Extensive Validation and Prospective Observation of the Impact of an AI-Based Rapid Antibiotics Susceptibility Prediction Platform in Multiple Medical Centers

Wang, Hsin-Yao; Chung, Chia-Ru; Tseng, Yi-Ju; Chen, Chao‐Jung; Lin, Ting-Wei; Yu, Jia-Ruei; Wu, Min‐Hsien; Huang, Wan‐Ting; Liu, Tsui-Ping; Lee, Tzong-Yi; Horng, Jorng-Tzong; Lu, Jang‐Jih

doi:10.2139/ssrn.3938835

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“…The cut-off was set to 0.48, whereas an isolate with probability lower than 0.48 is predicted as MSSA, and MRSA is predicted when probability is higher than 0.48. In the deployment of the AI model [ 15 ], we found that wrong predictions frequently occurred in the probability range between 0.40 and 0.48. (See Supplementary Table S1 .)…”

Section: Introductionmentioning

confidence: 99%

Investigating Unfavorable Factors That Impede MALDI-TOF-Based AI in Predicting Antibiotic Resistance

et al. 2022

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The combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility testing (AST) of Staphylococcus aureus. Based on the AI predictive probability, cases with probabilities between the low and high cut-offs are defined as being in the “grey zone”. We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. In total, 479 S. aureus isolates were collected and analyzed by MALDI-TOF, and AST prediction and standard AST were obtained in a tertiary medical center. The predictions were categorized as correct-prediction group, wrong-prediction group, and grey-zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For methicillin-resistant S. aureus (MRSA), a larger cefoxitin zone size was found in the wrong-prediction group. Multilocus sequence typing of the MRSA isolates in the grey-zone group revealed that uncommon strain types comprised 80%. Of the methicillin-susceptible S. aureus (MSSA) isolates in the grey-zone group, the majority (60%) comprised over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity contribute to suboptimal predictive performance.

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Section: Introductionmentioning

confidence: 99%