Deep Learning for Chest Radiographs

2021 ◽  
Keyword(s):  
Deep Learning ◽  
Chest Radiographs

Radiology “forensics”: determination of age and sex from chest radiographs using deep learning

2021 ◽  
Author(s):  
Paul H. Yi ◽  
Jinchi Wei ◽  
Tae Kyung Kim ◽  
Jiwon Shin ◽  
Haris I. Sair ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chest Radiographs ◽  
Age And Sex

scholarly journals Prediction of Pulmonary to Systemic Flow Ratio in Patients With Congenital Heart Disease Using Deep Learning–Based Analysis of Chest Radiographs

2020 ◽  
Vol 5 (4) ◽  
pp. 449 ◽  
Author(s):  
Shuhei Toba ◽  
Yoshihide Mitani ◽  
Noriko Yodoya ◽  
Hiroyuki Ohashi ◽  
Hirofumi Sawada ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Deep Learning ◽  
Congenital Heart ◽  
Chest Radiographs ◽  
Flow Ratio

Extraction of Aortic Knuckle Contour in Chest Radiographs Using Deep Learning

2018 ◽  
Author(s):  
Zhiyun Xue ◽  
Rodney Long ◽  
Stefan Jaeger ◽  
Les Folio ◽  
R. George Thoma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chest Radiographs

scholarly journals Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

PLoS Medicine ◽  
2018 ◽  
Vol 15 (11) ◽  
pp. e1002683 ◽  
Author(s):  
John R. Zech ◽  
Marcus A. Badgeley ◽  
Manway Liu ◽  
Anthony B. Costa ◽  
Joseph J. Titano ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Cross Sectional Study ◽  
Chest Radiographs ◽  
Sectional Study ◽  
Cross Sectional ◽  
Generalization Performance ◽  
Deep Learning Model

Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning

2020 ◽  
Vol 30 (9) ◽  
pp. 4943-4951
Author(s):  
Young-Gon Kim ◽  
Sang Min Lee ◽  
Kyung Hee Lee ◽  
Ryoungwoo Jang ◽  
Joon Beom Seo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lung Nodule ◽  
Chest Radiographs ◽  
Computer Aided Detection ◽  
Matrix Size ◽  
Computer Aided

scholarly journals Comparing a deep learning model's diagnostic performance to that of radiologists to detect Covid -19 features on chest radiographs

2021 ◽  
Vol 31 (5) ◽  
pp. 53
Author(s):  
Sabitha Krishnamoorthy ◽  
Manju Chandran ◽  
Sudhakar Ramakrishnan ◽  
LansonBrijesh Colaco ◽  
Akshay Dias ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Chest Radiographs

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.

Bone Suppression on Pediatric Chest Radiographs via a Deep Learning-Based Cascade Model

2022 ◽  
pp. 106627
Author(s):  
Kyungjin Cho ◽  
Jiyeon Seo ◽  
Mingyu Kim ◽  
Gil-Sun Hong ◽  
Namkug Kim
Keyword(s):  
Deep Learning ◽  
Cascade Model ◽  
Chest Radiographs ◽  
Pediatric Chest

scholarly journals Development and Validation of a Deep Learning–based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs

2018 ◽  
Vol 69 (5) ◽  
pp. 739-747 ◽  
Author(s):  
Eui Jin Hwang ◽  
Sunggyun Park ◽  
Kwang-Nam Jin ◽  
Jung Im Kim ◽  
So Young Choi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pulmonary Tuberculosis ◽  
Performance Test ◽  
Automatic Detection ◽  
High Sensitivity ◽  
High Specificity ◽  
Roc Curves ◽  
Chest Radiographs ◽  
Automated System ◽  
Active Pulmonary Tuberculosis

Abstract Background Detection of active pulmonary tuberculosis on chest radiographs (CRs) is critical for the diagnosis and screening of tuberculosis. An automated system may help streamline the tuberculosis screening process and improve diagnostic performance. Methods We developed a deep learning–based automatic detection (DLAD) algorithm using 54c221 normal CRs and 6768 CRs with active pulmonary tuberculosis that were labeled and annotated by 13 board-certified radiologists. The performance of DLAD was validated using 6 external multicenter, multinational datasets. To compare the performances of DLAD with physicians, an observer performance test was conducted by 15 physicians including nonradiology physicians, board-certified radiologists, and thoracic radiologists. Image-wise classification and lesion-wise localization performances were measured using area under the receiver operating characteristic (ROC) curves and area under the alternative free-response ROC curves, respectively. Sensitivities and specificities of DLAD were calculated using 2 cutoffs (high sensitivity [98%] and high specificity [98%]) obtained through in-house validation. Results DLAD demonstrated classification performance of 0.977–1.000 and localization performance of 0.973–1.000. Sensitivities and specificities for classification were 94.3%–100% and 91.1%–100% using the high-sensitivity cutoff and 84.1%–99.0% and 99.1%–100% using the high-specificity cutoff. DLAD showed significantly higher performance in both classification (0.993 vs 0.746–0.971) and localization (0.993 vs 0.664–0.925) compared to all groups of physicians. Conclusions Our DLAD demonstrated excellent and consistent performance in the detection of active pulmonary tuberculosis on CR, outperforming physicians, including thoracic radiologists.

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