Using machine learning techniques to predict antimicrobial resistance in stone disease patients.
Lazaros TzelvesLazaros LazarouGeorgios FeretzakisDimitris KallesPanagiotis MourmourisEvangelos LoupelisSpyridon BasourakosMarinos BerdempesIoannis ManolitsisIraklis MitsogiannisAndreas SkolarikosIoannis VarkarakisPublished in: World journal of urology (2022)
Artificial intelligence technology can be used for making predictions on antibiotic resistance patterns when knowing Gram staining with an accuracy of 77% and nearly 87% when identifying specific microorganisms. This knowledge can aid urologists prescribing the appropriate antibiotic 24-48 h before test results are known.