Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species.
Sandflies are vectors for several tropical diseases such as leishmaniasis, bartonellosis, and sandfly fever. Moreover, sandflies exhibit species-specificity in transmitting particular pathogen species, with females being responsible for disease transmission. Thus, effective classification of sandfly...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0320224 |
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| author | Mohammad Fraiwan Rami Mukbel Dania Kanaan |
| author_facet | Mohammad Fraiwan Rami Mukbel Dania Kanaan |
| author_sort | Mohammad Fraiwan |
| collection | DOAJ |
| description | Sandflies are vectors for several tropical diseases such as leishmaniasis, bartonellosis, and sandfly fever. Moreover, sandflies exhibit species-specificity in transmitting particular pathogen species, with females being responsible for disease transmission. Thus, effective classification of sandfly species and the corresponding sex identification are important for disease surveillance and control, managing breeding/populations, research and development, and conducting epidemiological studies. This is typically performed manually by observing internal morphological features, which maybe an error-prone tedious process. In this work, we developed a deep learning artificial intelligence system to determine the gender and to differentiate between three species of two sandfly subgenera (i.e., Phlebotomus alexandri, Phlebotomus papatasi, and Phlebotomus sergenti). Using locally field-caught and prepared samples over a period of two years, and based on convolutional neural networks, transfer learning, and early fusion of genital and pharynx images, we achieved exceptional classification accuracy (greater than 95%) across multiple performance metrics and using a wide range of pre-trained convolutional neural network models. This study not only contributes to the field of medical entomology by providing an automated and accurate solution for sandfly gender identification and taxonomy, but also establishes a framework for leveraging deep learning techniques in similar vector-borne disease research and control efforts. |
| format | Article |
| id | doaj-art-5b9fcf195a0c4119834f7cb58ef150cd |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-5b9fcf195a0c4119834f7cb58ef150cd2025-08-20T02:08:24ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01204e032022410.1371/journal.pone.0320224Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species.Mohammad FraiwanRami MukbelDania KanaanSandflies are vectors for several tropical diseases such as leishmaniasis, bartonellosis, and sandfly fever. Moreover, sandflies exhibit species-specificity in transmitting particular pathogen species, with females being responsible for disease transmission. Thus, effective classification of sandfly species and the corresponding sex identification are important for disease surveillance and control, managing breeding/populations, research and development, and conducting epidemiological studies. This is typically performed manually by observing internal morphological features, which maybe an error-prone tedious process. In this work, we developed a deep learning artificial intelligence system to determine the gender and to differentiate between three species of two sandfly subgenera (i.e., Phlebotomus alexandri, Phlebotomus papatasi, and Phlebotomus sergenti). Using locally field-caught and prepared samples over a period of two years, and based on convolutional neural networks, transfer learning, and early fusion of genital and pharynx images, we achieved exceptional classification accuracy (greater than 95%) across multiple performance metrics and using a wide range of pre-trained convolutional neural network models. This study not only contributes to the field of medical entomology by providing an automated and accurate solution for sandfly gender identification and taxonomy, but also establishes a framework for leveraging deep learning techniques in similar vector-borne disease research and control efforts.https://doi.org/10.1371/journal.pone.0320224 |
| spellingShingle | Mohammad Fraiwan Rami Mukbel Dania Kanaan Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species. PLoS ONE |
| title | Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species. |
| title_full | Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species. |
| title_fullStr | Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species. |
| title_full_unstemmed | Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species. |
| title_short | Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species. |
| title_sort | using deep learning artificial intelligence for sex identification and taxonomy of sand fly species |
| url | https://doi.org/10.1371/journal.pone.0320224 |
| work_keys_str_mv | AT mohammadfraiwan usingdeeplearningartificialintelligenceforsexidentificationandtaxonomyofsandflyspecies AT ramimukbel usingdeeplearningartificialintelligenceforsexidentificationandtaxonomyofsandflyspecies AT daniakanaan usingdeeplearningartificialintelligenceforsexidentificationandtaxonomyofsandflyspecies |