AI-Blue-Carba: A Rapid and Improved Carbapenemase Producer Detection Assay Using Blue-Carba With Deep Learning - 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; et al. (Frontiers in Microbiology, 2020)
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 carbapenemase-producing Gram-negative bacteria 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 160carbapenemase-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 98.1 % at 15 min, which is a rapid and accurate method to detect carbapenemase-producing Gram-negative bacteria.