AI-Blue-Carba: A Rapid and Improved Carbapenemase Producer Detection Assay Using Blue-Carba With Deep Learning

Ling Jia, Lu Han, He Xin Cai, Ze Hua Cui, Run Shi Yang, Rong Min Zhang, Shuan Cheng Bai, Xu Wei Liu, Ran Wei, Liang Chen, Xiao Ping Liao, Ya Hong Liu, Xi Ming Li, Jian Sun

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Article number585417
JournalFrontiers in Microbiology
StatePublished - Nov 20 2020

All Science Journal Classification (ASJC) codes

  • Microbiology (medical)
  • Microbiology


  • Blue-Carba
  • OD value
  • carbapenemase-producing gram-negative bacteria
  • deep learning
  • rapid detection

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