Volume 3, Issue 2 (April 2024)
Health Science Monitor 2024, 3(2): 113-119 |
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Mansouri V, Gholizadeh S, Hosseinpoor S. Impact of artificial intelligence on medical entomology research. Health Science Monitor 2024; 3 (2) :113-119
URL: http://hsm.umsu.ac.ir/article-1-150-en.html
URL: http://hsm.umsu.ac.ir/article-1-150-en.html
Department of Environmental Health Engineering, School of Public Health, Urmia University of Medical Sciences, Urmia, Iran
Abstract: (873 Views)
Artificial intelligence (AI) and its techniques are a rapidly growing field and are being used in various fields, including healthcare and many others. Medical entomology is one of the important sectors in health care. Diseases transmitted through carriers impose a great economic and social burden on the health of society. Mosquito-borne diseases pose major challenges to human health, affecting more than 600 million people and killing more than 1 million people each year. In the current study, we reviewed more than 30 papers in PubMed and Google Scholar that dealt with the application of artificial intelligence techniques in medical entomology. Articles were classified based on the use of AI and its techniques in this field and show that this new tool can play an important role in predicting the risk of contracting vector-borne diseases and the accurate monitoring of insect vector species.
Type of Study: Research |
Subject:
Medical Entomology
Received: 2023/10/22 | Accepted: 2024/02/27 | Published: 2024/04/9
Received: 2023/10/22 | Accepted: 2024/02/27 | Published: 2024/04/9
References
1. Balla E, Flórián N, Gergócs V, Gránicz L, Tóth F, Németh T, Dombos M. An opto-electronic sensor-ring to detect arthropods of significantly different body sizes. Sensors. 2020;20(4):982. DOI: 10.3390/s20040982 [DOI] [PMID] [PMCID]
2. Balyen L, Peto T. Promising artificial intelligence-machine learning-deep learning algorithms in ophthalmology. Asia-Pacific Journal of Ophthalmology. 2019;8(3):264-72. [DOI]
3. Braukmann TW, Ivanova NV, Prosser SW, Elbrecht V, Steinke D, Ratnasingham S, et al. Metabarcoding a diverse arthropod mock community. Molecular Ecology Resources. 2019;19(3):711-27. DOI: 10.1111/1755-0998.12994 [DOI] [PMID] [PMCID]
4. Caraballo H, King K. Emergency department management of mosquito-borne illness: malaria, dengue, and West Nile virus. Emergency Medicine Practice. 2014;16(5):1-23; quiz 24. DOI: 10.1111/acem.12434 [DOI] [PMID]
5. Cardoso P, Erwin TL, Borges PA, New TR. The seven impediments in invertebrate conservation and how to overcome them. Biological Conservation. 2011;144(11):2647-55. DOI: 10.1016/j.biocon.2011.07.024 [DOI]
6. Chari K, Agrawal M. Impact of incorrect and new requirements on waterfall software project outcomes. Empirical Software Engineering. 2018;23:165-85. [DOI]
7. Dong X, Yan N, Wei Y, editors. Insect sound recognition based on convolutional neural network. 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC); 2018: IEEE. [DOI]
8. Elbrecht V, Braukmann TW, Ivanova NV, Prosser SW, Hajibabaei M, Wright M, et al. Validation of COI metabarcoding primers for terrestrial arthropods. PeerJ. 2019;7:e7745. DOI: 10.7717/peerj.7745 [DOI] [PMID] [PMCID]
9. Elbrecht V, Leese F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass-sequence relationships with an innovative metabarcoding protocol. PLoS One. 2015;10(7):e0130324. DOI: 10.1371/journal.pone.0130324 [DOI] [PMID] [PMCID]
10. Gebru A, Jansson S, Ignell R, Kirkeby C, Prangsma JC, Brydegaard M. Multiband modulation spectroscopy for the determination of sex and species of mosquitoes in flight. Journal of Biophotonics. 2018;11(8):e201800014. DOI: 10.1002/jbio.201800014 [DOI] [PMID]
11. Genoud AP, Basistyy R, Williams GM, Thomas BP. Optical remote sensing for monitoring flying mosquitoes, gender identification and discussion on species identification. Applied Physics B. 2018;124:1-11. [DOI] [PMID] [PMCID]
12. González García C, Núñez Valdéz ER, García Díaz V, Pelayo García-Bustelo BC, Cueva Lovelle JM. A review of artificial intelligence in the internet of things. International Journal of Interactive Multimedia and Artificial Intelligence. 2019;5. [DOI]
13. Holmström O, Stenman S, Suutala A, Moilanen H, Kücükel H, Ngasala B, et al. A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy. PLoS One. 2020;15(11):e0242355. DOI: 10.1371/journal.pone.0242355 [DOI] [PMID] [PMCID]
14. Hortal J, de Bello F, Diniz-Filho JAF, Lewinsohn TM, Lobo JM, Ladle RJ. Seven shortfalls that beset large-scale knowledge of biodiversity. Annual Review of Ecology, Evolution, and Systematics. 2015;46:523-49. DOI: 10.1146/annurev-ecolsys-112414-054133 [DOI]
15. Høye TT, Ärje J, Bjerge K, Hansen OL, Iosifidis A, Leese F, et al. Deep learning and computer vision will transform entomology. Proceedings of the National Academy of Sciences. 2021;118(2):e2002545117. DOI: 10.1073/pnas.2002545117 [DOI] [PMID] [PMCID]
16. Javaid M, Haleem A, Singh RP, Suman R, Rab S. Significance of machine learning in healthcare: Features, pillars and applications. International Journal of Intelligent Networks. 2022;3:58-73. DOI: 10.1016/j.ijin.2022.05.002 [DOI]
17. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255-60. [DOI] [PMID]
18. Južnič-Zonta Ž, Sanpera-Calbet I, Eritja R, Palmer JR, Escobar A, Garriga J, et al. Mosquito alert: leveraging citizen science to create a GBIF mosquito occurrence dataset. Gigabyte. 2022 [Google Scholar]
19. Kalamatianos R, Karydis I, Doukakis D, Avlonitis M. DIRT: The dacus image recognition toolkit. Journal of Imaging. 2018;4(11):129. DOI: 10.3390/jimaging4110129 [DOI]
20. Ke-Chen S, Yun-Hui Y, Wen-Hui C, Zhang X. Research and perspective on local binary pattern. Acta Automatica Sinica. 2013;39(6):730-44. [DOI]
21. Khalighifar A, Jiménez-García D, Campbell LP, Ahadji-Dabla KM, Aboagye-Antwi F, Ibarra-Juárez LA, et al. Application of deep learning to community-science-based mosquito monitoring and detection of novel species. Journal of Medical Entomology. 2022;59(1):355-62. DOI: 10.1093/jme/tjab134 [DOI] [PMID]
22. Khalighifar A, Komp E, Ramsey JM, Gurgel-Gonçalves R, Peterson AT. Deep learning algorithms improve automated identification of Chagas disease vectors. Journal of Medical Entomology. 2019;56(5):1404-10. DOI: 10.1093/jme/tjz099 [DOI] [PMID]
23. Kirkeby C, Rydhmer K, Cook SM, Strand A, Torrance MT, Swain JL, et al. Advances in automatic identification of flying insects using optical sensors and machine learning. Scientific Reports. 2021;11(1):1555. DOI: 10.1038/s41598-021-81034-9 [DOI] [PMID] [PMCID]
24. Kiskin I, Zilli D, Li Y, Sinka M, Willis K, Roberts S. Bioacoustic detection with wavelet-conditioned convolutional neural networks. Neural Computing and Applications. 2020;32:915-27. [DOI]
25. Kittichai V, Pengsakul T, Chumchuen K, Samung Y, Sriwichai P, Phatthamolrat N, et al. Deep learning approaches for challenging species and gender identification of mosquito vectors. Scientific Reports. 2021;11(1):4838. DOI: 10.1038/s41598-021-84411-4 [DOI] [PMID] [PMCID]
26. Krampa FD, Aniweh Y, Awandare GA, Kanyong P. Recent progress in the development of diagnostic tests for malaria. Diagnostics. 2017;7(3):54. DOI: 10.3390/diagnostics7030054 [DOI] [PMID] [PMCID]
27. Krehenwinkel H, Wolf M, Lim JY, Rominger AJ, Simison WB, Gillespie RG. Estimating and mitigating amplification bias in qualitative and quantitative arthropod metabarcoding. Scientific Reports. 2017;7(1):17668. DOI: 10.1038/s41598-017-17944-4 [DOI] [PMID] [PMCID]
28. Kumari U, Memon MM, Narejo S, Afzal M, editors. Malaria disease detection using machine learning. 2nd International Conference on Computational Sciences and Technologies (INCCST 20); 2020. [Google Scholar]
29. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44. DOI: 10.1038/nature14539 [DOI] [PMID]
30. Ligsay A, Telle O, Paul R. Challenges to mitigating the urban health burden of mosquito-borne diseases in the face of climate change. International Journal of Environmental Research and Public Health. 2021;18(9):5035. DOI: 10.3390/ijerph18095035 [DOI] [PMID] [PMCID]
31. Lipton R, Schwedt T, Friedman B, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1545-602. DOI: 10.1016/S0140-6736(16)31678-6 [DOI] [PMID]
33. Montgomery GA, Dunn RR, Fox R, Jongejans E, Leather SR, Saunders ME, et al. Is the insect apocalypse upon us? How to find out. Biological Conservation. 2020;241:108327. DOI: 10.1016/j.biocon.2020.108327 [DOI]
34. Ong OT, Kho EA, Esperança PM, Freebairn C, Dowell FE, Devine GJ, Churcher TS. Ability of near-infrared spectroscopy and chemometrics to predict the age of mosquitoes reared under different conditions. Parasites & Vectors. 2020;13(1):1-10. [DOI] [PMID] [PMCID]
35. Organization WH. World malaria report 2014: summary. World Health Organization; 2015. [URL]
36. Parveen R, Jalbani AH, Shaikh M, Memon KH, Siraj S, Nabi M, Lakho S. Prediction of malaria using artificial neural network. Int J Comput Sci Netw Secur. 2017;17(12):79-86. [Google Scholar]
37. Pecere S, Milluzzo SM, Esposito G, Dilaghi E, Telese A, Eusebi LH. Applications of artificial intelligence for the diagnosis of gastrointestinal diseases. Diagnostics. 2021;11(9):1575. DOI: 10.3390/diagnostics11091575 [DOI] [PMID] [PMCID]
38. Peiffer-Smadja N, Dellière S, Rodriguez C, Birgand G, Lescure F-X, Fourati S, et al. Machine learning in the clinical microbiology laboratory: has the time come for routine practice? Clinical Microbiology and Infection. 2020;26(10):1300-9. DOI: 10.1016/j.cmi.2020.06.024 [DOI] [PMID] [PMCID]
39. Potamitis I, Eliopoulos P, Rigakis I. Automated remote insect surveillance at a global scale and the internet of things. Robotics. 2017;6(3):19. DOI: 10.3390/robotics6030019 [DOI]
40. Raja DB, Mallol R, Ting CY, Kamaludin F, Ahmad R, Ismail S, et al. Artificial intelligence model as predictor for dengue outbreaks. Malaysian Journal of Public Health Medicine. 2019;19(2):103-8. [DOI]
41. Redmon J, Farhadi A. Yolov3: An incremental improvement. arXiv preprint arXiv:180402767. 2018. [Google Scholar]
42. Sun Y, Liu X, Yuan M, Ren L, Wang J, Chen Z. Automatic in-trap pest detection using deep learning for pheromone-based Dendroctonus valens monitoring. Biosystems Engineering. 2018;176:140-50. DOI: 10.1016/j.biosystemseng.2018.08.012 [DOI]
43. Wagner DL. Insect declines in the Anthropocene. Annual Review of Entomology. 2020;65:457-80. [DOI] [PMID]
44. da Silva Motta D, Badaró R, Santos A, Kirchner F. Use of Artificial Intelligence on the Control of Vector-Borne Diseases. Vectors and Vector-Borne Zoonotic Diseases. 2018. DOI: 10.1089/vbz.2017.2234 [DOI] [PMID] [PMCID]
45. Ärje J, Melvad C, Jeppesen MR, Madsen SA, Raitoharju J, Rasmussen MS, et al. Automatic image‐based identification and biomass estimation of invertebrates. Methods in Ecology and Evolution. 2020;11(8):922-31. DOI: 10.1111/2041-210X.13444 [DOI]
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