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 PCXRimages, 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 .
Several reasons explain the significant role that chest X-rays play on supporting clinical analysis and early disease detection in pediatric patients, such as low cost, high resolution, low radiation levels , and high availability. In the last decade , Deep Learning (DL) has been given special attention from the computer-aided diagnosis research community, outperforming the state of the art of many techniques, including those applied to pediatric chest X-rays (PCXR). Due to this increasing interest, much high-quality secondary research has also arisen, overviewing machine learning and DL algorithms on medical imaging and PCXR, in particular. However, these secondary studies follow different guidelines, hampering their reproduction or improvement by third-parties regarding the identified trends and gaps. This paper proposes a "deep radiography" of primary research on DL techniques applied in PCXR images. We elaborated on a Systematic Literature Mapping (SLM) protocol, including automatic search on six sources for studies published from January 1 , 2010, to May 20, 2020, and selection criteria utilized on a hundred research papers. As a result, this paper categorizes twenty-six relevant studies and provides a research agenda highlighting limitations, gaps, and trends for further investigations on DL usage in PCXR images. Besides the fact that there is no systematic mapping study on this research topic, to the best of authors' knowledge, this URLanizes the process of finding and selecting relevant studies and data gathering and synthesis in a reproducible way .
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Despite advances in the acquisition of medical imaging and computer-aided support techniques, x-rays due to their
Several reasons explain the significant role that chest X-rays play on supporting clinical analysis and early disease detection in pediatric patients, such as
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availability and
resolution,
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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
, and high availability. In
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as a viable alternative, deep learning
, Deep Learning
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, 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
has been given special attention from the computer-aided diagnosis research community, outperforming the state of the art of
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radiography
X-rays
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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 PCXRimages, we propose in this article
Due to this increasing interest, much high-quality secondary research has also arisen, overviewing machine learning and DL algorithms on medical imaging and PCXR, in particular. However, these secondary studies follow different guidelines, hampering their reproduction or improvement by third-parties regarding the identified trends and gaps. This paper proposes
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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
primary research on
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to PCXR images
in PCXR images. We elaborated on a Systematic Literature Mapping (SLM) protocol, including automatic search on six sources for studies published from January 1
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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
2010, to May 20, 2020, and selection criteria utilized on a hundred research papers. As a result, this paper categorizes twenty-six relevant studies and provides a research agenda highlighting limitations, gaps, and trends for further investigations on DL usage in PCXR images. Besides the fact that there is no systematic mapping study on this research topic, to the best of authors' knowledge, this URLanizes the process of finding and selecting relevant studies and data gathering and synthesis in a reproducible way