Advancements in diagnostic approaches for malaria and dengue fever cases in Indonesia and Nigeria - Prasaja, Brahma Indra; Ramadhani, Fathia; Abdulhamid, Kabiru Abdullahi (Institute for Advanced Science, Social, and Sustainable Future, 2025)
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.