AI-Assisted Fungal Infection Detection Using Impedimetric Odour Biosensors

Öz

Yeast fungal infections have been widely recognized and if no quick and accurate treatment method is applied, they can be very dangerous and might even turn into death. In comparison with old-fashioned diagnostic solutions such as Culturing, which takes around one to three days to reveal yeast infections, rapid and effective treatment is often not initiated. In the current study a novel method is offered involving the extraction of yeast fungal strain identification in a rapid, cost-effective, and accurate way. Through the application of a gelatin-based hydrogel coating that represents the way in which odor receptors attach to cells a sensing concept for impedimetric odor was constructed. The hydrogel was further improved by adding glycerol for its structural stability and graphite powder for its better conductivity. The process of making a sensor involved applying the modified hydrogel to wires made of copper. The sensor was then exposed to the odor molecules from culture tests of Candida albicans, Candida glabrata, and Candida tropicalis, which were placed in a controlled environment. Changes in impedance took place, and these measurements were analyzed using a Random Forest machine learning algorithm that helped to get 94% classification success. This new testing process may lead to a revolution in the era of clinical diagnostics. It will enable speediness, simplicity, as well as precision in the detection of yeast fungal infections, which, in turn, will decrease health risks leading to unnecessary treatment costs by approved drug companies.