An AI-Powered Diagnostic Tool for Plasmodium Malaria: A Case Study on Microscopic Image Analysis - Junaiddin Junaiddin; Andirwana Andirwana; Muhamad Faizal Arianto; Astuti R Astuti R; Andi Sulfikar; Muhammad Rivaldi; Khusnul Sari Wahyuni (Indonesian Journal of Global Health Research, 2025)
Malaria is still become disease the infection that has the biggest impact to health humans all over the world. When parasites Plasmodium enter into blood can influence physiology general body host, so that can cause disruption of hematological parameters which can cause a number of manifestation clinical such as anemia and thrombocytopenia so that can make things worse Health conditions. Identification in a way accurate infection parasite plasmodium is very important for determine therapy that will given with right. Technology intelligence artificial intelligence (AI) offers solution innovative For identify parasite Plasmodium Malaria with more efficient and accurate. AI, especially that based on deep learning and computer vision, can analyze picture microscopic with high speed and accuracy so that help speed up diagnosis and treatment in Medical decision. For develop and test system based intelligence artificial that can in a way automatic classify type parasite Plasmodium Malaria based on picture microscopic. Type study This covering image data set collection parasite Plasmodium malaria from laboratory medical, data labeling, and training of deep learning models based on convolutional neural networks (CNN). This model will evaluated based on accuracy, sensitivity, and specificity in differentiate various type Plasmodium. Type study is combination between experimental, quantitative and developmental AI technology in microbiology medical. Malaria Level (Mild, Moderate, Severe) and Duration of AI Use (Sig = 0.004). While Malaria Level and Duration Inspection Microscopic Conventional show significant and relevant results, which strengthen urgency innovation AI-based. With mark significance (Sig) 0.006, AI (Artificial Intelligence) is effectively superior in the diagnosis of Severe Malaria in endemic areas.