AUTHOR=Jia Ling , Han Lu , Cai He-Xin , Cui Ze-Hua , Yang Run-Shi , Zhang Rong-Min , Bai Shuan-Cheng , Liu Xu-Wei , Wei Ran , Chen Liang , Liao Xiao-Ping , Liu Ya-Hong , Li Xi-Ming , Sun Jian TITLE=AI-Blue-Carba: A Rapid and Improved Carbapenemase Producer Detection Assay Using Blue-Carba With Deep Learning JOURNAL=Frontiers in Microbiology VOLUME=11 YEAR=2020 URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2020.585417 DOI=10.3389/fmicb.2020.585417 ISSN=1664-302X ABSTRACT=

A rapid and accurate detection of carbapenemase-producing Gram-negative bacteria (CPGNB) has an immediate demand in the clinic. Here, we developed and validated a method for rapid detection of CPGNB using Blue-Carba combined with deep learning (designated as AI-Blue-Carba). The optimum bacterial suspension concentration and detection wavelength were determined using a Multimode Plate Reader and integrated with deep learning modeling. We examined 160 carbapenemase-producing and non-carbapenemase-producing bacteria using the Blue-Carba test and a series of time and optical density values were obtained to build and validate the machine models. Subsequently, a simplified model was re-evaluated by descending the dataset from 13 time points to 2 time points. The best suitable bacterial concentration was determined to be 1.5 optical density (OD) and the optimum detection wavelength for AI-Blue-Carba was set as 615 nm. Among the 2 models (LRM and LSTM), the LSTM model generated the higher ROC-AUC value. Moreover, the simplified LSTM model trained by short time points (0–15 min) did not impair the accuracy of LSTM model. Compared with the traditional Blue-Carba, the AI-Blue-Carba method has a sensitivity of 95.3% and a specificity of 95.7% at 15 min, which is a rapid and accurate method to detect CPGNB.