adults in low- and middle-income countries: Systematic review and meta-analysis. J. Infect. Dis. 2011, 204 (Suppl. 4), S1120–S1129.
  47. Ling, D.I.; Pai, M.; Davids, V.; Brunet, L.; Lenders, L.; Meldau, R.; Calligaro, G.; Allwood, B.; van Zyl-Smit, R.; Peter, J.; et al. Are interferon-γ release assays useful for diagnosing active tuberculosis in a high-burden setting? Eur. Respir. J. 2011, 38, 649–656.
  48. Sollai, S.; Galli, L.; de Martino, M.; Chiappini, E. Systematic review and meta-analysis on the utility of Interferon-gamma release assays for the diagnosis of Mycobacterium tuberculosis infection in children: A 2013 update. BMC Infect. Dis. 2014, 14 (Suppl. 1), S6.
  49. Detjen, A.K.; Keil, T.; Roll, S.; Hauer, B.; Mauch, H.; Wahn, U.; Magdorf, K. Interferon-gamma release assays improve the diagnosis of tuberculosis and nontuberculous mycobacterial disease in children in a country with a low incidence of tuberculosis. Clin. Infect. Dis. 2007, 45, 322–328.
  50. Vincenti, D.; Carrara, S.; Butera, O.; Bizzoni, F.; Casetti, R.; Girardi, E.; Goletti, D. Response to region of difference 1 (RD1) epitopes in human immunodeficiency virus (HIV)-infected individuals enrolled with suspected active tuberculosis: A pilot study. Clin. Exp. Immunol. 2007, 150, 91–98.
  51. Campainha, S.; Gomes, T.; Carvalho, A.; Duarte, R. Negative predictive value of TST and IGRA in anti-TNF treated patients. Eur. Respir. J. 2012, 40, 790–791.
  52. Brodie, D.; Lederer, D.J.; Gallardo, J.S.; Trivedi, S.H.; Burzynski, J.N.; Schluger, N.W. Use of an interferon-gamma release assay to diagnose latent tuberculosis infection in foreign-born patients. Chest 2008, 133, 869–874.
  53. Dorman, S.E.; Belknap, R.; Graviss, E.A.; Reves, R.; Schluger, N.; Weinfurter, P.; Wang, Y.; Cronin, W.; Hirsch-Moverman, Y.; Teeter, L.D.; et al. Interferon-γ release assays and tuberculin skin testing for diagnosis of latent tuberculosis infection in healthcare workers in the United States. Am. J. Respir. Crit. Care Med. 2014, 189, 77–87.
  54. Kim, S.H.; Choi, S.J.; Kim, H.B.; Kim, N.J.; Oh, M.D.; Choe, K.W. Diagnostic usefulness of a T-cell based assay for extrapulmonary tuberculosis. Arch. Intern. Med. 2007, 167, 2255–2259.
  55. Dheda, K.; van Zyl Smit, R.; Badri, M.; Pai, M. T-cell interferon-gamma release assays for the rapid immunodiagnosis of tuberculosis: Clinical utility in high-burden vs. low-burden settings. Curr. Opin. Pulm. Med. 2009, 15, 188–200.
  56. Janssens, J.P.; Roux-Lombard, P.; Perneger, T.; Metzger, M.; Vivien, R.; Rochat, T. Quantitative scoring of an interferon-gamma assay for differentiating active from latent tuberculosis. Eur. Respir. J. 2007, 30, 722–728.
  57. Ling, D.I.; Nicol, M.P.; Pai, M.; Pienaar, S.; Dendukuri, N.; Zar, H.J. Incremental value of T-SPOT.TB for diagnosis of active pulmonary tuberculosis in children in a high-burden setting: A multivariable analysis. Thorax 2013, 68, 860–866.
  58. Pinto, L.M.; Grenier, J.; Schumacher, S.G.; Denkinger, C.M.; Steingart, K.R.; Pai, M. Immunodiagnosis of tuberculosis: State of the art. Med. Princ. Pract. 2012, 21, 4–13.
  59. Lalvani, A.; Pareek, M. Interferon gamma release assays: Principles and practice. Enferm. Infecc. Microbiol. Clin. 2010, 28, 245–252.
  60. Shafeque, A.; Bigio, J.; Hogan, C.A.; Pai, M.; Banaei, N. Fourth-Generation QuantiFERON-TB Gold Plus: What Is the Evidence? J. Clin. Microbiol. 2020, 58, e01950-19.
  61. Venkatappa, T.K.; Punnoose, R.; Katz, D.J.; Higgins, M.P.; Banaei, N.; Graviss, E.A.; Belknap, R.W.; Ho, C.S. Comparing QuantiFERON-TB Gold Plus with Other Tests To Diagnose Mycobacterium tuberculosis Infection. J. Clin. Microbiol. 2019, 57, e00985-19.
  62. Moon, H.W.; Gaur, R.L.; Tien, S.S.; Spangler, M.; Pai, M.; Banaei, N. Evaluation of QuantiFERON-TB Gold-Plus in Health Care Workers in a Low-Incidence Setting. J. Clin. Microbiol. 2017, 55, 1650–1657.
  63. Theel, E.S.; Hilgart, H.; Breen-Lyles, M.; McCoy, K.; Flury, R.; Breeher, L.E.; Wilson, J.; Sia, I.G.; Whitaker, J.A.; Clain, J.; et al. Comparison of the QuantiFERON-TB Gold Plus and QuantiFERON-TB Gold In-Tube Interferon Gamma Release Assays in Patients at Risk for Tuberculosis and in Health Care Workers. J. Clin. Microbiol. 2018, 56, e00614-18.
  64. Lee, R.S.; Pai, M. Real-Time Sequencing of Mycobacterium tuberculosis: Are We There Yet? J. Clin. Microbiol. 2017, 55, 1249–1254.
  65. Walker, T.M.; Kohl, T.A.; Omar, S.V.; Hedge, J.; Del Ojo Elias, C.; Bradley, P.; Iqbal, Z.; Feuerriegel, S.; Niehaus, K.E.; Wilson, D.J.; et al. Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: A retrospective cohort study. Lancet Infect. Dis. 2015, 15, 1193–1202.
  66. World Health Organization. The Use of Next-Generation Sequencing Technologies for the Detection of Mutations Associated with Drug Resistance in Mycobacterium tuberculosis Complex: Technical Guide. Available online: (accessed on 23 August 2022).
  67. Balada-Llasat, J.M.; Kamboj, K.; Pancholi, P. Identification of mycobacteria from solid and liquid media by matrix-assisted laser desorption ionization-time of flight mass spectrometry in the clinical laboratory. J. Clin. Microbiol. 2013, 51, 2875–2879.
  68. Alcolea-Medina, A.; Fernandez, M.T.C.; Montiel, N.; García, M.P.L.; Sevilla, C.D.; North, N.; Lirola, M.J.M.; Wilks, M. An improved simple method for the identification of Mycobacteria by MALDI-TOF MS (Matrix-Assisted Laser Desorption- Ionization mass spectrometry). Sci. Rep. 2019, 9, 20216.
  69. Shitikov, E.; Ilina, E.; Chernousova, L.; Borovskaya, A.; Rukin, I.; Afanas’ev, M.; Smirnova, T.; Vorobyeva, A.; Larionova, E.; Andreevskaya, S.; et al. Mass spectrometry based methods for the discrimination and typing of mycobacteria. Infect. Genet. Evol. 2012, 12, 838–845.
  70. Tudó, G.; Monté, M.R.; Vergara, A.; López, A.; Hurtado, J.C.; Ferrer-Navarro, M.; Vila, J.; Gonzalez-Martin, J. Implementation of MALDI-TOF MS technology for the identification of clinical isolates of Mycobacterium spp. in mycobacterial diagnosis. Eur. J. Clin. Microbiol. Infect. Dis. 2015, 34, 1527–1532.
  71. Zingue, D.; Flaudrops, C.; Drancourt, M. Direct matrix-assisted laser desorption ionisation time-of-flight mass spectrometry identification of mycobacteria from colonies. Eur. J. Clin. Microbiol. Infect. Dis. 2016, 35, 1983–1987.
  72. Lopes, U.K.; Valiati, J.F. Pre-trained convolutional neural networks as feature extractors for tuberculosis detection. Comput. Biol. Med. 2017, 89, 135–143.
  73. Aguiar, F.S.; Torres, R.C.; Pinto, J.V.; Kritski, A.L.; Seixas, J.M.; Mello, F.C. Development of two artificial neural network models to support the diagnosis of pulmonary tuberculosis in hospitalized patients in Rio de Janeiro, Brazil. Med. Biol. Eng. Comput. 2016, 54, 1751–1759.
  74. Jaeger, S.; Juarez-Espinosa, O.H.; Candemir, S.; Poostchi, M.; Yang, F.; Kim, L.; Ding, M.; Folio, L.R.; Antani, S.; Gabrielian, A.; et al. Detecting drug-resistant tuberculosis in chest radiographs. Int. J. Comput. Assist. Radiol. Surg. 2018, 13, 1915–1925.
  75. Khan, S.; Ullah, R.; Shahzad, S.; Anbreen, N.; Bilal, M.; Khan, A. Analysis of tuberculosis disease through Raman spectroscopy and machine learning. Photodiagnosis Photodyn. Ther. 2018, 24, 286–291.
  76. Mohamed, E.I.; Mohamed, M.A.; Moustafa, M.H.; Abdel-Mageed, S.M.; Moro, A.M.; Baess, A.I.; El-Kholy, S.M. Qualitative analysis of biological tuberculosis samples by an electronic nose-based artificial neural network. Int. J. Tuberc. Lung Dis. 2017, 21, 810–817.
  77. Kuok, C.P.; Horng, M.H.; Liao, Y.M.; Chow, N.H.; Sun, Y.N. An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks. Microsc. Res. Tech. 2019, 82, 709–719.
  78. Elveren, E.; Yumuşak, N. Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm. J. Med. Syst. 2011, 35, 329–332.
  79. Osamor, V.C.; Azeta, A.A.; Ajulo, O.O. Tuberculosis-Diagnostic Expert System: An architecture for translating patients information from the web for use in tuberculosis diagnosis. Health Inform. J. 2014, 20, 275–287.
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : , , , ,
View Times: 368
Revisions: 2 times (View History)
Update Date: 25 Nov 2022
Video Production Service