Dataset Information

Chest Radiographic Patterns and the Transmission of Tuberculosis: Implications for Automated Systems.

ABSTRACT:

Background

Computer-aided detection to identify and diagnose pulmonary tuberculosis is being explored. While both cavitation on chest radiograph and smear-positivity on microscopy are independent risk factors for the infectiousness of pulmonary tuberculosis it is unknown which radiographic pattern, were it detectable, would provide the greatest public health benefit; i.e. reduced transmission. Herein we provide that evidence.

Objectives

1) to determine whether pulmonary tuberculosis in a high income, low incidence country is more likely to present with "typical" adult-type pulmonary tuberculosis radiographic features and 2) to determine whether those with "typical" radiographic features are more likely than those without such features to transmit the organism and/or cause secondary cases.

Methods

Over a three-year period beginning January 1, 2006 consecutive adults with smear-positive pulmonary tuberculosis in the Province of Alberta, Canada, were identified and their pre-treatment radiographs scored by three independent readers as "typical" (having an upper lung zone predominant infiltrate, with or without cavitation but no discernable adenopathy) or "atypical" (all others). Each patient's pre-treatment bacillary burden was carefully documented and, during a 30-month transmission window, each patient's transmission events were recorded. Mycobacteriology, radiology and transmission were compared in those with "typical" versus "atypical" radiographs.

Findings

A total of 97 smear-positive pulmonary tuberculosis cases were identified, 69 (71.1%) with and 28 (28.9%) without "typical" chest radiographs. "Typical" cases were more likely to have high bacillary burdens and cavitation (Odds Ratios and 95% Confidence Intervals: 2.75 [1.04-7.31] and 9.10 [2.51-32.94], respectively). Typical cases were also responsible for most transmission events-78% of tuberculin skin test conversions (p<0.002) and 95% of secondary cases in reported close contacts (p<0.01); 94% of secondary cases in "unreported" contacts (p<0.02).

Conclusion

As a group, smear-positive pulmonary tuberculosis patients with typical radiographic features constitute the greatest public health risk. This may have implications for automated detection systems.

SUBMITTER: Lau A

PROVIDER: S-EPMC4841548 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Chest Radiographic Patterns and the Transmission of Tuberculosis: Implications for Automated Systems.

Lau Angela A Barrie James J Winter Christopher C Elamy Abdel-Halim AH Tyrrell Gregory G Long Richard R

PloS one 20160422 4

<h4>Background</h4>Computer-aided detection to identify and diagnose pulmonary tuberculosis is being explored. While both cavitation on chest radiograph and smear-positivity on microscopy are independent risk factors for the infectiousness of pulmonary tuberculosis it is unknown which radiographic pattern, were it detectable, would provide the greatest public health benefit; i.e. reduced transmission. Herein we provide that evidence.<h4>Objectives</h4>1) to determine whether pulmonary tuberculos ...[more]

PMID: 27105337

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Project description:PurposeTo study the extent of pulmonary involvement in coronavirus 19 (COVID-19) with quantitative CT and to assess the impact of disease burden on opacity visibility on chest radiographs.Materials and methodsThis retrospective study included 20 pairs of CT scans and same-day chest radiographs from 17 patients with COVID-19, along with 20 chest radiographs of controls. All pulmonary opacities were semiautomatically segmented on CT images, producing an anteroposterior projection image to match the corresponding frontal chest radiograph. The quantitative CT lung opacification mass (QCTmass) was defined as (opacity attenuation value + 1000 HU)/1000 × 1.065 (g/mL) × combined volume (cm3) of the individual opacities. Eight thoracic radiologists reviewed the 40 radiographs, and a receiver operating characteristic curve analysis was performed for the detection of lung opacities. Logistic regression analysis was performed to identify factors affecting opacity visibility on chest radiographs.ResultsThe mean QCTmass per patient was 72.4 g ± 120.8 (range, 0.7-420.7 g), and opacities occupied 3.2% ± 5.8 (range, 0.1%-19.8%) and 13.9% ± 18.0 (range, 0.5%-57.8%) of the lung area on the CT images and projected images, respectively. The radiographs had a median sensitivity of 25% and specificity of 90% among radiologists. Nineteen of 186 opacities were visible on chest radiographs, and a median area of 55.8% of the projected images was identifiable on radiographs. Logistic regression analysis showed that QCTmass (P < .001) and combined opacity volume (P < .001) significantly affected opacity visibility on radiographs.ConclusionQCTmass varied among patients with COVID-19. Chest radiographs had high specificity for detecting lung opacities in COVID-19 but a low sensitivity. QCTmass and combined opacity volume were significant determinants of opacity visibility on radiographs.Earlier incorrect version appeared online. This article was corrected on April 6, 2020 and December 14, 2020.Supplemental material is available for this article.© RSNA, 2020.

Project description:ObjectivesNew chest compression detection technology allows for the recording and graphical depiction of clinical cardiopulmonary resuscitation (CPR) chest compressions. The authors sought to determine the inter-rater reliability of chest compression pattern classifications by human raters. Agreement with automated chest compression classification was also evaluated by computer analysis.MethodsThis was an analysis of chest compression patterns from cardiac arrest patients enrolled in the ongoing Resuscitation Outcomes Consortium (ROC) Continuous Chest Compressions Trial. Thirty CPR process files from patients in the trial were selected. Using written guidelines, research coordinators from each of eight participating ROC sites classified each chest compression pattern as 30:2 chest compressions, continuous chest compressions (CCC), or indeterminate. A computer algorithm for automated chest compression classification was also developed for each case. Inter-rater agreement between manual classifications was tested using Fleiss's kappa. The criterion standard was defined as the classification assigned by the majority of manual raters. Agreement between the automated classification and the criterion standard manual classifications was also tested.ResultsThe majority of the eight raters classified 12 chest compression patterns as 30:2, 12 as CCC, and six as indeterminate. Inter-rater agreement between manual classifications of chest compression patterns was κ = 0.62 (95% confidence interval [CI] = 0.49 to 0.74). The automated computer algorithm classified chest compression patterns as 30:2 (n = 15), CCC (n = 12), and indeterminate (n = 3). Agreement between automated and criterion standard manual classifications was κ = 0.84 (95% CI = 0.59 to 0.95).ConclusionsIn this study, good inter-rater agreement in the manual classification of CPR chest compression patterns was observed. Automated classification showed strong agreement with human ratings. These observations support the consistency of manual CPR pattern classification as well as the use of automated approaches to chest compression pattern analysis.

Dataset Information

Chest Radiographic Patterns and the Transmission of Tuberculosis: Implications for Automated Systems.

Background

Objectives

Methods

Findings

Conclusion

Publications

Chest Radiographic Patterns and the Transmission of Tuberculosis: Implications for Automated Systems.

Similar Datasets

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