Pediatric Chest Radiography Research Agenda: Is Deep Learning Still in Childhood?

Despite advances in the acquisition of medical imaging and computer-aided support techniques, x-rays due to their low cost, high availability and low radiation levels are still an important diagnostic procedure, constituting the most frequently performed radiographic examination in pediatric patients for disease investigation while researchers are looking for increasingly efficient techniques to support decision-making. Emerging in the last decade as a viable alternative, deep learning (DL), a technique inspired by neuroscientific and neural connections, has gained much attention from researchers and made significant advances in the field of medical imaging, outperformed the state-of-art of many techniques, including those applied to pediatric chest radiography (PCXR). Given the scenario and considering the fact that, as far as we know, there is still no mapping study on the application of deep learning techniques in PCXR images, we propose in this article a "deep radiography" of the last decade in this research topic and a preliminary research agenda that deals with the state of the art of applying DL on PCXR that constitute a collaborative tool for future researchers. Our goal is to identify primary studies and support the process of choosing and developing DL techniques applied to PCXR images, in addition to pointing out gaps and trends by drawing up a preliminary research agenda. A protocol is described in each phase detailing criteria used from selection to extraction and our set of selected studies is subjected to careful analysis to respond to the research form. Six basic sources were used and the synthesis, results, limitations, and conclusions are exposed.