prediction of the antimicrobial resistance of acinetobacter baumannii using artificial neural networks in northern iran

Background: antimicrobial resistance (amr) in clinical and environmental acinetobacter baumannii strains has been recognized as a worldwide challenge for public health. artificial neural networks (anns), an artificial intelligence (ai) algorithm, is a computational model for understanding the complex relationship between input and output data. the anns model can support authorities in making proper prescriptions in a significantly shorter time frame, facilitating a more accurate treatment procedure while saving budget and required medical staff. objectives: the present study aimed to investigate whether ai can improve the detection of amr a. baumannii isolates under experimental conditions. methods: clinical and environmental a. baumannii isolates were collected from hospitalized patients and the perimeter of hospitals. the minimum inhibitory concentrations (mics) of isolates to antibiotics and biocides effective doses were determined using the microdilution broth test according to clsi-2021 guidelines. the anns model was trained using a portion of in vitro datasets (i.e., train set), taking into account different characteristics of clinical/environmental isolates recorded for each isolate in the dataset. finally, the anns model was used to predict the amr class and biocides dose class of the laboratory dataset (i.e., test set), and results were compared with existing data to determine the accuracy of anns model predictions. results: on average, 35% of a. baumannii strains were isolated from clinical/environmental samples. the minimum sensitivity level [i.e., r class of ciprofloxacin (cip5) and ticarcillin (tic75)] was observed in 70% of clinical a. baumannii isolates, and the most effective dose of bzk and bzt biocides against environmental isolates was 256 µg/ml, while it was 128 µg/ml for clx. results showed that the prevalence of a. baumannii isolates resistant to antibiotics and biocides is high not only in hospitals but also in the environment, and the anns model can predict the classification of the remaining test dataset with approximately 90% accuracy. conclusions: this measure can contribute to the prevention of the overuse and incorrect use of antimicrobial agents to combat rising resistance rates of a. baumannii by focusing on a wider variety of potentially effective parameters on the required dose of antimicrobial agents. this model can be practically used in hospitals as part of treatment protocols, highlighting the cheap and fast diagnosis and prescription.