**8.2 Computed tomography**

Computed tomography (CT) integrates many X-ray images taken from different angles thanks to the high-speed rotating platform that rotates on the same axis where the patient lies. The type of images it produces is cross-sectional [1]. Using AI computer vision techniques, it is possible to operate directly on a fixed plane (one section) or to use complete volumes (several consecutive sections). Most of the research in this area is classification (about 36%), followed by segmentation (27%), detection (22%), and others (15%) [30]. Broadly speaking we can list the works in this area in the identification of organs (kidney [48], liver [49, 50], lungs [51, 52], and heart [53, 54]) and in the identification of substructures or lesions (artery calcification [55], nodules [56], polyps [57, 58], and lymph nodes [59–61]). Among the most commonly used measures to report the performance of the different models are accuracy, sensitivity, specificity, AUC-ROC, and F1 score [1]. The processing of the images as input is also diverse. It is possible to use 3-dimensional inputs, that is, several consecutive slices that form a volume. Projection methods, such as maximum intensity projection, can also be used to transform a 3-dimensional input into a 2-dimensional one [1, 62].

#### **8.3 Positron emission tomography**

Positron emission tomography (PET) is a technique that allows the observation of metabolic processes in different tissues of the patient's body. Radiolabeled compounds that follow a specific metabolic pathway are injected, the radiation is detected by sensors and then the complete image is reconstructed with the areas of highest activity [1]. 18F-fluorodeoxyglucose (FDG) is one of the most widely used radioactive substrates as a marker in PET [1, 63]. Among the applications of AI computer vision to this medical imaging modality, we have the segmentation of tumor areas in the brain [64], heart [65], head and neck [66], and nasopharynx [67] to adjust the dose and position of the radiotherapy intervention. With respect to classification tasks, work has been published on esophageal cancer [68], Alzheimer's disease typing [69], and Hodgkin's lymphoma [1, 70].
