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Learning algorithms estimate pose and detect motor anomalies in flies exposed to minimal doses of a toxicant. | LitMetric

AI Article Synopsis

  • Pesticide exposure, even in small amounts, can harm ecosystems, and this study tested machine learning for detecting motor issues in insects due to low insecticide exposure.
  • The Mediterranean fruit fly was used as a model species and trained algorithms to analyze their movements from video recordings in a special setup.
  • The Random Forest and K-Nearest Neighbor algorithms showed the best results, indicating that machine learning can effectively identify the subtle effects of insecticides on fly behavior, with implications for environmental health.

Article Abstract

Pesticide exposure, even at low doses, can have detrimental effects on ecosystems. This study aimed at validating the use of machine learning for recognizing motor anomalies, produced by minimal insecticide exposure on a model insect species. The Mediterranean fruit fly, (Diptera: Tephritidae), was exposed to food contaminated with low concentrations of essential oil (EO). A deep learning approach enabled fly pose estimation on video recordings in a custom-built arena. Five machine learning algorithms were trained on handcrafted features, extracted from the predicted pose, to distinguish treated individuals. Random Forest and K-Nearest Neighbor algorithms best performed, with an area under the receiver operating characteristic (ROC) curve of 0.75 and 0.73, respectively. Both algorithms achieved an accuracy of 0.71. Results show the machine learning potential for detecting sublethal effects arising from insecticide exposure on fly motor behavior, which could also affect other organisms and environmental health.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10696104PMC
http://dx.doi.org/10.1016/j.isci.2023.108349DOI Listing

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