Keywords:Neural network, Microbial identification, Numerical identification method
In the treatment of infectious diseases, rapid and correct identification of the inflammatory bacteria is important for the Antimicrobial Stewardship. In the numerical identification method, multiple biochemical reactions of the inflammatory bacteria are tested, and the positive/negative results are accumulated. The metabolic profiles are compared with established database obtained by testing known strains, and the species is estimated by calculating likelihood. In many clinical laboratories, automated bacterial identification devices based on biochemical reactions are used. RAISUS S4, developed by us, is one of them. It monitors time series data of biochemical reaction as fluorescence value and identifies bacterial species by numerical identification method. We conducted a study to establish a rapid and accurate testing method by using the fluorescence value more effectively. The accuracy of the neural network model using the fluorescence values as input and the bacterial species names as output were verified. The results showed a high rate of correct identification for both Gram-negative rods and Gram-positive cocci within 2 hours of incubation. This method follows the conventional method of classifying bacterial species based on biochemical reactions, and can also handle time series data, may be an effective way in clinical laboratories.
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