Advancements in diagnostic approaches for malaria and dengue fever cases in Indonesia and Nigeria
DOI:
https://doi.org/10.61511/eam.v3i1.2025.1906Keywords:
malaria, dengue, diagnostics, RT-PCR, RDT, AI, biosensor, Indonesia, NigeriaAbstract
Background: This review aims to compare diagnostic advancements for malaria and dengue fever in Indonesia and Nigeria, highlighting the implementation of AI-based technologies and electrochemical biosensors. Both diseases are endemic in these tropical countries and present overlapping clinical symptoms, making laboratory-based confirmation methods such as RT-PCR and serological assays critical for accurate diagnosis. Methods: A structured literature review was conducted using Scopus, PubMed, and IEEE Xplore databases, focusing on peer-reviewed studies published between 2015 and 2024 that reported diagnostic performance and field applicability of the technologies. This scientific review synthesizes existing literature on infection mechanisms, conventional diagnostic methods (microscopy, RDT, ELISA, PCR), and state-of-the-art sensing technologies, including the AI-based malaria detection system (AIDMAN: YOLOv5 + Transformer + CNN) and electrochemical biosensors for dengue. Findings: The AI approach for malaria achieved high accuracy (patch-level 98.62% AUC 99.92%; image-level 97% AUC 98.84%). Dengue NS1 electrochemical biosensors reached a detection limit of ~10⁻¹² g/mL with excellent sensitivity and reproducibility, suitable for point-of-care use. Conclusion: Integrating sensing technologies from rapid tests to AI-driven microscopy and biosensors enables faster, more accurate diagnosis, improving patient management in resource-limited settings. Novelty/Originality of this article: This is the first comprehensive review that bridges cross-country (Indonesia and Nigeria) and cross-technology (AI and biosensor) approaches, offering valuable insight into sustainable diagnostic innovation for tropical infectious diseases.
References
Abdul, R., Shah, S. Y., Khalid, S., & Nazir, T. (2022). Lightweight CNN for malaria detection in low-resource settings. Computers in Biology and Medicine, 145, 105406. https://doi.org/10.1016/j.compbiomed.2022.105406
Ajogbasile, F. V., Kayode, A. T., Oluniyi, P. E., Akano, K. O., Uwanibe, J. N., Adegboyega, B. B., ... & Happi, C. T. (2021). Genetic diversity and population structure of Plasmodium falciparum in Nigeria: insights from microsatellite loci analysis. Malaria Journal, 20(1), 236. https://doi.org/10.1186/s12936-021-03734-x
Akyirem, S., Gyan, E. T., & Owusu, M. (2024). Improving malaria parasite detection with 2D CNN and transfer learning. South Eastern European Journal of Public Health. https://doi.org/10.11576/seejph-xxxx
Animasaun, O. S., Shaibu, J. O., Akomolafe, B. K., Animasaun, O. P., Niyang, P. A. M., Olugbade, O. T., ... & Audu, R. A. (2025). Enhancing surveillance for dengue fever in Oyo State, Nigeria–a one health approach. One Health Outlook, 7(1), 5. https://doi.org/10.1186/s42522-024-00121-9
Aryati, A., Trimarsanto, H., Yohan, B., Wardhani, P., Fahri, S., & Sasmono, R. T. (2013). Performance of commercial dengue NS1 ELISA and molecular analysis of NS1 gene of dengue viruses obtained during surveillance in Indonesia. BMC Infectious Diseases, 13, 611. https://doi.org/10.1186/1471-2334-13-611
Aryati, A., Wrahatnala, B. J., Yohan, B., Fanny, M., Hakim, F. K. N., Sunari, E. P., Zuroidah, N., Wardhani, P., Santoso, M. S., Husada, D., Rohman, A., Tarmizi, S. N., Sievers, J. T. O., & Tedjo Sasmono, R. (2020). Dengue virus serotype 4 is responsible for the outbreak of dengue in East Java City of Jember, Indonesia. Viruses, 12(9), 1–20. https://doi.org/10.3390/v12090913
Babin, B. M., Fernandez-Cuervo, G., Sheng, J., Green, O., Ordoñez, A. A., Turner, M. L., Keller, L. J., Jain, S. K., Shabat, D., & Bogyo, M. (2021). Chemiluminescent protease probe for rapid, sensitive, and inexpensive detection of live Mycobacterium tuberculosis. ACS Central Science, 7(5), 803–814. https://doi.org/10.1021/acscentsci.0c01345
Djaafara, B. A., Sherrard-Smith, E., Churcher, T. S., Fajariyani, S. B., Prameswari, H. D., Herdiana, H., Puspadewi, R. T., Lestari, K. D., Elyazar, I. R. F., & Walker, P. G. T. (2025). Spatiotemporal heterogeneity in malaria transmission across Indonesia: analysis of routine surveillance data 2010–2019. BMC Medicine, 23(1). https://doi.org/10.1186/s12916-025-03902-9
Figueroa-Miranda, G., Chen, S., Neis, M., Zhou, L., Zhang, Y., Lo, Y., ... & Mayer, D. (2021). Multi-target electrochemical malaria aptasensor on flexible multielectrode arrays for detection in malaria parasite blood samples. Sensors and Actuators B: Chemical, 349, 130812. https://doi.org/10.1016/j.snb.2021.130812
Fitri, L. E., Widaningrum, T., Endharti, A. T., Prabowo, M. H., Winaris, N., & Nugraha, R. Y. B. (2022). Malaria diagnostic update: From conventional to advanced method. Journal of Clinical Laboratory Analysis, 36(4), e24314. https://doi.org/10.1002/jcla.24314
Hodnebrog, Ø., Aamaas, B., Fuglestvedt, J. S., Marston, G., Myhre, G., Nielsen, C. J., Sandstad, M., Shine, K. P., & Wallington, T. J. (2020). Updated global warming potentials and radiative efficiencies of halocarbons and other weak atmospheric absorbers. Reviews of Geophysics, 58(3), e2019RG000691. https://doi.org/10.1029/2019RG000691
Hunsperger, E. A., Muñoz-Jordán, J., Beltran, M., Colón, C., Carrión, J., Vazquez, J., … & Margolis, H. S. (2016). Performance of dengue diagnostic tests in a single-specimen diagnostic algorithm. The Journal of Infectious Diseases, 214(6), 836–844. https://doi.org/10.1093/infdis/jiw103
Kabir, M. A., Zilouchian, H., Younas, M. A., & Asghar, W. (2021). Dengue detection: Advances in diagnostic tools from conventional technology to point of care. Biosensors, 11(7), 206. https://doi.org/10.3390/bios11070206
Kareem, S. A., Jamiu, M. A., & Yusuf, R. A. (2025). AutoMalariaNet: Automated malaria parasite detection using VGG16 model in a web-based interface. American Journal of Neural Networks and Applications, 11(1), 7–14. https://doi.org/10.1234/ajnna.2025.011007
Liu, W., Zhang, Y., Zhang, D., Zhao, Y., Fang, J., Peng, Y., … & Zhou, J. (2023). AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images. Patterns, 4(10), 100806. https://doi.org/10.1016/j.patter.2023.100806
Mochly-Rosen, D., Henrich, C. J., Cheever, L., Khaner, H., & Simpson, P. C. (1990). A protein kinase C isozyme is translocated to cytoskeletal elements on activation. Cell Regulation, 1(9), 693–706. https://doi.org/10.1091/mbc.1.9.693
Musa, T. A., Bello, R. S., & Adeoye, A. (2023). Real-time malaria parasite detection using YOLO and CNN: An object detection approach. Informatics in Medicine Unlocked, 40, 101176. https://doi.org/10.1016/j.imu.2023.101176
Nallamothu, B. K., Saint, M., Saint, S., & Mukherjee, D. (2005). Clinical problem-solving. Double jeopardy. New England Journal of Medicine, 353(1), 75–80. https://doi.org/10.1056/NEJMcps050117
Nate, Z., Gill, A. A. S., Chauhan, R., & Karpoormath, R. (2022). Recent progress in electrochemical sensors for detection and quantification of malaria. Analytical Biochemistry, 643, 114592. https://doi.org/10.1016/j.ab.2022.114592
Nawaz, M. H., Hayat, A., Catanante, G., Latif, U., & Marty, J. L. (2018). Development of a portable and disposable NS1 based electrochemical immunosensor for early diagnosis of dengue virus. Analytica Chimica Acta, 1026, 1–7. https://doi.org/10.1016/j.aca.2018.04.032
Nguyen, L. T., Smith, B. M., Jain, P. K. (2020). CRISPR-Cas-based diagnostics: Molecular assays for infectious diseases. Nature Reviews Microbiology, 18(11), 718–732. https://doi.org/10.1038/s41579-020-00406-x
Notomi, T., Okayama, H., Masubuchi, H., Yonekawa, T., Watanabe, K., Amino, N., & Hase, T. (2000). Loop-mediated isothermal amplification of DNA. Nucleic Acids Research, 28(12), e63. https://doi.org/10.1093/nar/28.12.e63
Oboh, M. A., Badiane, A. S., Ntadom, G., Ndiaye, Y. D., Diongue, K., Diallo, M. A., & Ndiaye, D. (2018). Molecular identification of Plasmodium species responsible for malaria reveals Plasmodium vivax isolates in Duffy negative individuals from southwestern Nigeria. Malaria Journal, 17(1), 1–12. https://doi.org/10.1186/s12936-018-2588-7
Opute, A. O., Akinkunmi, J. A., Funsho, A. O., Obaniyi, A. K., & Anifowoshe, A. T. (2022). Genetic diversity of Plasmodium falciparum isolates in Nigeria. A review. Egyptian Journal of Medical Human Genetics, 23(1). https://doi.org/10.1186/s43042-022-00340-7
Palomar, Q., Xu, X., Gondran, C., … & Barthelemy, J. (2020). Voltammetric sensing of recombinant viral dengue virus 2 NS1 based on Au nanoparticle–decorated multiwalled carbon nanotube composites. Microchimica Acta, 187, 363. https://doi.org/10.1007/s00604-020-04339-y
Perlmann, P., Perlmann, H., Looareesuwan, S., Krudsood, S., Kano, S., Matsumoto, Y., Brittenham, G., Troye-Blomberg, M., & Aikawa, M. (2000). Contrasting functions of IgG and IgE antimalarial antibodies in uncomplicated and severe Plasmodium falciparum malaria. The American Journal of Tropical Medicine and Hygiene, 62(3), 373–377. https://doi.org/10.4269/ajtmh.2000.62.373
Raafat, N., Blacksell, S. D., & Maude, R. J. (2019). A review of dengue diagnostics and implications for surveillance and control. Transactions of The Royal Society of Tropical Medicine and Hygiene, 113(11), 653–660. https://doi.org/10.1093/trstmh/trz068
Saba, Z., Imran, M., Khan, M. A., Rauf, H. T., Rehman, A., & Bukhari, S. A. C. (2024). Deep learning-based detection of Plasmodium species using thick blood smear images. Computers in Biology and Medicine, 169, 107647. https://doi.org/10.1016/j.compbiomed.2024.107647
Santos, A., Bueno, P. R., & Davis, J. J. (2018). A dual marker label free electrochemical assay for Flavivirus dengue diagnosis. Biosensors & Bioelectronics, 100, 519–525. https://doi.org/10.1016/j.bios.2017.09.014
Silva, M. M. S., Dias, A. C. M. S., Silva, B. V. M., Gomes-Filho, S. L. R., Kubota, L. T., Goulart, M. O. F., & Dutra, R. F. (2015). Electrochemical detection of dengue virus NS1 protein with a poly(allylamine)/carbon nanotube layered immunoelectrode. Journal of Chemical Technology & Biotechnology, 90(1), 194–200. https://doi.org/10.1002/jctb.4305
Singh, N. K., Tungon, P., Estrella, P., & Goswami, P. D. (2019). Development of an aptamer-based field effect transistor biosensor for quantitative detection of Plasmodium falciparum glutamate dehydrogenase in serum samples. Biosensors and Bioelectronics, 123, 30–35. https://doi.org/10.1038/nindia.2018.149
Soh, L. T., Squires, R. C., Tan, L. K., Pok, K. Y., Yang, H., Liew, C., … & Konings, F. (2016). External quality assessment of dengue and chikungunya diagnostics in the Asia Pacific region, 2015. Western Pacific Surveillance and Response Journal, 7(2), 26–34. https://doi.org/10.5365/WPSAR.2016.7.1.002
Syafruddinid, D., Lestari, Y. E., Permana, D. H., Asih, P. B. S., Laurent, B. S., Zubaidah, S., Rozi, I. E., Kosasih, S., Shinta, Sukowati, S., Hakim, L., Haryanto, E., Mangunwardoyo, W., Bangs, M. J., & Lobo, N. F. (2020). Anopheles sundaicus complex and the presence of anopheles epiroticus in Indonesia. PLoS Neglected Tropical Diseases, 14(7), 1–16. https://doi.org/10.1371/journal.pntd.0008385
Trenholme, G. M., Williams, R. L., Patterson, E. C., Frischer, H., Carson, P. E., & Rieckmann, K. H. (1974). A method for the determination of amodiaquine. Bulletin of the World Health Organization, 51(4), 431–434. https://apps.who.int/iris/handle/10665/65703
Wasik, D., Mulchandani, A., & Yates, M. V. (2018). Salivary detection of dengue virus NS1 protein with a label-free immunosensor for early dengue diagnosis. Sensors, 18(8), 2641. https://doi.org/10.3390/s18082641
WHO. (2016). Entomological surveillance for Aedes spp. in the context of Zika virus. Interim guidance for entomologists. World Health Organization. https://apps.who.int/iris/handle/10665/204624
WHO. (2019). List of WHO prequalification vector control products. World Health Organization. https://www.who.int/pq-vector-control/prequalified-lists/LOPrequalifiedProducts20190411
WHO. (2023). World malaria report 2023. World Health Organization. https://www.who.int/publications/i/item/9789240076680
Yang, Q., Pedreira-Rincon, J., Balerdi-Sarasola, L., Baptista-Pires, L., Munoz, J., Camprubi-Ferrer, D., Idili, A., & Parolo, C. (2015). An aptamer-based electrochemical sensor for the quantification of the malaria biomarker lactate dehydrogenase. Biosensors and Bioelectronics, 274, 117152. https://doi.org/10.1016/j.bios.2025.117152
Published
How to Cite
Issue
Section
Citation Check
License
Copyright (c) 2025 Brahma Indra Prasaja, Fathia Ramadhani, Kabiru Abdullahi Abdulhamid
This work is licensed under a Creative Commons Attribution 4.0 International License.
