PUBLICATIONS

Abstract

Detecting drug-resistant tuberculosis in chest radiographs.


Jaeger S, Juarez-Espinosa O, Candemir S, Poostchi Mohammadabadi M, Yang F, Kim L, Ding M, Folio L, Antani SK, Gabrielian A, Hurt D, Rosenthal A, Thoma GR

International Journal of Computer Assisted Radiology and Surgery https://doi.org/10.1007/s11548-018-1857-9

Abstract:

Purpose Tuberculosis is a major global health threat claiming millions of lives each year. While the total number of tuberculosis cases has been decreasing over the last years, the rise of drug-resistant tuberculosis has reduced the chance of controlling the disease. The purpose is to implement a timely diagnosis of drug-resistant tuberculosis, which is essential to administering adequate treatment regimens and stopping the further transmission of drug-resistant tuberculosis. Methods A main tool for diagnosing tuberculosis is the conventional chest X-ray. We are investigating the possibility of discriminating automatically between drug-resistant and drug-sensitive tuberculosis in chest X-rays by means of image analysis and machine learning methods. Results For discriminating between drug-sensitive and drug-resistant tuberculosis, we achieve an area under the receiver operating characteristic curve (AUC) of up to 66%, using an artificial neural network in combination with a set of shape and texture features. We did not observe any significant difference in the results when including follow-up X-rays for each patient. Conclusion Our results suggest that a chest X-ray contains information about the likelihood of a drug-resistant tuberculosis infection, which can be exploited computationally. We therefore suggest to repeat the experiments of our pilot study on a larger set of chest X-rays


Jaeger S, Juarez-Espinosa O, Candemir S, Poostchi Mohammadabadi M, Yang F, Kim L, Ding M, Folio L, Antani SK, Gabrielian A, Hurt D, Rosenthal A, Thoma GR Detecting drug-resistant tuberculosis in chest radiographs. 
International Journal of Computer Assisted Radiology and Surgery https://doi.org/10.1007/s11548-018-1857-9

URL: https://link.springer.com/content/pdf/10.1007%2Fs11548-018-1857-9.pdf