Diagnostic Accuracy of Computer-Aided Detection of Pulmonary Tuberculosis in Chest Radiographs: A Validation Study from Sub-Saharan Africa

Background & Objectives: Pakistan ranked fifth amongst 22 high-burden Tuberculosis countries, and it is an epidemic in Pakistan, hence screening is performed nationally, as part of the ambitious ZERO TB drive. Our objective was to assess the diagnostic accuracy of Computer Aided Detection (CAD4TB) software on chest Xray in screening for pulmonary tuberculosis in comparison with gene-Xpert. Methods: The study was conducted by Radiology Department Lady Reading Hospital Peshawar in affiliation with Indus Hospital network over a period of one year. Screening was done by using mobile Xray unit equipped with CAD4TB software with scoring system. All of those having score of more than 70 and few selected cases with strong clinical suspicion but score of less than 70 were referred to dedicated TB clinic for Gene-Xpert analysis. Results: Among 26,997 individuals screened, 2617 (9.7%) individuals were found presumptive for pulmonary TB. Sputum samples for Gene-Xpert were obtained in 2100 (80.24%) individuals, out of which 1825 (86.9%) were presumptive for pulmonary TB on CAD4TB only. Gene-Xpert was positive in 159 (8.7%) patients and negative in 1,666(91.3%). Sensitivity and specificity of CAD4TB and symptomatology with threshold score of ≥70 was 83.2% and 12.7% respectively keeping Gene-Xpert as gold standard. Conclusion: Combination of chest X-ray analysis by CAD4TB and symptomatology is of immense value to screen a large population at risk in a developing high burden country. It is significantly a more effective tool for screening and early diagnosis of TB in individuals, who would otherwise go undiagnosed. Abbreviations: TB = Tuberculosis, WHO = World Health Organization, CAD4TB = Computer aided detection for tuberculosis, CXR = Chest X-Ray, CAR = Computer aided reading. doi: https://doi.org/10.12669/pjms.38.1.4531 How to cite this:Nishtar T, Burki S, Ahmad FS, Ahmad T. Diagnostic accuracy of computer aided reading of chest x-ray in screening for pulmonary tuberculosis in comparison with Gene-Xpert. Pak J Med Sci. 2022;38(1):---------. doi: https://doi.org/10.12669/pjms.38.1.4531 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Computer-aided detection of pulmonary tuberculosis on digital chest radiographs: a systematic review

The International Journal of Tuberculosis and Lung Disease ◽

10.5588/ijtld.15.0926 ◽

2016 ◽

Vol 20 (9) ◽

pp. 1226-1230 ◽

Cited By ~ 43

Author(s):

T. Pande ◽

C. Cohen ◽

M. Pai ◽

F. Ahmad Khan

Keyword(s):

Systematic Review ◽

Pulmonary Tuberculosis ◽

Chest Radiographs ◽

Computer Aided Detection ◽

Computer Aided ◽

Digital Chest

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Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning

European Radiology ◽

10.1007/s00330-020-06892-9 ◽

2020 ◽

Vol 30 (9) ◽

pp. 4943-4951

Author(s):

Young-Gon Kim ◽

Sang Min Lee ◽

Kyung Hee Lee ◽

Ryoungwoo Jang ◽

Joon Beom Seo ◽

...

Keyword(s):

Deep Learning ◽

Lung Nodule ◽

Chest Radiographs ◽

Computer Aided Detection ◽

Matrix Size ◽

Computer Aided

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Sensitivity of Computer-Aided Detection (CAD) Device for Lung Nodule Detection on Chest Radiography: A Real-Life Application

Radiology and Medical Diagnostic Imaging ◽

10.31487/j.rdi.2020.02.05 ◽

2020 ◽

pp. 1-5

Author(s):

Ammar Chaudhry ◽

William H. Moore

Keyword(s):

Sensitivity And Specificity ◽

Chest Radiography ◽

Predictive Value ◽

Lung Nodule ◽

Chest Radiographs ◽

Lung Nodules ◽

Computer Aided Detection ◽

Cad System ◽

Computer Aided ◽

The Mean

Purpose: The radiographic diagnosis of lung nodules is associated with low sensitivity and specificity. Computer-aided detection (CAD) system has been shown to have higher accuracy in the detection of lung nodules. The purpose of this study is to assess the effect on sensitivity and specificity when a CAD system is used to review chest radiographs in real-time setting. Methods: Sixty-three patients, including 24 controls, who had chest radiographs and CT within three months were included in this study. Three radiologists were presented chest radiographs without CAD and were asked to mark all lung nodules. Then the radiologists were allowed to see the CAD region-of-interest (ROI) marks and were asked to agree or disagree with the marks. All marks were correlated with CT studies. Results: The mean sensitivity of the three radiologists without CAD was 16.1%, which showed a statistically significant improvement to 22.5% with CAD. The mean specificity of the three radiologists was 52.5% without CAD and decreased to 48.1% with CAD. There was no significant change in the positive predictive value or negative predictive value. Conclusion: The addition of a CAD system to chest radiography interpretation statistically improves the detection of lung nodules without affecting its specificity. Thus suggesting CAD would improve overall detection of lung nodules.

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Evaluation of the diagnostic accuracy of Computer-Aided Detection of tuberculosis on Chest radiography among private sector patients in Pakistan

Scientific Reports ◽

10.1038/s41598-018-30810-1 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 10

Author(s):

Syed Mohammad Asad Zaidi ◽

Shifa Salman Habib ◽

Bram Van Ginneken ◽

Rashida Abbas Ferrand ◽

Jacob Creswell ◽

...

Keyword(s):

Private Sector ◽

Diagnostic Accuracy ◽

Chest Radiography ◽

Computer Aided Detection ◽

Computer Aided

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Computer-aided Detection of Lung Cancer on Chest Radiographs: Effect on Observer Performance

Radiology ◽

10.1148/radiol.10092437 ◽

2010 ◽

Vol 257 (2) ◽

pp. 532-540 ◽

Cited By ~ 39

Author(s):

Bartjan de Hoop ◽

Diederik W. De Boo ◽

Hester A. Gietema ◽

Frans van Hoorn ◽

Banafsche Mearadji ◽

...

Keyword(s):

Lung Cancer ◽

Chest Radiographs ◽

Observer Performance ◽

Computer Aided Detection ◽

Computer Aided

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A Computer-Aided Detection System for Digital Chest Radiographs

Journal of Healthcare Engineering ◽

10.1155/2016/8208923 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 6

Author(s):

Juan Manuel Carrillo-de-Gea ◽

Ginés García-Mateos ◽

José Luis Fernández-Alemán ◽

José Luis Hernández-Hernández

Keyword(s):

Template Matching ◽

Detection System ◽

Local Binary Patterns ◽

Chest Radiographs ◽

Computer Aided Detection ◽

Success Rates ◽

Novel Approach ◽

Computer Aided ◽

Digital Chest ◽

Areas Of Interest

Computer-aided detection systems aim at the automatic detection of diseases using different medical imaging modalities. In this paper, a novel approach to detecting normality/pathology in digital chest radiographs is proposed. The problem tackled is complicated since it is not focused on particular diseases but anything that differs from what is considered as normality. First, the areas of interest of the chest are found using template matching on the images. Then, a texture descriptor called local binary patterns (LBP) is computed for those areas. After that, LBP histograms are applied in a classifier algorithm, which produces the final normality/pathology decision. Our experimental results show the feasibility of the proposal, with success rates above 87% in the best cases. Moreover, our technique is able to locate the possible areas of pathology in nonnormal radiographs. Strengths and limitations of the proposed approach are described in the Conclusions.

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