1992. Automated Classification of Pulmonary Tuberculosis-Associated Radiograph in the U.S. Hospital-scale Chest X-ray Database by using Deep Convolutional Neural Network
Session: Poster Abstract Session: Diagnostics: Bacteria and Mycobacteria
Saturday, October 6, 2018
Room: S Poster Hall
Posters
  • IDWeek-DAC4TBPoster.pdf (586.7 kB)
  • Background:

    Automated classification of chest radiograph (CXR) using deep convolutional neural network (DCCN) has emerged as an attractive option for tuberculosis surveillance and detection. The National Institute of Health (NIH) ChestX-ray8 database comprises 32,717 patients with X-ray images that were interpreted as abnormal based on natural language processing.

    Methods:

    Two de-identified HIPAA-compliant datasets including the NIH ChestX-ray8 database and the National Library of Medicine (NLM) Shenzhen Hospital X-ray set were included in this study.

    Firstly, Shenzhen Hospital X-ray set which consisted of 336 chest radiographs related to TB and 326 normal radiographs were used to develop DCCN. The dataset was split into training (75%), validation (15%), and test (10%). Based on TensorFlow framework, Inception-v3, a novel pre-trained DCCN, was augmented with several techniques to classify an image as having TB characteristics or as healthy. Receiver operating characteristic (ROC) curves and areas under the curve (AUCs) were used to assess model performance.

    Next, 89,845 radiographs (38,086 normal and 51,759 abnormal images) from ChestX-ray8 dataset which comprises 63,061 normal radiographs and 51,759 chest radiographs with one of eight common thoracic abnormality (including atelectasis, cardiomegaly, effusion, infiltration, mass, nodule,pneumonia and pneumothorax) were used to create the second test set to assess external validity of DCCN model. At the end, prevalence of tuberculosis associated radiographs in the U.S. radiograph dataset was calculated by using trained DCCN model.

    Results:

    The final DCCN model had an AUCs of 0.96 to classify TB associated and normal chest radiographs in Shenzhen Hospital X-ray set. However, AUCs of trained DCCN to classify abnormal radiographs in ChestX-ray8 dataset was decreased to 0.54. Lastly, the final DCCN model predicted that there was 36.51% (13,905 of 51,759) of abnormal radiographs in ChestX-ray8 dataset related to tuberculosis.

    Conclusion:

    Our trained DCCN model suggested 36.51% of abnormal chest radiography in the U.S. dataset was associated with TB. However, AUCs of DCCN to classify normal chest radiograph differed upon settings and source of training set. Further researches should focus on improving efficacy of deep learning algorithm across various databases.

    Krit Pongpirul, MD, PhD, Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand, Seelwan Sathitratanacheewin, MD, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand and Panasun Sunanta, BS, Institute for Deep Learning in Medicine, Bangkok, Thailand

    Disclosures:

    K. Pongpirul, None

    S. Sathitratanacheewin, None

    P. Sunanta, None

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