This line applies Computer Vision, Machine Learning and Generative AI to dentistry. It develops intelligent systems for the automatic analysis of clinical images of the oral cavity, aiming to support early detection and computer-aided diagnosis of potentially malignant lesions and oral cancer.

This research is carried out in collaboration with the ORALMED research group at the Complutense University of Madrid (UCM), through OralMedNet, a telediagnosis service for oral mucosal lesions. Through a web application, dentists and physicians submit clinical images and lesion characteristics, which are reviewed by ORALMED's oral medicine specialists to improve the diagnosis of oral cancer and oral potentially malignant disorders. The project brings together dentists and computer scientists from UCM, UNED, UOC and other institutions.

Contact: Margarita Bachiller (marga@dia.uned.es)

This line explores how Artificial Intelligence and bio-inspired Computer Vision models can contribute to the development of visual neuroprostheses that improve the perception of the environment for people with visual impairments. This research is currently carried out within the AI4BRAIN project, in collaboration with the Universidad Politécnica de Cartagena (UPCT) and the Universidad Miguel Hernández (UMH).

UNED's contribution focuses on bio-inspired Artificial Intelligence techniques for image processing and retinal modelling, with the goal of generating more efficient visual representations for prosthetic vision systems (AI4BRAIN-UNED project).

Contact: Mariano Rincón (mrincon@dia.uned.es)

This line investigates the development and application of Artificial Intelligence and Data Science techniques to support the early detection, diagnosis and monitoring of cognitive impairment and other neuropsychological disorders. Projects include the analysis of clinical data, traditional neuropsychological tests and immersive assessments in Virtual Reality, with the aim of building clinical decision support tools.

This line is currently carried out mainly through the SCREENIA project, a continuation of the IDENTIA project. Both focus on intelligent systems for the early detection of cognitive impairment through the analysis of digital biomarkers and Artificial Intelligence techniques (AI4EICI).

Contact: Estela Díaz (ediazlopez@dia.uned.es)

This line applies Artificial Intelligence and Machine Learning to metabolomics in order to discover biomarkers that support the early diagnosis, prognosis and monitoring of disease. Metabolomics studies the small molecules (metabolites) present in a biological sample, such as blood serum or plasma, and offers a direct snapshot of an organism's physiological state. Its data, typically obtained by mass spectrometry or nuclear magnetic resonance, are high-dimensional and noisy, and often come from a limited number of samples, which makes them particularly hard to analyse.

Our projects cover the full analysis pipeline, from data preprocessing and feature selection to interpretable predictive models and the biological interpretation of the results. We currently work with serum and plasma data from two international cohorts, ADNI (Alzheimer's Disease Neuroimaging Initiative) and PPMI (Parkinson's Progression Markers Initiative), to identify metabolic profiles associated with Alzheimer's and Parkinson's disease, with a particular focus on the stability of the selected biomarkers across different algorithms, validation strategies and datasets.

Contact: David Bernal (dbernal@dia.uned.es)

This line develops techniques for analysing structural and functional magnetic resonance images to study the brain, including the localisation, characterisation and tracking of brain structures, and diagnostic support. Current technology makes a wide variety of non-invasive brain images available. Neuroimaging studies analyse the brain at different scales, seeking to identify the relationship between structure, function and behaviour. Among other topics, we focus on analysing both functional and structural MRI at different levels of abstraction.

In recent years we have taken part in several research projects on biomedical image analysis. These include project 018-ABEL-CM-2013, which developed computer vision techniques for characterising amorphous objects in 2D and 3D images, applied to the study of focal brain lesions. We have also worked on resting-state functional MRI (resting-state fMRI), developing clustering, classification and diagnostic support methods based on the study of the main functional brain networks.

Contact: Mariano Rincón (mrincon@dia.uned.es)

This line investigates Artificial Intelligence models capable of automatically analysing and assessing aesthetic aspects of images and visualisations, drawing on principles from psychology, neuroscience and design.

It builds on the G-GI3000/IDIK project, aimed at the automatic assessment of the landscape integration of infrastructures using visual information.

Contact: Mariano Rincón (mrincon@dia.uned.es)

This research line develops intelligent technologies to support assessment, intervention and follow-up in psychoeducational settings. Using Artificial Intelligence, Machine Learning and Knowledge Engineering, we design tools that help personalise learning, identify educational needs early and support the decisions of teachers and school counsellors.

Projects include intelligent recommender systems, educational assistants, predictive models of academic performance, psychoeducational assessment tools and support platforms for students with specific educational support needs.

Topics of interest

• Artificial Intelligence in Education.
• Learning analytics.
• Machine learning and educational data mining.
• Intelligent recommender systems.
• Knowledge Engineering and ontologies.
• Virtual Reality and immersive technologies.

Neurodegenerative diseases have become especially relevant as the population ages. This line investigates the application of Artificial Intelligence to the analysis of neuropsychological studies, in order to support early detection of cognitive impairment and the development of clinical assessment support tools.

Current work focuses on the analysis of semantic memory, cognitive tests and drawing tests performed by older adults, seeking to identify patterns consistent with the early stages of neurodegenerative diseases.

Topics of interest

• Artificial Intelligence for health.
• Machine learning and data mining.
• Decision theory.
• Processing of clinical and neuropsychological data.
• Biomedical ontologies.

This line develops Evolutionary Computation techniques for image processing, segmentation, detection and analysis. Projects explore how evolutionary algorithms can optimise computer vision processes in different application domains, especially medical imaging.

Previous projects include Computer Vision applications for the automatic analysis of medical images.

This line investigates the automatic design of models using Evolutionary Computation techniques. It addresses problems such as the design of electronic circuits, industrial models, hardware, mathematical functions, neural networks and other systems that can be optimised with evolutionary algorithms.

This line studies the foundations of Evolutionary Computation and the development of new evolutionary models, operators and mechanisms. Previous work includes research on selection mechanisms, evolution strategies and new approaches to improve the performance of evolutionary algorithms.