scholarly journals Diagnostic accuracy of computer aided reading of chest x-ray in screening for pulmonary tuberculosis in comparison with Gene-Xpert

2021 ◽  
Vol 38 (1) ◽  
Author(s):  
Tahira Nishtar ◽  
Shamsullah Burki ◽  
Fatima Sultan Ahmad ◽  
Tabish Ahmad
Keyword(s):  
Pulmonary Tuberculosis ◽  
Diagnostic Accuracy ◽  
World Health ◽  
Computer Aided Detection ◽  
Pulmonary Tb ◽  
High Burden ◽  
X Ray ◽  
Computer Aided ◽  
Chest X Ray ◽  
Computer Aided Reading

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.

scholarly journals Computer Aided Tuberculosis Detection, A Review

2021 ◽  
Author(s):  
Pragya Shukla ◽  
Jasleen Saini ◽  
B.S. Saini
Keyword(s):  
Probabilistic Methods ◽  
Computer Aided Detection ◽  
Chronological Order ◽  
Borderline Cases ◽  
X Ray ◽  
Computational Capability ◽  
Computer Aided ◽  
Chest X Ray ◽  
The Moment ◽  
Visualization Techniques

This paper aims at presenting a complete picture of advances till now in the field of computer-aided detection of Pulmonary Tuberculosis using Chest X-ray Images. Advances are analyzed in chronological order as they happen and are divided into three phases in which technology shifted into new paradigms. Study concludes that although techniques that use Machine learning based methods for segmentation and classification are prevailing for the moment in terms of flexibility for very particular feature extraction in borderline cases where probabilistic methods can be tweaked according to requirements and accuracy, Deep Convolutional Neural Network based technique will secure higher standings as the computational capability and dataset management improves. Finally, briefly an attempt at using visualization techniques for borderline cases is discussed.

Digital Chest X-ray with Computer-aided Detection for TB Screening within Correctional Facilities

2021 ◽  
Author(s):  
Kavindhran Velen ◽  
Farzana Sathar ◽  
Christopher J Hoffmann ◽  
Harry Hausler ◽  
Amanda Fononda ◽  
...  
Keyword(s):  
Correctional Facilities ◽  
Computer Aided Detection ◽  
X Ray ◽  
Computer Aided ◽  
Digital Chest ◽  
Chest X Ray

scholarly journals Factors Associated with Participation in Pulmonary Tuberculosis Screening Using Chest X-Ray among Diabetes Mellitus Type II Patients in Denpasar, Bali, Indonesia

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
I Gusti Ngurah Edi Putra ◽  
Putu Ayu Swandewi Astuti ◽  
I Ketut Suarjana ◽  
Ketut Hari Mulyawan ◽  
I Made Kerta Duana ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Health Insurance ◽  
Pulmonary Tuberculosis ◽  
Health Provider ◽  
Type Ii ◽  
Cross Sectional ◽  
Pulmonary Tb ◽  
X Ray ◽  
Factors Associated ◽  
Chest X Ray

Diabetes mellitus (DM) increases the risk of developing pulmonary tuberculosis (TB) disease. Therefore, pulmonary TB screening among DM patients is essential. This study aimed to identify factors associated with participation of DM type II patients in pulmonary TB screening using chest X-ray. This was a cross-sectional analytic study and was part of TB-DM screening study in Denpasar, Bali, Indonesia. The sample consisted of 365 DM type II patients selected by quota sampling among DM type II patients joining the screening program from January until March 2016 in 11 public health centres in Denpasar. Data were collected via structured interviews. The contributing factors were determined by modified Poisson regression test for cross-sectional data. From the findings, less than half (45.48%) of DM type II patients participated in chest X-ray examination for TB. Factors associated with participation in pulmonary TB screening were having a higher educational level [APR = 1.34, 95% CI (1.07–1.67)], having family member who developed pulmonary TB disease [APR = 1.47, 95% CI (1.12–1.93)], the travel time to referral hospital for screening being ≤ 15 minutes [APR = 1.6, 95% CI (1.26–2.03)], having health insurance [APR = 2.69, 95% CI (1.10–6.56)], and receiving good support from health provider [APR = 1.35, 95% CI (1.06–1.70)]. Therefore, training for health provider on providing counselling, involvement of family members in screening process, and improving the health insurance coverage and referral system are worth considering.

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

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e106381 ◽  
Author(s):  
Marianne Breuninger ◽  
Bram van Ginneken ◽  
Rick H. H. M. Philipsen ◽  
Francis Mhimbira ◽  
Jerry J. Hella ◽  
...  
Keyword(s):  
Pulmonary Tuberculosis ◽  
Diagnostic Accuracy ◽  
Validation Study ◽  
Chest Radiographs ◽  
Sub Saharan Africa ◽  
Computer Aided Detection ◽  
Computer Aided ◽  
Sub Saharan

Computer-aided detection (CAD) of solid pulmonary nodules in chest x-ray equivalent ultralow dose chest CT - first in-vivo results at dose levels of 0.13 mSv

2016 ◽  
Vol 85 (12) ◽  
pp. 2217-2224 ◽  
Author(s):  
Michael Messerli ◽  
Thomas Kluckert ◽  
Meinhard Knitel ◽  
Fabian Rengier ◽  
René Warschkow ◽  
...  
Keyword(s):  
Pulmonary Nodules ◽  
Chest Ct ◽  
Computer Aided Detection ◽  
Ultralow Dose ◽  
X Ray ◽  
Computer Aided ◽  
Chest X Ray ◽  
Dose Levels

scholarly journals Comparison of Chest X-Ray Findings Between Primary and Secondary Multidrug Resistant Pulmonary Tuberculosis

2021 ◽  
Vol 5 (10) ◽  
pp. 903-910
Author(s):  
Ricky Septafianty ◽  
Anita Widyoningroem ◽  
M. Yamin S. S ◽  
Rosy Setiawati ◽  
Soedarsono
Keyword(s):  
Pulmonary Tuberculosis ◽  
Multidrug Resistant ◽  
X Rays ◽  
Pulmonary Tb ◽  
X Ray ◽  
Pleural Thickening ◽  
Significant Difference ◽  
Record Data ◽  
Mdr Tb ◽  
Chest X Ray

Introduction: Radiological imaging has a key role in multidrug-resistant (MDR) pulmonary tuberculosis (TB) screening and diagnosis. However, new cases of MDR pulmonary TB are often overlooked; therefore, its transmission might continue before its diagnosis. The most widely used and affordable radiological modality is a chest radiograph. This study aims to describe the characteristics of primary and secondary MDR pulmonary TB chest x-ray findings for differential diagnosis. Methods: This study was an analytic observational study with a retrospective design. Researchers evaluated medical record data of primary and secondary MDR pulmonary TB patients who underwent chest x-ray examinations. The patient's chest x-rays were then evaluated. Evaluated variables were lung, pleural, and mediastinal abnormalities and severity category. Results: The most common chest x-ray finding in primary MDR pulmonary TB was consolidation (96.2%), which was mostly unilateral (52.0%), accompanied by cavities (71.2%), most of which were multiple (83.8%) with a moderate category of severity. The most common chest x-ray finding in secondary MDR pulmonary TB was consolidation (100%), which was mostly bilateral (60.4%), accompanied by cavities (80.2%), most of which were multiple (90.1%) with severe category of severity. Pleural thickening (47.5%) was also found. Conclusion: There was a significant difference between primary and secondary MDR pulmonary TB in terms of mild severity category, and pleural thickening. Mild severity category is mostly found in primary MDR-TB and pleural thickening is mostly found in secondary TB.

scholarly journals Evaluation of chest X-Ray with automated interpretation algorithms for mass tuberculosis screening in prisons.

2021 ◽  
Author(s):  
Thiego Ramon Soares ◽  
Roberto Dias de Oliveira ◽  
Yiran E. Liu ◽  
Andrea da Silva Santos ◽  
Paulo C.P. Santos ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
World Health ◽  
Tuberculosis Screening ◽  
X Ray ◽  
Product Profile ◽  
Screening Study ◽  
Chest X Ray ◽  
Quantitative Results ◽  
Health Organization ◽  
The Relationship

Rationale: The World Health Organization (WHO) recommends systematic tuberculosis (TB) screening in prisons. Evidence is lacking for accurate and scalable screening approaches in this setting. Objectives: To assess the diagnostic accuracy of artificial intelligence-based chest x-ray interpretation algorithms for TB screening in prisons. Methods: Prospective TB screening study in three prisons in Brazil from October 2017 to December 2019. We administered a standardized questionnaire, performed chest x-ray in a mobile unit, and collected sputum for confirmatory testing using Xpert MTB/RIF and culture. We evaluated x-ray images using three algorithms (CAD4TB version 6, LunitTB and qXR) and compared their diagnostic accuracy. We utilized multivariable logistic regression to assess the effect of demographic and clinical characteristics on algorithm accuracy. Finally, we investigated the relationship between abnormality scores and Xpert semi-quantitative results. Measurements and Main Results: Among 2,075 incarcerated individuals, 259 (12.5%) had confirmed TB. All three algorithms performed similarly overall with AUCs of 0.87-0.91. At 90% sensitivity, only LunitTB and qXR met the WHO Target Product Profile requirements for a triage test, with specificity of 84% and 74%, respectively. All algorithms had variable performance by age, prior TB, smoking, and presence of TB symptoms. LunitTB was the most robust to this heterogeneity, but nonetheless failed to meet the TPP for individuals with previous TB. Abnormality scores of all three algorithms were significantly correlated with sputum bacillary load. Conclusions: Automated x-ray interpretation algorithms can be an effective triage tool for TB screening in prisons. However, their specificity is insufficient in individuals with previous TB.

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