Deep-Learning Based Automated Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs: Diagnostic Performance in Systematic Screening of Asymptomatic Individuals

2019 ◽  
Author(s):  
Jong Hyuk Lee ◽  
Sunggyun Park ◽  
Eui Jin Hwang ◽  
Jin Mo Goo ◽  
Woo Young Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pulmonary Tuberculosis ◽  
Diagnostic Performance ◽  
Detection Algorithm ◽  
Automated Detection ◽  
Chest Radiographs ◽  
Systematic Screening ◽  
Active Pulmonary Tuberculosis ◽  
Asymptomatic Individuals

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.

scholarly journals The Accuracy of Emergency Physicians’ Suspicions of Active Pulmonary Tuberculosis

2021 ◽  
Vol 10 (4) ◽  
pp. 860
Author(s):  
Shiang-Jin Chen ◽  
Chun-Yu Lin ◽  
Tzu-Ling Huang ◽  
Ying-Chi Hsu ◽  
Kuan-Ting Liu
Keyword(s):  
Chronic Kidney Disease ◽  
Weight Loss ◽  
Kidney Disease ◽  
Pulmonary Tuberculosis ◽  
General Ward ◽  
Chest Radiographs ◽  
Emergency Physicians ◽  
Active Pulmonary Tuberculosis ◽  
Factors Associated ◽  
History Of

Objective: To investigate factors associated with recognition and delayed isolation of pulmonary tuberculosis (PTB). Background: Precise identification of PTB in the emergency department (ED) remains challenging. Methods: Retrospectively reviewed PTB suspects admitted via the ED were divided into three groups based on the acid-fast bacilli culture report and whether they were isolated initially in the ED or general ward. Factors related to recognition and delayed isolation were statistically compared. Results: Only 24.94% (100/401) of PTB suspects were truly active PTB and 33.77% (51/151) of active PTB were unrecognized in the ED. Weight loss (p = 0.022), absence of dyspnea (p = 0.021), and left upper lobe field (p = 0.024) lesions on chest radiographs were related to truly active PTB. Malignancy (p = 0.015), chronic kidney disease (p = 0.047), absence of a history of PTB (p = 0.013), and lack of right upper lung (p ≤ 0.001) and left upper lung (p = 0.020) lesions were associated with PTB being missed in the ED. Conclusions: Weight loss, absence of dyspnea, and left upper lobe field lesions on chest radiographs were related to truly active PTB. Malignancy, chronic kidney disease, absence of a history of PTB, and absence of right and/or left upper lung lesions on chest radiography were associated with isolation delay.

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

Automated detection algorithm for C4d immunostaining showed comparable diagnostic performance to pathologists in renal allograft biopsy

2020 ◽  
Vol 33 (8) ◽  
pp. 1626-1634
Author(s):  
Gyuheon Choi ◽  
Young-Gon Kim ◽  
Haeyon Cho ◽  
Namkug Kim ◽  
Hyunna Lee ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Renal Allograft ◽  
Detection Algorithm ◽  
Automated Detection ◽  
Allograft Biopsy
Sign in / Sign up

Export Citation Format

Share Document