P-1996. Performance Evaluation of an Artificial-Intelligence-Driven Single-Cell Imaging Platform for Rapid Phenotypic Antimicrobial Susceptibility Testing
Dong Woo Shin, Yunsang Choi, Kyunghwa Lee, Seong Jin Choi, Song Mi Moon, Eu Suk Kim, Hong-Bin Kim, Jeong Su Park, Kyoung-Ho Song

TL;DR
A new AI-based imaging platform called PhAST can rapidly test antibiotic susceptibility in bacteria from blood cultures within 90 minutes, showing high accuracy.
Contribution
The PhAST assay introduces a rapid, AI-driven single-cell imaging method for phenotypic antimicrobial susceptibility testing directly from blood cultures.
Findings
The PhAST assay achieved 95.1% categorical agreement with reference methods for Enterobacterales.
The assay delivered results in 117 minutes on average, significantly faster than conventional methods.
Performance was comparable to commercial systems like VITEK2 with low error rates.
Abstract
Conventional microbiological methods require several days from specimen receipt to organism identification and antimicrobial susceptibility testing (AST), delaying appropriate therapy. We evaluated an early prototype of the PhAST assay, a novel artificial-intelligence (AI)-driven single-cell imaging platform for rapid phenotypic AST. The prototype PhAST assay was developed to provide bacterial identification at the group level defined by the Clinical and Laboratory Standards Institute, and phenotypic AST results were obtained in ninety minutes, directly from a positive blood culture. Susceptibility was determined by AI-based quantitative analysis of single-cell phenotypes from images and videos of antibiotic-exposed and untreated cells (Figure 1). The assay was performed on positive blood culture broth samples flagged by a routine blood culture system obtained from the clinical…
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Taxonomy
TopicsBacterial Identification and Susceptibility Testing · Cell Image Analysis Techniques · Biosensors and Analytical Detection
