Biomedical EM Vision, Prognostics, Diagnostics

Nowadays, there is an increasing need for effective, reliable, fast, and low-cost technologies to perform biomedical electromagnetic (EM) vision, prognostics, and diagnostics. In this framework, the members of the ELEDIA Research Center network developed several techniques based on artificial intelligence (AI) and machine learning (ML) for the real-time inversion of data from heterogeneous sensing technologies, as well as multi-resolution approaches adaptively refining the resolution within the detected regions of interest. More in detail, the ELEDIA research areas in the field include

Early detection, localization, and characterization of brain pathologies (e.g., tumors, Alzheimer’s disease);
Reliable classification of brain strokes (i.e., hemorrhagic/ischemic) to select the most suitable treatment for the patient;
Continuous monitoring/follow-up (also remotely) of chest organs (e.g., lungs) to track their health status, for instance by checking the air/liquid content of the lungs of patients recovered in intensive care units;
Early detection of breast cancer and skin tumors which cannot be easily detected by visual inspection through non-invasive and non-painful techniques.

Within this framework, the members of the ELEDIA Research Center network developed several techniques based on AI and the learning-by-examples (LBE) paradigm enabling the real-time inversion of data collected over an external observation domain with heterogeneous sensing technologies (e.g., microwaves, electrical impedance tomography, and terahertz radiation). Moreover, innovative strategies have been introduced to exploit the a-priori information on the imaged domain by means of suitable differential formulations of the scattering problem at hand, along with multi-resolution approaches aimed at adaptively refining the resolution within the detected regions of interest (RoIs).

For additional information contact us at contact@eledia.org

M. Li, R. Guo, K. Zhang, Z. Lin, F. Yang, S. Xu, X. Chen, A. Massa, and A. Abubakar, "Machine Learning in Electromagnetics With Applications to Biomedical Imaging: A Review," IEEE Antennas and Propagation Magazine vol. 63, no. 3, pp. 39-51, June 2021 (DOI: 10.1109/MAP.2020.3043469)
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M. Salucci, G. Oliveri, and A. Massa, "Real-Time Electrical Impedance Tomography of the Human Chest by Means of a Learning-by-Examples Method," IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology vol. 3, no. 2, pp. 88-96, June 2019 (DOI: 10.1109/JERM.2019.2893217)
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M. Salucci and G. Oliveri, "Robust real‐time inversion of electrical impedance tomography data for human lung ventilation monitoring," Microwave and Optical Technology Letters vol. 61, no. 1, pp. 5-8, November 2018 (DOI: 10.1002/mop.31501)
M. Salucci, J. Vrba, I. Merunka, and A. Massa, "Real‐time brain stroke detection through a learning‐by‐examples technique—An experimental assessment," Microwave and Optical Technology Letters vol. 59, no. 11, pp. 2796-2799, August 2017 (DOI: 10.1002/mop.30821)
S. Hagness, E. Fear, and A. Massa, "Guest Editorial: Special Cluster on Microwave Medical Imaging," IEEE Antennas and Wireless Propagation Letters vol. 11, pp. 1592-1597 2012 (DOI: 10.1109/LAWP.2013.2240569)

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