Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: Feasibility study

International Journal of Tuberculosis and Lung Disease

Volumn 22, Issue 3, 2018, Pages 328-335

  Becker, A S ^a   Bluthgen C ^a   Van Phi, V D ^a   Sekaggya Wiltshire, C ^b   Castelnuovo, B ^b   Kambugu, A ^b   Fehr, J ^a   Frauenfelder, T ^a  

^a UNIVERSITY HOSPITAL ZURICH   (Switzerland)

^b MAKERERE UNIVERSITY   (Uganda)

Author keywords

Chest radiograph; CXR; Deep learning; TB; Teleradiology

Indexed keywords

ADULT; AREA UNDER THE CURVE; ARTICLE; CLASSIFICATION; CONTROLLED STUDY; DEEP LEARNING; DIAGNOSTIC TEST ACCURACY STUDY; FEASIBILITY STUDY; FEMALE; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; IMAGE ANALYSIS; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MEDICAL PHOTOGRAPHY; OBSERVATIONAL STUDY; PLEURA EFFUSION; PREDICTIVE VALUE; PRIORITY JOURNAL; PROSPECTIVE STUDY; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SOFTWARE; THORAX RADIOGRAPHY; TUBERCULOSIS; UGANDA; DEVICES; DIAGNOSTIC IMAGING; LUNG TUBERCULOSIS; MIXED INFECTION; PROCEDURES; TELERADIOLOGY;

ADULT; COINFECTION; DEEP LEARNING; FEASIBILITY STUDIES; FEMALE; HIV INFECTIONS; HUMANS; MALE; PREDICTIVE VALUE OF TESTS; PROSPECTIVE STUDIES; RADIOGRAPHY, THORACIC; ROC CURVE; SOFTWARE; TELERADIOLOGY; TUBERCULOSIS, PULMONARY; UGANDA;

EID: 85040508258     PISSN: 10273719     EISSN: 18157920     Source Type: Journal    
DOI: 10.5588/ijtld.17.0520     Document Type: Article

References (28)

1. Tiemersma EW, van derWerf MJ, Borgdorff M W, Williams B G, Nagelkerke N J D. Natural history of tuberculosis: duration and fatality of untreated pulmonary tuberculosis in HIV negative patients: A systematic review. PLOS ONE 2011; 6: E17601.
2. Skoura E, Zumla A, Bomanji J. Imaging in tuberculosis. Int J Infect Dis 2015; 32: 87-93.
3. Somashekar N, Chadha V K, Praseeja P, et al. Role of pre-Xpertw screening using chest X-ray in early diagnosis of smearnegative pulmonary tuberculosis. Int J Tuberc Lung Dis 2014; 18: 1243-1244.
4. Davies P, Gordon S B, Davies G. Clinical tuberculosis. Boca Raton, FL, USA: CRC Press Book, 2014.
5. World Health Organization. Global tuberculosis report, 2016. WHO/HTM/TB/2016.13. Geneva, Switzerland: WHO, 2016.
6. Serre T, Kreiman G, Kouh M, Cadieu C, Knoblich U, Poggio T. A quantitative theory of immediate visual recognition. In: Cisek P, Drew T, Kalaska J, eds. Computational neuroscience: Theoretical insights into brain function. Progress in Brain Research. Vol 165. Amsterdam, The Netherlands: Elsevier, 2007: pp 33-56.
7. Szot A, Jacobson F L, Munn S, et al. Diagnostic accuracy of chest X-rays acquired using a digital camera for low-cost teleradiology. Int J Med Inform 2004; 73: 65-73.
8. Esteva A, Kuprel B, Novoa R A, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115-118.
9. LeCun YA, Bottou L, Orr G B, Muller K-R. Efficient BackProp. In: Neural networks: Tricks of the trade. Lecture Notes in Computer Science. Vol 7700. Berlin, Heidelberg, Germany: Springer Berlin Heidelberg, 2012: pp 9-48.
10. Hinton G E. To recognize shapes, first learn to generate images. Prog Brain Res 2007; 165: 535-547.
11. Bengio Y. Learning Deep architectures for AI. In: Jordan M, ed. Foundations and trendsw in machine learning. Delft, The Netherlands: Now Publishers Inc, 2009.
12. Becker A S, Marcon M, Ghafoor S, Wurnig M C, Frauenfelder T, Boss A. Deep Learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Invest Radiol 2017; 52: 434-440.
13. Harries A D, Maher D, Graham S. TB/HIV: A clinical manual. Geneva, Switzerland: World Health Organization, 2004.
14. Davis J L, Worodria W, Kisembo H, et al. Clinical and radiographic factors do not accurately diagnose smear-negative tuberculosis in HIV-infected inpatients in Uganda: A crosssectional study. PLOS ONE 2010; 5: E9859.
15. Kosack C S, Spijker S, Halton J, et al. Evaluation of a chest radiograph reading and recording system for tuberculosis in a HIV-positive cohort. Clin Radiol 2017; 72: 519.e1-519.e9.
16. Semakula-Katende N S, Andronikou S, Lucas S. Digital platform for improving non-radiologists' and radiologists' interpretation of chest radiographs for suspected tuberculosis-A method for supporting task-shifting in developing countries. Pediatr Radiol 2016; 46: 1384-1391.
17. Seddon J A, Padayachee T, Plessis du A-M, et al. Teaching chest X-ray reading for child tuberculosis suspects. Int J Tuberc Lung Dis 2014; 18: 763-769.
18. Kohli M, Prevedello L M, Filice R W, Geis J R. Implementing machine learning in radiology practice and research. AJR Am J Roentgenol 2017; 208: 754-760.
19. Jordan M I, Mitchell T M. Machine learning: Trends, perspectives, and prospects. Science 2015; 349: 255-260.
20. Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A. Learning deep features for discriminative localization. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27-30 June 2016, Las Vegas, NV, USA: pp 2921- 2929.
21. Lakhani P, Sundaram B. Deep Learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 2017; 284: 574- 582.
22. Geras K J, Wolfson S, Kim S G, Moy L, Cho K. High-resolution breast cancer screening with multi-view deep convolutional neural networks. Computer Vision and Pattern Recognition, 6 November 2017.
23. Steiner A, Mangu C, vandenHombergh J, et al. Screening for pulmonary tuberculosis in a Tanzanian prison and computeraided interpretation of chest X-rays. Public Health Action 2015; 5: 249-254.
24. 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.
25. Maduskar P, Muyoyeta M, Ayles H, Hogeweg L, Peters-Bax L, van Ginneken B. Detection of tuberculosis using digital chest radiography: Automated reading vs. interpretation by clinical officers. Int J Tuberc Lung Dis 2013; 17: 1613-1620.
26. Martini K, Ganter C, Maggiorini M, et al. Interpretation of bedside chest X-rays in the ICU: is the radiologist still needed? J Clin Imaging 2015; 39: 1018-1023.
27. Fraser R S, Muller N L, Colman N, Pare P D. Fraser and Paŕe's diagnosis of diseases of the chest. Vols 1-4. Philadelphia, PA, USA: W B Saunders, 1999.
28. Schmidhuber J. Deep Learning in neural networks: An overview. Neural Networks 2015; 61: 85-117.