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 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.

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

Symptom and digital chest X-ray TB screening in South African prisons: yield and cost-effectiveness

2020 ◽  
Vol 24 (3) ◽  
pp. 295-302 ◽  
Author(s):  
H-Y. Kim ◽  
V. Zishiri ◽  
L. Page-Shipp ◽  
S. Makgopa ◽  
G. J. Churchyard ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
South African ◽  
Correctional Facilities ◽  
Cost Effectiveness Ratio ◽  
X Ray ◽  
Symptom Screening ◽  
Night Sweats ◽  
Digital Chest ◽  
Chest X Ray ◽  
Optimal Approach

BACKGROUND: Correctional inmates are at a high risk of tuberculosis (TB). The optimal approach to screening this population is unclear.METHODS: We retrospectively reviewed records from TB screening in 64 correctional facilities in South Africa between January 2015 and July 2016. Inmates received symptom screening (any of cough, fever, weight loss, or night sweats) combined with digital chest X-ray (CXR), when available. CXRs were assessed as ‘abnormal' or with no abnormalities. Inmates with either a symptom or an ‘abnormal' CXR were asked to provide a single spot sputum for Xpert® MTB/RIF testing. We estimated the incremental cost-effectiveness ratio (ICER) per additional TB case detected using CXR screening among asymptomatic inmates.RESULTS: Of 61 580 inmates, CXR screening was available for 41 852. Of these, 19 711 (47.1%) had TB symptoms. Among 22 141 inmates without symptoms, 1939/19 783 (9.8%) had an abnormal CXR, and 8 (1.2%) were Xpert-positive among those with Xpert tests done. Of 14 942 who received symptom screening only and had symptoms, 84% (12 616) had an Xpert result, and 105 (0.8%) were positive. The ICER for CXR screening was US$22 278.CONCLUSION: Having CXR in addition to symptom screening increased yield but added considerable cost. A major limitation of screening was the low specificity of the symptom screen.

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 Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mu Sook Lee ◽  
Yong Soo Kim ◽  
Minki Kim ◽  
Muhammad Usman ◽  
Shi Sub Byon ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Absolute Error ◽  
Training Dataset ◽  
Computer Aided Detection ◽  
Test Dataset ◽  
Cardiothoracic Ratio ◽  
Computer Aided ◽  
Chest X Ray ◽  
Public Datasets

AbstractWe examined the feasibility of explainable computer-aided detection of cardiomegaly in routine clinical practice using segmentation-based methods. Overall, 793 retrospectively acquired posterior–anterior (PA) chest X-ray images (CXRs) of 793 patients were used to train deep learning (DL) models for lung and heart segmentation. The training dataset included PA CXRs from two public datasets and in-house PA CXRs. Two fully automated segmentation-based methods using state-of-the-art DL models for lung and heart segmentation were developed. The diagnostic performance was assessed and the reliability of the automatic cardiothoracic ratio (CTR) calculation was determined using the mean absolute error and paired t-test. The effects of thoracic pathological conditions on performance were assessed using subgroup analysis. One thousand PA CXRs of 1000 patients (480 men, 520 women; mean age 63 ± 23 years) were included. The CTR values derived from the DL models and diagnostic performance exhibited excellent agreement with reference standards for the whole test dataset. Performance of segmentation-based methods differed based on thoracic conditions. When tested using CXRs with lesions obscuring heart borders, the performance was lower than that for other thoracic pathological findings. Thus, segmentation-based methods using DL could detect cardiomegaly; however, the feasibility of computer-aided detection of cardiomegaly without human intervention was limited.

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