Improving Tuberculosis (TB) Prediction using Synthetically Generated Computed Tomography (CT) Images

Abstract

The evaluation of infectious disease processes on radiologic images is an important and challenging task in medical image analysis. Pulmonary infections can often be best imaged and evaluated through computed tomography (CT) scans, which are often not available in low-resource environments and difficult to obtain for critically ill patients. On the other hand, X-ray, a different type of imaging procedure, is inexpensive, often available at the bedside and more widely available, but offers a simpler, two dimensional image. We show that by relying on a model that learns to generate CT images from X-rays synthetically, we can improve the automatic disease classification accuracy and provide clinicians with a different look at the pulmonary disease process. Specifically, we investigate Tuberculosis (TB), a deadly bacterial infectious disease that predominantly affects the lungs, but also other organ systems. We show that relying on synthetically generated CT improves TB identification by 7.50% and distinguishes TB properties up to 12.16% better than the X-ray baseline.

Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review

“…CXR data may be drawn upon by multimodal deep learning models and combined with other modalities such as ECGs to better predict specific disease states [115,116]. Early work has even shown that two-dimensional CXRs can be used to reconstruct three-dimensional CT images and improve pathology detection and classification efforts [117]. Interpretation automation may benefit patients in communities lacking radiologist expertise and where investigations presently go unreported.…”