Diagnostics
While AI has been successful in correctly identifying 88% of malarial infection, more needs to be done to improve its accuracy.
cases, such as identifying a single parasite in a field of view. By contrast, the AI system simplifies the process: technicians only need to load the slide, and the automated microscope manages the diagnosis with pre-trained software.
That said, the initial AI training process itself was extensive and required several different stages and thousands of thousands of slides to teach the system to identify malaria parasites. Once the initial training stage, which took place in Seattle, where the software was designed, was complete, the team at UCLH were called upon to help before receiving the microscope.
“AI is just one prong in the arsenal. But the fact that it’s not been worked on for very long and is already working well is very impressive.”
“We were sent a load of records of files full of examples of samples that the device had read out and then we had to confirm whether the microscope was right or not. So our expert microscopists spent a period of time looking over these tiles, which gave the software creators the final layer they needed,” Rees- Channer recalls. “It was a complicated process before we even got the device.”
A valuable support tool
The automated system achieved an 88% accuracy rate but also falsely identified 122 samples as positive, which could lead to unnecessary anti-malarial treatment, so there is clearly significant potential for improvement. “It’s a step on the road; it’s not the endpoint,” Rees- Channer explains. “AI improves as it’s trained, so the more data it processes, the better it gets. It performed particularly well with P. falciparum, which is great.
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With more slides and more images, including false positives, true negatives and true positives, the system’s accuracy will only continue to improve.” Clinical trials, like the one conducted at UCLH, are critical to pinpoint areas for refinement. “We’re moving in the right direction, but there’s still work to be done,” she adds. While not a replacement for human expertise, the AI system could prove to be a valuable support tool, particularly in overwhelmed labs in remote areas. It can oversee initial slide readings, reducing the workload for technicians, although samples would still potentially have to be sent to specialists for confirmation. “It’s a support guide rather than a final result,” Rees-Channer stresses.
One prong in the arsenal
Looking ahead, Rees-Channer is no longer working on AI-assisted malaria diagnostics, but she does remain deeply involved in malaria research. Her current research focuses on antibody responses to single infections over time, exploring different transmission loci. When asked about the future of malaria innovations, she once again emphasises the need for a multi-pronged approach. “Tackling malaria has always been multifactorial,” she says. “The more strategies we have, the closer we’ll get to solving the problem, and it’s going to vary case by case.” AI-powered diagnostics is just one such tool. Another critical area is the detection of drug- resistance markers, vital in regions where resistance to treatment is a growing issue. Alongside these, advancements in malaria vaccines, vector control strategies and improved drug therapies – especially those with fewer side effects – are essential. “AI diagnostics is just one prong in the arsenal,” Rees-Channer adds. “But the fact that it’s not been worked on for very long and is already working well is very impressive.”
Practical Patient Care /
www.practical-patient-care.com
Jarun Ontakrai/
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