Advanced artificial intelligence methods applied to societal challenges in biomedical engineering

• Autores: Dimitri Viatkin
• Directores de la Tesis: Begoña García-Zapirain (dir. tes.), Amaia Méndez Zorrilla (dir. tes.)
• Lectura: En la Universidad de Deusto ( España ) en 2023
• Idioma: inglés
• Tribunal Calificador de la Tesis: Adel Said Elmaghraby (presid.), Ibon Oleagordia Ruiz (secret.), Daniel Esteban Sierra Sosa (voc.)
• Programa de doctorado: Programa de Doctorado en Ingeniería para la Sociedad de la Información y Desarrollo Sostenible por la Universidad de Deusto
• Materias:
  • Matemáticas
    • Ciencia de los ordenadores
      • Inteligencia artificial
  • Ciencias tecnológicas
    • Tecnología de los ordenadores
      • Dispositivos de transmisión de datos
    • Tecnología electrónica
      • Otras especialidades de tecnología electrónica
• Enlaces
  • Tesis en acceso abierto en: repositorio.deusto.es
• Resumen

  • In recent years, artificial intelligence and machine learning algorithms have been increasingly used not only in scientific, but also in applied and societal fields. This is due to the development of computing power, technological development and the development of the algorithms used. Developed algorithms of artificial intelligence begin to be introduced also in medicine, engineering, bioengineering, biomedical and other frontier areas, where different knowledge areas touch each other. Development and training of artificial intelligence algorithms for solving problems, which are at the interaction boundary of different fields of science, can improve the quality of interaction between experts from different fields, expand the frontiers of knowledge and solve applied problems in the areas under study.

    This dissertation examines the possibilities of using artificial intelligence methods in biomedical engineering tasks, at the edge of medicine and engineering. The possibilities of analysis, development, use and interpretation of artificial intelligence algorithms in applied problems for sustainable development of society, medical and industrial development are considered. The dissertation consists of two case studies conducted in Spain and Russia, each using a different methodology and approach to analysis.

    The first case study explores the application of deep learning to the task of measuring the position of patients' fingers in multiple sclerosis. Tracking the limited degree of mobility of the fingers on the hand can be used as a marker to characterize the course of multiple sclerosis and the success of the treatment prescribed. The objectives of this case study were to review and analyze the literature on the various methods available for assessing finger position, to collect and prepare data for a single camera-based computer vision system designed to detect finger position, and to train and test a neural network based on a neural network for assessing finger position.

    The second case study explores the potential of deep learning methods for materials analysis and the possibility of applying them for biomedical purposes. This case study explores the potential of neural networks to analyze the properties and structure of materials with different amounts of data and different representations. The generation of materials based on a number of incomplete parameters with limited data has been studied. Algorithms for processing different types of material data representations and their parameters have been studied. In this case study, the following tasks were accomplished: literature review and analysis on various available material analysis methods, collectiovn and preparation of data for a material analysis system with different structures and parameters, training and testing the neural network on the collected data. The neural network prediction of critical superconductivity temperature for materials based on their chemical formula was considered. The prediction of the reduced glass transition temperature of metal alloys based on a neural network was considered. The prediction of material composition based on the required physical parameters for cellulosic materials was considered. The use of generative-adversarial networks to predict the properties and composition of metal alloys based on incomplete material information with an acceptable range of predicted parameters was considered. The second case study demonstrates the development of the idea of applying neural networks to materials problems, from predicting a single parameter from a chemical formula, to predicting physical parameters and material composition based on incomplete data.