Computer Aided Diagnosis of Tuberculosis Using Digital Chest Radiographs: A Systematic Review

CHEST Journal ◽  
2015 ◽  
Vol 148 (4) ◽  
pp. 135A ◽  
Author(s):  
Tripti Pande ◽  
Chad Cohen ◽  
Madhukar Pai ◽  
Faiz Ahmad Khan
Keyword(s):  
Systematic Review ◽  
Computer Aided Diagnosis ◽  
Chest Radiographs ◽  
Computer Aided ◽  
Digital Chest ◽  
Aided Diagnosis

scholarly journals Computer-aided diagnosis of emphysema in COPD patients: Neural-network-based analysis of lung shape in digital chest radiographs

2007 ◽  
Vol 29 (1) ◽  
pp. 76-86 ◽  
Author(s):  
Giuseppe Coppini ◽  
Massimo Miniati ◽  
Marco Paterni ◽  
Simonetta Monti ◽  
Ezio Maria Ferdeghini
Keyword(s):  
Neural Network ◽  
Computer Aided Diagnosis ◽  
Chest Radiographs ◽  
Copd Patients ◽  
Computer Aided ◽  
Digital Chest ◽  
Aided Diagnosis

Computer-aided diagnosis for detection of cardiomegaly in digital chest radiographs

2005 ◽  
Author(s):  
Takayuki Ishida ◽  
Shigehiko Katsuragawa ◽  
Koichi Chida ◽  
Heber MacMahon ◽  
Kunio Doi
Keyword(s):  
Computer Aided Diagnosis ◽  
Chest Radiographs ◽  
Computer Aided ◽  
Digital Chest ◽  
Aided Diagnosis

Computer-aided Diagnosis for the Detection and Classification of Lung Cancers on Chest Radiographs

2006 ◽  
Vol 13 (8) ◽  
pp. 995-1003 ◽  
Author(s):  
Junji Shiraishi ◽  
Hiroyuki Abe ◽  
Feng Li ◽  
Roger Engelmann ◽  
Heber MacMahon ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Chest Radiographs ◽  
Lung Cancers ◽  
Computer Aided ◽  
Aided Diagnosis

Deep Learning for Computer-aided Diagnosis of Pneumoconiosis

2021 ◽  
Author(s):  
Zheng Wang ◽  
Qingjun Qian ◽  
Jianfang Zhang ◽  
Caihong Duo ◽  
Wen He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Chest Radiographs ◽  
Diagnostic Sensitivity ◽  
Junior Doctors ◽  
Diagnostic Efficiency ◽  
Test Set ◽  
Computer Aided ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Abstract Background: The diagnosis of pneumoconiosis relies primarily on chest radiographs and exhibits significant variability between physicians. Computer-aided diagnosis (CAD) can improve the accuracy and consistency of these diagnoses. However, CAD based on machine learning requires extensive human intervention and time-consuming training. As such, deep learning has become a popular tool for the development of CAD models. In this study, the clinical applicability of CAD based on deep learning was verified for pneumoconiosis patients.Methods: Chest radiographs were collected from 5424 occupational health examiners who met the inclusion criteria. The data were divided into training, validation, and test sets. The CAD algorithm was then trained and applied to processing of the validation set, while the test set was used to evaluate diagnostic efficacy. Three junior and three senior physicians provided independent diagnoses using images from the test set and a comprehensive diagnosis for comparison with the CAD results. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the proposed CAD system. A McNemar test was used to evaluate diagnostic sensitivity and specificity for pneumoconiosis, both before and after the use of CAD. A kappa consistency test was used to evaluate the diagnostic consistency for both the algorithm and the clinicians.Results: ROC results suggested the proposed CAD model achieved high accuracy in the diagnosis of pneumoconiosis, with a kappa value of 0.90. The sensitivity, specificity, and kappa values for the junior doctors increased from 0.86 to 0.98, 0.68 to 0.86, and 0.54 to 0.84, respectively (p<0.05), when CAD was applied. However, metrics for the senior doctors were not significantly different.Conclusion: DL-based CAD can improve the diagnostic sensitivity, specificity, and consistency of pneumoconiosis diagnoses, particularly for junior physicians.

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