The AI-powered Ocular diagnostic tool, developed under the Makerere University AI Health Lab, is proving to be a cost-effective and time-saving innovation in the detection of malaria parasites, according to project leaders responses to questions during a World Malaria Day 2025 webinar.

Responding to a querries on the economic viability of the tool, Dr. Rose Nakasi, the lead researcher on the Ocular project, highlighted the tool’s ability to significantly cut down diagnostic time and increase patient throughput without compromising accuracy.

“According to WHO guidelines, a single lab technician can reliably analyze only 30 to 40 slides per day using conventional microscopy,” said Dr. Nakasi. “This is due to the time-intensive nature of confirming malaria negativity—technicians must examine over 100 fields of view per slide, taking up to 30 minutes per patient.”

By contrast, the Ocular tool, powered by artificial intelligence can process and analyze a slide in under two seconds. The system uses a smartphone mounted on a standard microscope with a low-cost 3D-printed adapter to capture images of stained blood smears. The AI then detects and quantifies malaria parasites, dramatically improving diagnostic speed and coverage.

Dr. Nakasi emphasized that the tool’s low infrastructure requirements enhance its cost-effectiveness. “Most public health facilities at level three and above already have functioning microscopes,” she explained. “What we provide is a printable adapter and a smartphone affordable items when compared to the cost of misdiagnosis or delayed care.”

On the issue of clinical value, Dr. Alfred Andama, a chief laboratory technician of the project, explained the medical significance of the increased detection rate. “Yes, we encountered instances where human technicians missed parasites that the tool detected,” he confirmed. “This is crucial because undiagnosed infections, even with low parasite density, can still cause symptoms and contribute to transmission.”

Dr. Andama also noted that improved detection does not mean over-diagnosis. “The AI tool is trained to identify parasites with high precision and can support clinicians in confirming borderline cases, especially in high-burden areas,” he said. This, he added, has implications for timely treatment and disease control.

The cost of deploying the Ocular tool beyond hardware includes training, linkage to electronic health records, and integration with national health data systems for real-time reporting. However, both Dr. Nakasi and Dr. Andama stressed that the long-term savings in technician time, improved diagnostic accuracy, and faster patient management, far outweigh initial setup expenses.

In conclusion, the Ocular diagnostic tool not only promises improved clinical outcomes through faster and more sensitive detection of malaria but also presents a scalable and cost-efficient solution for resource-limited settings. Researchers hope its successful pilot will pave the way for broader national and regional adoption.

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