*The New Landscape of Diagnostic Imaging with the Incorporation of Computer Vision DOI: http://dx.doi.org/10.5772/intechopen.110133*

the structures in the area will absorb the rays differentially, which will result in lights and shadows [31]. In the AI field of computer vision applied to X-ray, there is a preponderance of work in the area of the thoracic cavity [32]. Thus, we found work focused on the detection of pulmonary nodules with models trained on images from one pool of patients and tested in a different pool. We also found longitudinal work, where the model was trained and tested on images from the same patients, with images separated by a time window [32–34]. Another large part of the work focused on the detection of pneumonia. Several models were trained on datasets from different hospitals, which showed variations in various image features between hospitals. As expected, the models showed better metrics when trained and tested on data from the same hospital [32, 35–37]. With the advent of COVID-19, there was an explosion of research in the detection of this pathology in X-ray images. Thus, numerous models were created that attempted to distinguish COVID-19 pneumonia from viral or bacterial pneumonia. These developments were key since they allowed screening and managing patients automatically and to avoid spreading the contagion of COVID-19 patients [32, 38–41]. Work was also carried out to contribute to the detection of tuberculosis in chest images. These models demonstrated satisfactory performance in screening tuberculosis images with respect to normal lungs or other pulmonary pathologies. However, the models did not show the ability to distinguish between active and quiescent disease [32, 42, 43]. Additionally, part of the research was also directed to the detection of pneumothorax. This part of the development was of important value in patient triaging, especially in determining the size and position of the pneumothorax and its changes over time in the same patient. Several of these models have already received FDA clearance as assistive devices in the emergency unit [32, 44–46]. As a final part of this section, to a lesser extent than the previous ones, models were also built for the detection of other types of pulmonary involvement, such as consolidation, edema, emphysema, fibrosis, and pleural effusion [32, 47].
