Literature DB >> 29860192

Effectiveness of bone suppression imaging in the diagnosis of tuberculosis from chest radiographs in Vietnam: An observer study.

Naoki Kodama1, Thai Van Loc2, Phan Thanh Hai2, Nguyen Van Cong2, Shinsuke Katsuhara3, Satoshi Kasai3, Aziz Sheikh4.   

Abstract

OBJECTIVE: To assess the effectiveness of bone suppression imaging (BSI) in the diagnosis of tuberculosis from chest radiographs (CXRs) in Vietnam.
MATERIALS AND METHODS: A total of 97 images (50 tuberculosis and 47 normal) comprised the dataset for this observer study with unanimous consensus of a panel of 3 expert radiologists. The participants were 9 Vietnamese radiologists (6 chest radiologists and 3 non-chest radiologists). Participants recorded their confidence levels regarding the presence of tuberculosis after reading a standard chest radiograph directly first and then after BSI processing. Receiver operating characteristic (ROC) analysis was used to evaluate participant performance. In addition, the change in participants' decision regarding the presence or absence of tuberculosis after BSI processing was recorded for each patient. Improvements in sensitivity and specificity were calculated.
RESULTS: The average AUC for non-chest radiologists improved from 0.882 without BSI to 0.933 with BSI (P = 0.048). In addition, BSI improved sensitivity by 10.0% whereas specificity decreased by 2.8% among non-chest radiologists.
CONCLUSION: Using BSI improved the accuracy of tuberculosis diagnosis from CXRs, particularly by non-chest radiologists.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Bone suppression; Chest radiograph; Infectious disease; Southeast Asia; Tuberculosis; Vietnam

Mesh:

Year:  2018        PMID: 29860192     DOI: 10.1016/j.clinimag.2018.05.021

Source DB:  PubMed          Journal:  Clin Imaging        ISSN: 0899-7071            Impact factor:   1.605


  1 in total

1.  DeBoNet: A deep bone suppression model ensemble to improve disease detection in chest radiographs.

Authors:  Sivaramakrishnan Rajaraman; Gregg Cohen; Lillian Spear; Les Folio; Sameer Antani
Journal:  PLoS One       Date:  2022-03-31       Impact factor: 3.240

  1 in total

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