Abstract: The well-documented global shortage of radiologists is most acutely manifested in countries where the rapid rise of a middle class has created a new capacity to produce imaging studies at a rate which far exceeds the time required to train experts capable of interpreting such studies. The production to interpretation gap is seen clearly in the case of the most common of imaging studies: the chest x-ray, where technicians are increasingly called upon to not only acquire the image, but also to interpret it. The dearth of expert radiologists leads to both delayed and inaccurate diagnostic insights. The present study utilizes a robust radiology database, machine-learning technologies, and robust clinical validation to produce expert-level automatic interpretation of routine chest x-rays. Using a convolutional neural network (CNN) we achieve a performance which is slightly higher than radiologists in the detection of four common chest X-ray (CXR) findings which include focal lung opacities, diffuse lung opacity, cardiomegaly, and abnormal hilar prominence. The agreement of \algoname \space vs. radiologists is slightly higher (1-7\%) than the agreement among a team of three expert radiologists.
Keywords: CNN, Deep Learning, Medical Imaging, Algorithms, Computer Aided Diagnosis, CAD, Chest X-ray, CXR
Author Affiliation: Zebra Medical Vision, Rabin Medical Center