Abstract
Microwave (MW) technologies are increasingly being employed in biomedicine and health care. Applications such as MW ablation (MWA), MW sensors for the realtime monitoring of physiological parameters, and lab-on-a-chip devices are just some of the examples in a wide variety of possibilities. Recently, high-frequency irreversible electroporation (H-FIRE) has been attracting more attention due to its important applications in oncology. It uses pulsed electrical fields (PEFs) to induce a controlled but irreversible process of permeabilization of cell membranes, thus triggering a substantial alteration in the physiological equilibria of cells, ultimately leading to their death. In this article we proposed an ML-based approach that exploits an ANN to estimate ablation occurrence, ablation area, and E-field lethal threshold (i.e., the three parameters highly impacting each H-FIRE therapeutic application). The ANN was modeled and trained by examining a large set of scientific publications describing H-FIRE experiments and their outcomes. Starting from these data, the ANN was capable of providing a satisfactory estimation accuracy on the aforementioned parameters. The achieved results, although very promising, could be further improved by widening the initial data set (i.e., by gathering details about a larger number of H-FIRE experiments) as well as by evaluating more complex ANN models. The presented tool has powerful potential to achieve fast, personalized protocols in H-FIRE therapy, streamlining the long and complex treatment planning process based on standard electromagnetic and multiphysics simulation methods for a new era of H-FIRE applications.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.