Automated malaria detection system developed using AI and a microscope

Each year, more than 200 million people fall sick with malaria and more than half a million of these infections lead to death.

The World Health Organization recommends parasite-based diagnosis before starting treatment for the disease. Doctors use a variety of diagnostic methods, including conventional light microscopy, rapid diagnostic tests and polymerase chain reaction.

The standard for malaria diagnosis remains manual light microscopy, during which a specialist examines blood films with a microscope to confirm the presence of malaria parasites. But the accuracy of the results depends on the skills of the microscopist.

“At an 88 per cent diagnostic accuracy rate relative to microscopists, the AI system identified malaria parasites almost, though not quite, as well as experts,” said Dr Roxanne Rees-Channer, a UCLH researcher.

“This level of performance in a clinical setting is a major achievement for AI algorithms targeting malaria. It indicates that the system can indeed be a clinically useful tool for malaria diagnosis in appropriate settings.”

The researchers sampled more than 1,200 blood samples of travellers who had returned to the UK from malaria-endemic countries. The study tested the accuracy of the AI and automated microscope system in a true clinical setting under ideal conditions.

They evaluated samples using both manual light microscopy and the AI-microscope system. By hand, 113 samples were diagnosed as malaria parasite positive, whereas the AI system correctly identified 99 samples as positive, which corresponds to an 88 per cent accuracy rate.

“AI for medicine often posts rosy preliminary results on internal datasets, but then falls flat in real clinical settings. This study independently assessed whether the AI system could succeed in a true clinical use case,” said Rees-Channer, who is also the lead author of the study.

The fully automated malaria diagnostic system uses an automated microscopy platform that scans blood films and a malaria detection algorithm that processes the image to detect parasites and the quantity present.

“Even expert microscopists can become fatigued and make mistakes, especially under a heavy workload,” Rees-Channer explained. “Automated diagnosis of malaria using AI could reduce this burden for microscopists and thus increase the feasible patient load.”

Furthermore, these systems deliver reproducible results and can be widely deployed, the scientists wrote.

Despite its high accuracy rate, the automated system also falsely identified 122 samples as positive, which can lead to patients receiving unnecessary anti-malarial drugs.

“The AI software is still not as accurate as an expert microscopist. This study represents a promising datapoint rather than a decisive proof of fitness,” Rees-Channer added.

Last year, University of Queensland researchers developed a chemical-free, needle-free tool to detect malaria through the skin using infrared light.