The first scholarship in ICT and Mental Health awards a doctoral thesis for the early detection of psychotic disorders with artificial intelligence

Last April, the TIC Salut Social Foundation, in collaboration with the Barbosa & Petit Foundation, awarded the first TIC Mental Health Scholarship to the best PhD project in technological innovation. The award winner was PhD student from La Salle – Ramon Llull University, Jordi Malé. His research focuses on the efficiency improvement of the diagnosis of psychotic disorders.

Under the name BeGin (Deep learning-based brain shape morphology analysis and correlates between brain and facial biomarkers for the diagnosis of psychotic disorders), the project is funded by the Ministry of Science and Innovation and directed by Dr. Xavier Sevillano and Dr. Neus Martínez-Abadías from the Human Environment Research Group (HER) at La Salle – Ramon Llull University.

The second call for the ICT Mental Health Scholarship 2025 is now open. The deadline for applications is October 30, 2024. The participation rules can be consulted at the following link.

Technology at the service of diagnosis

Currently, psychotic disorders are diagnosed through clinical interviews once the patient has had one or more psychotic episodes. It is a diagnosis with a significant subjective weight and carried out when the patient is suffering from the manifestation of the disease. On the contrary, BeGin’s approach is eminently objective since it is based on obtaining biomarkers from clinical tests such as brain MRIs. As Malé explains, “deep learning helps us automatically identify the existence of differences between the brain morphology of healthy people and people diagnosed with schizophrenia or bipolar disorder. However, it should be noted that our approach aims to be a complement and an aid to the diagnostic methods currently applied.”

In this sense, the project represents a pioneering effort that uses phenomics combined with machine learning methods to advance the diagnosis of conditions such as schizophrenia and bipolar disorder. Through a non-invasive approach and integrating facial and genetic brain biomarkers as the basis of rapid and accurate diagnosis.

“Our work focuses on using Deep Learning techniques to analyze brain and face morphology, as well as establishing correlations between these two types of biomarkers,” Malé explains. This methodology has already begun to show results in identifying significant anatomical differences. And these can be key for the diagnosis of psychosis.

Preliminary results and challenges

The models developed so far improve the current accuracy in the field of neurology and radiology in the localization of key anatomical points of the brain with minimal errors. “We are advancing in the accuracy and speed of diagnosing psychotic disorders, bringing great benefit to patient care,” Malé asserts. One of the main challenges has been the management of diverse data and the effective integration of these into a coherent model. A task that continues to evolve.

Looking to the future, the thesis author envisions an expansion of his project to include other disorders that affect brain anatomy. He details that “our goal is to adapt our research to be used in a wide range of conditions, providing medical professionals with tools to make objective and early diagnoses.”

Impact on the Health System and society

Malé’s research has the potential to profoundly change how psychotic disorders are diagnosed and treated, moving towards a more objective approach based on clear and detectable biomarkers. This could not only reduce the costs associated with long-term treatment but also improve outcomes for patients, offering a better quality of life.

The recognition by the TIC Salut Social Foundation not only underscores the potential of technology in improving mental health but also shows continued support for innovation that can transform clinical medicine. The BeGin project is an example of how the integration of multiple disciplines and technologies can lead to significant advances in the treatment and diagnosis of complex diseases.