The integration of medical images is the process of registering and fusing them to obtain a greater amount of diagnostic information. In this work an analysis is performed for the integration of images obtained through computed axial tomography and magnetic resonance imaging, for which a tool was developed in the Matlab program, where the registration is implemented through equivalent features; in addition, the pairs of images are compared by several fusion rules, with a view to identify the best algorithm in which the resulting fused image contains the most information from the original representations.

In the present research work, we investigated the use of the image intensifier in the extraction of radiopaque foreign bodies in traumatology. First of all, it is necessary to clarify that this method constitutes an essential component of practically generalized use, in which low current level radiation is used, that is, fluoroscopic radiation, so that it can be applied for a considerably longer time than that of the longest radiographic exposure. This tool works with a tube intended for this purpose, which is known as fluoroscopy. The radiations from the tube pass through the patient and reach the serigraph, on which the image intensifier or fluoroscopic screen is mounted. In the latter case, this is where the chain ends, since it is on this screen that the image is formed and where the physician directly observes the region to be studied. It is also necessary to define that a foreign body is any element foreign to the body that enters it, either through the skin or through any natural orifice such as the eyes, nose, throat, preventing its normal functioning. It was possible to obtain as a result that the advantages of fluoroscopic navigation are the reduction of surgical time and the amount of irradiation, which goes from about 140 seconds without navigation to only 8 seconds, which is a substantial difference. Among the conclusions, it was possible to highlight that in the case of a radiopaque object, it is essential to have an image intensifier for localization of the foreign body during surgery; while in the case of a radiolucent foreign body, it is more advisable to locate it through the clinic, since these tend to form granulomas.

The cost of diagnostic errors has been high in the developed world economics according to a number of recent studies and continues to rise. Up till now, a common process of performing image diagnostics for a growing number of conditions has been examination by a single human specialist (i.e., single-channel recognition and classification decision system). Such a system has natural limitations of unmitigated error that can be detected only much later in the treatment cycle, as well as resource intensity and poor ability to scale to the rising demand. At the same time Machine Intelligence (ML, AI) systems, specifically those including deep neural network and large visual domain models have made significant progress in the field of general image recognition, in many instances achieving the level of an average human and in a growing number of cases, a human specialist in the effectiveness of image recognition tasks. The objectives of the AI in Medicine (AIM) program were set to leverage the opportunities and advantages of the rapidly evolving Artificial Intelligence technology to achieve real and measurable gains in public healthcare, in quality, access, public confidence and cost efficiency. The proposal for a collaborative AI-human image diagnostics system falls directly into the scope of this program.