Noninvasive malaria detection beyond blood sampling
Shaun G Hong1,2, Jung Woo Leem1, Haripriya Sakthivel1,3,2
1Weldon School of Biomedical Engineering, Purdue University West Lafayette Indiana 47907 USA.
Sensors & Diagnostics
|August 13, 2026
Summary
Noninvasive malaria detection offers a promising alternative to blood tests, utilizing accessible samples and advanced AI. This review explores new methods for accurate, field-ready malaria diagnostics.
Area of Science:
- Biomedical diagnostics
- Parasitology
- Artificial Intelligence in Healthcare
Background:
- Malaria remains a significant global health issue, with current blood-based diagnostics facing limitations.
- Existing methods require trained personnel, sterile conditions, and can miss low-parasitemia or asymptomatic cases.
Purpose of the Study:
- To review recent advancements in noninvasive malaria detection technologies.
- To explore diverse sampling matrices, detection mechanisms, and data-driven analytical approaches.
- To provide a roadmap for implementing noninvasive malaria diagnostics in healthcare systems.
Main Methods:
- Review of nonblood sampling matrices (urine, saliva, breath, VOCs).
- Categorization of detection technologies (biosensors, optical systems, digital health platforms).
- Analysis of modalities based on performance, suitability, and translational potential.
Main Results:
- Noninvasive methods leverage accessible biological samples and physiological signals.
- Diverse technologies show potential, including biosensors, optical systems, and AI-powered platforms.
- Integration of machine learning and AI enhances diagnostic capabilities.
Conclusions:
- Noninvasive malaria detection offers a path toward more accessible, sensitive, and patient-friendly diagnostics.
- Addressing translational, equity, AI, scalability, and regulatory factors is crucial for implementation.
- The review identifies practical strategies for developing field-ready, affordable, and programmatically relevant malaria detection tools.
