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The mosquito  Aedes aegypti  is the major vector of arboviruses like dengue, Zika and chikungunya viruses. Attempts to reduce arboviruses emergence focusing on  Ae. aegypti  control has proven challenging due to the increase of insecticide resistances. An emerging strategy which consists of releasing  Ae. aegypti  artificially infected with  Wolbachia  in natural mosquito populations is currently being developed. The monitoring of  Wolbachia -positive  Ae. aegypti  in the field is performed in order to ensure the program effectiveness. Here, the reliability of the Matrix‑Assisted Laser Desorption Ionization‑Time Of Flight (MALDI‑TOF) coupled with the machine learning methods like Convolutional Neural Network (CNN) to detect  Wolbachia  in field  Ae. aegypti  was assessed for the first time. For this purpose, laboratory reared and field  Ae. aegypti  were analyzed. The results showed that the CNN recognized  Ae. aegypti  spectral patterns associated with  Wolbachia -infection. The MALDI-TOF coupled with the CNN (sensitivity = 93%, specificity = 99%, accuracy = 97%) was more efficient than the loop-mediated isothermal amplification (LAMP), and as efficient as qPCR for  Wolbachia  detection. It therefore represents an interesting method to evaluate the prevalence of  Wolbachia  in field  Ae. aegypti  mosquitoes.

Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence