Algorithmic approaches to identification and antibiotic susceptibility testing of pathogenic microbes
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Speaker
Abstract
In this talk, I'll first provide some context regarding the challenges of hospital-acquired infections, antibiotic resistance, and sepsis, a disease estimated to kill millions worldwide, and one of the leading causes of death in hospitalized patients. I'll then review traditional laboratory techniques for identifying pathogenic microbes and performing antibiotic susceptibility testing (AST), and how antibiotic susceptibility is reported in terms of the Minimum Inhibitory Concentration (MIC). For the remainder of the talk, I'll describe the technologies and algorithms used by the Accelerate Pheno system for rapid identification and AST. Identification involves automated fluorescent in-situ hybridization, or FISH, coupled with algorithmic analysis of microscopic images. For AST, the Pheno system extracts a number of features from time-lapse images of growing bacterial colonies exposed to antibiotics (e.g. morphology, division rates, and growth curves), which become the inputs to a suite of machine learning algorithms for determining MIC values.