An evaluation of automated chest radiography reading software for tuberculosis screening among public- And private-sector patients

European Respiratory Journal

Volumn 49, Issue 5, 2017, Pages

  Rahman, Md Toufiq ^a   Codlin, Andrew J ^b   Rahman, Md Mahfuzur ^a   Nahar, Ayenun ^a   Reja, Mehdi ^a   Islam, Tariqul ^c   Qin, Zhi Zhen ^b   Khan, Md Abdus Shakur ^d   Banu, Sayera ^a   Creswell, Jacob ^b  

^a INTERNATIONAL CENTRE FOR DIARRHOEAL DISEASE RESEARCH   (Bangladesh)

^b Stop TB Partnership   (Switzerland)

^c National Institute of Neurosciences and Hospital   (Bangladesh)

^d National Institute of Diseases of Chest and Hospital   (Bangladesh)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; ALGORITHM; AREA UNDER THE CURVE; BANGLADESH; COMPUTER ASSISTED DIAGNOSIS; EXTENSIVELY DRUG RESISTANT TUBERCULOSIS; FEMALE; HUMAN; LUNG TUBERCULOSIS; MALE; MASS SCREENING; MIDDLE AGED; MULTIDRUG RESISTANT TUBERCULOSIS; PRIVATE SECTOR; PROCEDURES; PROSPECTIVE STUDY; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SOFTWARE; THORAX RADIOGRAPHY; X-RAY COMPUTED TOMOGRAPHY;

ADULT; AGED; ALGORITHMS; AREA UNDER CURVE; BANGLADESH; DIAGNOSIS, COMPUTER-ASSISTED; EXTENSIVELY DRUG-RESISTANT TUBERCULOSIS; FEMALE; HUMANS; MALE; MASS SCREENING; MIDDLE AGED; PRIVATE SECTOR; PROSPECTIVE STUDIES; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; RADIOGRAPHY, THORACIC; ROC CURVE; SENSITIVITY AND SPECIFICITY; SOFTWARE; TOMOGRAPHY, X-RAY COMPUTED; TUBERCULOSIS, MULTIDRUG-RESISTANT; TUBERCULOSIS, PULMONARY;

EID: 85044163215     PISSN: 09031936     EISSN: 13993003     Source Type: Journal    
DOI: 10.1183/13993003.02159-2016     Document Type: Article

References (32)

1. van’t Hoog AH, Onozaki I, Lonnroth K. Choosing algorithms for TB screening: a modelling study to compare yield, predictive value and diagnostic burden. BMC Infect Dis 2014; 14: 532.
2. Somashekar N, Chadha VK, Praseeja P, et al. Role of pre-Xpert screening using chest X-ray in early diagnosis of smear-negative pulmonary tuberculosis. Int J Tuberc Lung Dis 2014; 18: 1243–1244.
3. Pande T, Pai M, Khan FA, et al. Use of chest radiography in the 22 highest tuberculosis burden countries. Eur Respir J 2015; 46: 1816–1819.
4. Pedrazzoli D, Lalli M, Boccia D, et al. Can tuberculosis patients in resource-constrained settings afford chest radiography? Eur Respir J 2017 47: 1601877.
5. WHO Expert Committee on Tuberculosis. Ninth Report. WHO Technical Report Series, No. 552. Geneva, World Health Organization, 1974.
6. Waaler HT. Descriptions of the study material and organization of the study. Bull Int Union Tuberc 1968; 41: 110–114.
7. Pinto LM, Pai M, Dheda K, et al. Scoring systems using chest radiographic features for the diagnosis of pulmonary tuberculosis in adults: a systematic review. Eur Respir J 2013; 42: 480–494.
8. Karssemeijer N, Bluekens AM, Beijerinck D, et al. Breast cancer screening results 5 years after introduction of digital mammography in a population-based screening program. Radiology 2009; 253: 353–358.
9. van Ginneken B, Stegmann MB, Loog M. Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Med Image Anal 2006; 10: 19–40.
10. Breuninger M, van Ginneken B, Philipsen RH, et al. Diagnostic accuracy of computer-aided detection of pulmonary tuberculosis in chest radiographs: a validation study from sub-Saharan Africa. PloS One 2014; 9: e106381.
11. Maduskar P, Muyoyeta M, Ayles H, et al. Detection of tuberculosis with digital chest radiography: automatic reading versus interpretation by clinical officers. Int J Tuberc Lung Dis 2013; 17: 1613–1620.
12. Muyoyeta M, Maduskar P, Moyo M, et al. The sensitivity and specificity of using a computer aided diagnosis program for automatically scoring chest X-rays of presumptive TB patients compared with Xpert MTB/RIF in Lusaka Zambia. PLoS One 2014; 9: e93757.
13. Steiner A, Mangu C, van den Hombergh J, et al. Screening for pulmonary tuberculosis in a Tanzanian prison and computer-aided interpretation of chest X-rays. Public Health Action 2015; 5: 249–254.
14. Philipsen RHHM, Sánchez CI, Maduskar P, et al. Automated chest-radiography as a triage for Xpert testing in resource-constrained settings: a prospective study of diagnostic accuracy and costs. Sci Rep 2015; 5: 12215.
15. Pande T, Cohen C, Pai M, et al. Computer aided detection of pulmonary tuberculosis on digital chest radiographs: a systematic review. Int J Tuberc Lung Dis 2016; 20: 1226–1230.
16. Pantoja A, Fitzpatrick C, Vassall A, et al. Xpert MTB/RIF for diagnosis of TB and drug-resistant TB: a cost and affordability analysis. Eur Respir J 2013; 42: 708–820.
17. Steingart KR, Schiller I, Horne DJ, et al. Xpert® MTB/RIF assay for pulmonary tuberculosis and rifampicin resistance in adults. Cochrane Database Syst Rev 2014; 1: CD009593.
18. Steingart KR, Henry M, Ng V, et al. Fluorescence versus conventional sputum smear microscopy for tuberculosis: a systematic review. Lancet Infect Dis 2006; 6: 570–581.
19. World Health Organization. Automated real-time nucleic acid amplification technology for rapid and simultaneous detection of tuberculosis and rifampicin resistance: Xpert MTB/RIF assay for the diagnosis of pulmonary and extra-pulmonary TB in adults and children. Policy update. WHO/HTM/TB/2013.16. Geneva, WHO, 2013.
20. Kik SV, Denkinger CM, Casenghi M, et al. Tuberculosis diagnostics: which target product profiles should be prioritised? Eur Respir J 2014; 44: 537–540.
21. Theron G, Pooran A, Peter J, et al. Do adjunct tuberculosis tests, when combined with Xpert MTB/RIF, improve accuracy and the cost of diagnosis in a resource-poor setting? Eur Respir J 2012; 40: 161–168.
22. Albert H, Nathavitharana RR, Isaacs C, et al. Development, roll-out and impact of Xpert MTB/RIF for tuberculosis: what lessons have we learnt and how can we do better? Eur Respir J 2016; 48: 516–525.
23. Weyer K, Mirzayev F, Migliori GB, et al. Rapid molecular TB diagnosis: evidence, policy making and global implementation of Xpert MTB/RIF. Eur Respir J 2013; 42: 252–271.
24. Hossain S, Zaman K, Quaiyum A, et al. Care seeking in tuberculosis: results from a countrywide cluster randomised survey in Bangladesh. BMJ Open 2014; 4: e004766.
25. Shaikh B, Hatcher J. Health seeking behaviour and health services utilization trends in National Health Survey of Pakistan: what needs to be done? J Pak Med Assoc 2007; 57: 411–414.
26. Puri L, Oghor C, Denkinger CM, et al. Xpert MTB/RIF for tuberculosis testing: access and price in highly privatised health markets. Lancet Glob Health 2016; 4: e94–e95.
27. Qin ZZ, Pai M, Van Gemert W, et al. How is Xpert MTB/RIF being implemented in 22 high tuberculosis burden countries? Eur Respir J 2015; 45: 549–554.
28. Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011; 12: 77.
29. WHO. The World Health Report 2006: Working Together for Health. Geneva, WHO, 2006.
30. World Health Organization. Global Tuberculosis Report 2015. WHO/HTM/TB/2015.22. Geneva, WHO, 2015. Available from: http://who.int/tb/publications/global_report/en/
31. Onozaki I, Law I, Sismanidis C, et al. National tuberculosis prevalence surveys in Asia, 1990–2012: an overview of results and lessons learned. Trop Med Int Health 2015; 20: 1128–1145.
32. Wells WA, Ge CF, Patel N, et al. Size and usage patterns of private TB drug markets in the high burden countries. PLoS One 2011; 6: e18964.