Van Haeverbeke, MaximeMaximeVan HaeverbekeCums, CharlotteCharlotteCumsVackier, ThijsThijsVackierBraeken, DriesDriesBraekenStock, MichielMichielStockSteenackers, HansHansSteenackersDe Baets, BernardBernardDe Baets2026-01-192026-01-192024979-8-3503-5496-62766-4465https://imec-publications.be/handle/20.500.12860/58660In light of the worldwide emergence of antibioticresistant bacteria, appropriate and targeted approaches to antibiotic treatments are crucial. Such targeted or precision medicine approaches rely on precisely and quickly determining antibiotic susceptibility. The current routinely applied antibiotic susceptibility testing assays operate within a timeline of one to two days, hindering timely treatment decisions. Furthermore, point-of-care testing would further improve the efficiency of the tests. Recent research demonstrates that electrochemical impedance spectroscopy and related techniques are promising solutions because they can be developed as miniaturised point-of-care devices and high-throughput installations in healthcare facilities. This paper proposes a label-free impedimetric sensing approach to antibiotic susceptibility testing. The proposed novel method uses unmodified microelectrode arrays and convolutional neural network models leveraging impedance spectroscopy measurements and their derived equivalent electrical circuit features to accurately determine bacterial biofilms’ antibiotic susceptibility.enBacterial biofilmantimicrobial susceptibilityelectrochemical impedance spectroscopyequivalent electrical circuitmachine learningScience & TechnologyTechnologyProceedings paper10.1109/BioCAS61083.2024.10798403979-8-3503-5495-9WOS:001411993700178