José S. Torrecilla - Artificial intelligence in the early diagnosis of diseases through breath analysis

Please view the video here and the presentation slides here

Biography: 

José S. Torrecilla is currently an Associate Professor and Researcher at the Chemical Engineering Department of the Complutense University of Madrid (UCM), Spain. He is the principal investigator of the AlgoReach Research group. Dr. Torrecilla received his MBA, B.Sc., and Ph.D. (with honors) in Chemical Engineering from the UCM and carried out his postdoctoral studies at Queen’s University of Belfast (UK) and the Spanish Ministry of Science and Technology. He has been working for more than 25 years in the development of chemometric tools and intelligent machine learning models to interpret complex systems and reach applications in different sectors such as health (non-invasive early diagnosis), food technology (quality control and adulteration detection), chemical engineering, and more. He has written technological books and patents. Additionally, Dr. Torrecilla has published over one hundred articles in prestigious journals and has participated as a principal investigator in a high number of collaborations and projects at national and international levels.

Abstract: 

The combination and growth of the fields of chemical analysis and mathematical tools and models leads to relevant and innovative alternatives within the field of disease diagnosis. In medicine, the analysis of different kinds of samples generates large amounts of patient-specific data. For this reason, the use of mathematical tools capable of pinpointing and distinguishing the most relevant information contained in such databases is crucial within the scope of medical diagnosis. This integration becomes increasingly important when it comes to the diagnosis of diseases where the prognosis depends on the time and stage at which it was diagnosed, for instance, any type of cancer.

Tools based on artificial intelligence and machine learning are designed to be able to find differences and similarities between large amounts of samples by analysis of many variables and identification of their relationships and patterns. The integration of intelligent tools in the field of diagnosis offers the ability to distinguish profiles and facilitate the association of samples with pathological or healthy patterns, leading to informed and meticulous diagnoses.

Furthermore, in many occasions, the synergy between different intelligent tools in the same algorithm offers outstanding advantages in the development of applications, including the diagnosis of diseases by means of a technique as minimally invasive as the analysis of the patient's breath. For this reason, the research group AlgoReach aims to find the best intelligent algorithms, either through a single tool or through a combination, to reveal relevant and generally hidden characteristics that offer speedy and reliable depictions of diseases intended for their early, safe, and accurate diagnosis.

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