Diagnostics
endemic regions, largely due to a lack of skilled staff who are capable of conducting the process, which involves not only preparing blood films but also accurately detecting parasites, distinguishing species and avoiding misidentifications. “The main point here is that there’s a lack of expertise in general; there aren’t enough specialists to diagnose cases at the required level, nor the means to train them,” explains Roxanne Rees- Channer, a malaria researcher at the Hospital for Tropical Diseases at UCLH.
“There are established levels for microscopist expertise, and it’s rare to find a Level 1 microscopist in low-resource settings. Then you’ve got the fatigue issue. Imagine being in an area where there’s a lot of malaria where it takes 30 minutes to read a single slide. If you’re reading slide after slide all day, you’ll inevitably start making mistakes.” Plus, on the technical side, microscope maintenance is often inadequate in low-resource countries compared to wealthier nations where funding allows for regular upkeep. “In addition, if an experienced technician leaves to work elsewhere, it takes a long time to train a replacement,” Rees- Channer adds.
Given these challenges, other malaria diagnostic techniques have emerged over time, although none is a silver bullet. Molecular diagnostics, such as PCR and loop-mediated isothermal amplification (LAMP), for example, are more sensitive and specific than microscopy, but they are also more expensive and not widely available in most malaria-endemic regions. As Rees-Channer summarises, “These techniques are brilliant but we’re not able to get them to the places where they really need them.”
In addition, there are malaria rapid diagnostic tests (RDTs), which are very affordable – often less than $1 per test. “However, they are less effective at identifying anything beyond Plasmodium falciparum [the deadliest malaria parasite and the most prevalent on the African continent]. They’re also not quantitative – you can’t measure infection intensity,” Rees-Channer notes. Some Plasmodium falciparum parasites, particularly in Africa, have also developed gene deletions that render them undetectable by certain RDTs. “This means you could get a false-negative result when using an RDT. So, there’s a need for new diagnostics to compensate for these issues. It’s a multifactorial and complex problem.” It is also one that Rees-Channer is determined to contribute to solving. “I’ve been in malaria research for a while, and I’m very aware of the challenges across every branch of diagnosis. Diagnostics require a multi-pronged approach because it’s so complicated. No single method will ever be perfect, but I was keen to work on something that could fill a hole somewhere or resolve a specific problem,” she explains.
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www.practical-patient-care.com
AI vs expert microscopists Motivated by the potential of AI to revolutionise diagnostics, Rees-Channer joined an international team of researchers exploring its applications in the malaria field. It is one of many similar projects around the world focused on using AI to enhance the accuracy of diagnostic methods and reduce the burden on healthcare workers. Their study, which was carried out at UCLH and published in Frontiers in Malaria in 2023, evaluated whether a fully automated system – combining AI detection software with an automated microscope – could diagnose malaria with clinically useful accuracy. The goal was to compare this system with the gold standard: manual microscopy.
Manually detecting malaria requires highly trained microscopists who may find a helping hand in AI technologies.
“There are established levels for microscopist expertise, and it’s rare to find a Level 1 microscopist in low-resource settings.”
The researchers analysed over 1,200 blood samples from travellers returning to the UK from malaria-endemic regions, testing the accuracy of the AI and automated microscope system in a true clinical setting under ideal conditions. Using manual light microscopy, 113 samples were identified as positive for malaria, while the AI system correctly diagnosed 99 positive cases, achieving an 88% accuracy rate. “It’s a pretty good number by comparison to actual conventional expert microscopy,” Rees- Channer notes. The AI system also offers a significant advantage when it comes to speed. Conventional microscopy requires a technician to scan blood smears manually, identify species and count parasites – a process that takes at least 30 minutes per sample and demands extensive training. It can take months for a technician to become proficient and even longer to handle complex
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