An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information

Melendez, Jaime; Sánchez, Clara I.; Philipsen, Rick H. H. M.; Maduskar, Pragnya; Dawson, Rodney; Theron, Grant; Dheda, Keertan; Ginneken, Bram van

doi:10.1038/srep25265

Cited by 120 publications

(97 citation statements)

References 19 publications

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“…An efficacious automated and cost-effective method could aid screening evaluation efforts in developing nations and facilitate earlier detection of disease. Therefore, there has been an interest in the use of computer-aided diagnosis for detection of pulmonary TB at chest radiography, with multiple approaches proposed (4,6,7).…”

Section: Implication For Patient Carementioning

confidence: 99%

Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks

2017

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Purpose To evaluate the efficacy of deep convolutional neural networks (DCNNs) for detecting tuberculosis (TB) on chest radiographs. Materials and Methods Four deidentified HIPAA-compliant datasets were used in this study that were exempted from review by the institutional review board, which consisted of 1007 posteroanterior chest radiographs. The datasets were split into training (68.0%), validation (17.1%), and test (14.9%). Two different DCNNs, AlexNet and GoogLeNet, were used to classify the images as having manifestations of pulmonary TB or as healthy. Both untrained and pretrained networks on ImageNet were used, and augmentation with multiple preprocessing techniques. Ensembles were performed on the best-performing algorithms. For cases where the classifiers were in disagreement, an independent board-certified cardiothoracic radiologist blindly interpreted the images to evaluate a potential radiologist-augmented workflow. Receiver operating characteristic curves and areas under the curve (AUCs) were used to assess model performance by using the DeLong method for statistical comparison of receiver operating characteristic curves. Results The best-performing classifier had an AUC of 0.99, which was an ensemble of the AlexNet and GoogLeNet DCNNs. The AUCs of the pretrained models were greater than that of the untrained models (P < .001). Augmenting the dataset further increased accuracy (P values for AlexNet and GoogLeNet were .03 and .02, respectively). The DCNNs had disagreement in 13 of the 150 test cases, which were blindly reviewed by a cardiothoracic radiologist, who correctly interpreted all 13 cases (100%). This radiologist-augmented approach resulted in a sensitivity of 97.3% and specificity 100%. Conclusion Deep learning with DCNNs can accurately classify TB at chest radiography with an AUC of 0.99. A radiologist-augmented approach for cases where there was disagreement among the classifiers further improved accuracy. RSNA, 2017.

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Section: Implication For Patient Carementioning

confidence: 99%

Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks

2017

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“…In this study we evaluate one such software platform, CAD4TB v6, developed in association with Radboud University Medical Center, the Netherlands. The CAD4TB software is distributed by Delft Imaging Systems and is already in use in numerous settings worldwide where its performance has been previously studied 3,[9][10][11][12][13] . In 2018 version 6 of the software was released, the first version to use deep-learning technology.…”

Section: Introductionmentioning

confidence: 99%

Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system

Murphy

Habib

Zaidi

et al. 2020

Sci Rep

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111

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There is a growing interest in the automated analysis of chest X-Ray (CXR) as a sensitive and inexpensive means of screening susceptible populations for pulmonary tuberculosis. In this work we evaluate the latest version of CAD4TB, a software platform designed for this purpose. Version 6 of CAD4TB was released in 2018 and is here tested on an independent dataset of 5565 CXR images with GeneXpert (Xpert) sputum test results available (854 Xpert positive subjects). A subset of 500 subjects (50% Xpert positive) was reviewed and annotated by 5 expert observers independently to obtain a radiological reference standard. The latest version of CAD4TB is found to outperform all previous versions in terms of area under receiver operating curve (ROC) with respect to both Xpert and radiological reference standards. Improvements with respect to Xpert are most apparent at high sensitivity levels with a specificity of 76% obtained at 90% sensitivity. When compared with the radiological reference standard, CAD4TB v6 also outperformed previous versions by a considerable margin and achieved 98% specificity at 90% sensitivity. No substantial difference was found between the performance of CAD4TB v6 and any of the various expert observers against the Xpert reference standard. A cost and efficiency analysis on this dataset demonstrates that in a standard clinical situation, operating at 90% sensitivity, users of CAD4TB v6 can process 132 subjects per day at an average cost per screen of $5.95 per subject, while users of version 3 process only 85 subjects per day at a cost of $8.41 per subject. At all tested operating points version 6 is shown to be more efficient and cost effective than any other version.

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“…Prior studies have reported encouraging results of various DL algorithms for assessment of specific conditions such as pulmonary tuberculosis, cystic fibrosis, lines and tubes (position of peripherally inserted central catheters and endotracheal tubes), pneumoconiosis and lung nodules on CXR [5–16]. Another DL algorithm, now a commercially available application, subtracts ribs from single energy CXR to aid and expedite their interpretation by the radiologists.…”

Section: Introductionmentioning

confidence: 99%

Deep learning in chest radiography: Detection of findings and presence of change

et al. 2018

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BackgroundDeep learning (DL) based solutions have been proposed for interpretation of several imaging modalities including radiography, CT, and MR. For chest radiographs, DL algorithms have found success in the evaluation of abnormalities such as lung nodules, pulmonary tuberculosis, cystic fibrosis, pneumoconiosis, and location of peripherally inserted central catheters. Chest radiography represents the most commonly performed radiological test for a multitude of non-emergent and emergent clinical indications. This study aims to assess accuracy of deep learning (DL) algorithm for detection of abnormalities on routine frontal chest radiographs (CXR), and assessment of stability or change in findings over serial radiographs.Methods and findingsWe processed 874 de-identified frontal CXR from 724 adult patients (> 18 years) with DL (Qure AI). Scores and prediction statistics from DL were generated and recorded for the presence of pulmonary opacities, pleural effusions, hilar prominence, and enlarged cardiac silhouette. To establish a standard of reference (SOR), two thoracic radiologists assessed all CXR for these abnormalities. Four other radiologists (test radiologists), unaware of SOR and DL findings, independently assessed the presence of radiographic abnormalities. A total 724 radiographs were assessed for detection of findings. A subset of 150 radiographs with follow up examinations was used to asses change over time. Data were analyzed with receiver operating characteristics analyses and post-hoc power analysis.ResultsAbout 42% (305/ 724) CXR had no findings according to SOR; single and multiple abnormalities were seen in 23% (168/724) and 35% (251/724) of CXR. There was no statistical difference between DL and SOR for all abnormalities (p = 0.2–0.8). The area under the curve (AUC) for DL and test radiologists ranged between 0.837–0.929 and 0.693–0.923, respectively. DL had lowest AUC (0.758) for assessing changes in pulmonary opacities over follow up CXR. Presence of chest wall implanted devices negatively affected the accuracy of DL algorithm for evaluation of pulmonary and hilar abnormalities.ConclusionsDL algorithm can aid in interpretation of CXR findings and their stability over follow up CXR. However, in its present version, it is unlikely to replace radiologists due to its limited specificity for categorizing specific findings.

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An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information

Cited by 120 publications

References 19 publications

Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks

Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks

Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system

Deep learning in chest radiography: Detection of findings and presence of change

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