The Advancements in diagnostic approaches for malaria and dengue fever cases in Indonesia and Nigeria

Background : Malaria and dengue fever are endemic in both of Indonesia and Nigeria, sharing overlapping clinical symptoms that challenge accurate diagnosis. Laboratory assays such as RT-PCR and serological tests are essential for rapid and precise case confirmation. Methods: 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 article is the first comprehensive review comparing diagnostic advancements for malaria and dengue across two tropical countries (Indonesia, Nigeria), merging AI and biosensor technologies to inform future diagnostic tool development.