We present the development of an automatic dental diagnosis system based on artificial intelligence. The goal is to accurately support clinical decision-making in dentistry based on pantomographic images. We describe the construction of a large dataset of pantomographic images with high-quality annotations, the development of an intelligent model for semantic image interpretation and generation of presumptive diagnoses, and an application for end users. The model demonstrates the ability to correctly identify each individual tooth, detect multiple conditions and common pathologies in dental pieces, in case they arise, and generate a presumptive diagnostic text based on the information extracted from the image. The application, in the form of a digital web-based negatoscope, represents a technological advancement in radiology in line with the digital transformation era, offering a versatile and efficient tool for the interactive visualization of radiographic images, especially designed for dental professionals.

Autonomous driving, decades ago relegated to the realm of science fiction, emerged as a tangible reality that is rapidly transforming the automotive industry, redefining our relationship with vehicles, and placing them in the spotlight of both the industry and the general public. Through the study and analysis of modern and efficient interpolation techniques, we aim to reduce the current costs and processing requirements associated with the LiDAR sensor, which is one of the main information sources. Our approach explores the fusion of lower-cost LiDAR sensors with advanced interpolation techniques, with a particular focus on achieving performance parity with pricier 64-channel LiDAR setups. This work is based on 3 main axes: firstly, the analysis of available LiDAR data and its representation; secondly, the development and implementation of an interpolation technique based on 1D convolutional layers integrated with fully connected layers, in order to analyse data coming from a sliding window; and finally, the comparative evaluation of the results between different state-of-the-art interpolation techniques, using object detection networks in point clouds. By interpolating the point clouds with the proposed technique, improvements between 1.92% and 30.98% in detection and classification tasks were achieved, depending on the object and the type of detection (3D or bird's eye view). Furthermore, computational efficiency was not left aside by reducing the inference times necessary for interpolation, compared to other techniques used as contrast. This highlights the viability and scalability of our approach in realizing cost-effective yet high-perfermance autonomous driving systems.

The popularization of unmanned aerial vehicles (UAVs) is transforming contemporary Geotechnology related activities, offering accessibility, accuracy, and efficiency. In particular, landcover analysis and cartography with UAV-borne imagery enables precise identification and measurements for surveying purposes. While multispectral cameras facilitate detailed mapping, affordability drives the use of common RGB drone cameras. This, in turn, raises the requirement of robust and versatile analysis techniques for extracting meaningful information from RGB images. In this study, we assess different algorithms for mapping urban areas using high-resolution RGB aerial images from UAVs. We explore the use of different machine learning methods in landcover identification and classification, leading to a workflow for producing thematic maps, showcasing the potential of machine learning in urban mapping with UAV-acquired RGB imagery.

In neuroimaging, magnetic resonance images can be used to locate and obtain various parameters in order to find a wide range of pathologies, improving diagnosis and hence early treatment. Since images of the brain are volumetric, they are treated volumetrically in voxels, rather than planarly in pixels. We present an alternative implementation of local neighborhood-based texture parameters that have been recently shown to improve the detection of differences in the brain between healthy patients and those with Alzheimer's disease using diffusion tensor imaging [1]. We implemented the method (1) in Python using the Anaconda environment and the PyCharm compiler and (2) in 3D Slicer environment, as it is widely used by the neurology community, like the National Alliance for Medical Image Computing, among others. Weighted rotational invariant local operators were used in the calculus, namely average, standard deviation, coefficient of variation, normalized skewness, median, inter-quartile range and quartile coefficient of variation. Comparison between the implementations has been measured with normalized root-mean-square error. No differences have been observed for the non-linear parameters based on quartiles and errors smaller than 0.5% have been observed for operators that used Fast Fourier Transform based convolution instead of the explicit method.