MultiplexAI

The challenge

Parasitic diseases, from malaria to neglected tropical diseases (NTDs) such as filariasis and trypanosomiasis, affect hundreds of millions of people worldwide, especially in low- and middle-income countries (LMICs). In many of these regions, diagnosis still relies on conventional optical microscopy.

Optical microscopy is the gold-standard method for diagnosing parasitic diseases, but it holds important limitations. The process is labour-intensive, depends heavily on the expertise of trained specialists, and results can vary significantly between observers. Shortages of skilled personnel and limited laboratory resources contribute to delays and misdiagnosis.

These challenges affect patient care and pose a public health challenge: delayed or incorrect diagnoses can worsen disease outcomes, increase transmission, and place additional strain on already fragile health systems.      

Recent advances in AI offer new opportunities to address this challenge. AI systems can analyse medical images and automatically identify patterns associated with parasites, improving point-of-care diagnosis and accelerating treatment for patients.

The project

MultiplexAI is transforming standard microscopes into intelligent digital diagnostic systems.

The system combines three main components:

• A low-cost 3D-printed adaptor that connects a smartphone or tablet to the microscope eyepiece to capture images of samples.
• A mobile application with embedded AI models that analyse microscopy images directly on the device, detecting, differentiating and quantifying parasites in real time.
• A cloud-based telemedicine platform where images and results can be securely stored, visualised and reviewed remotely by experts, enabling quality control, second opinions and improved epidemiological surveillance.

Together, these components upgrade existing microscopes into connected diagnostic tools that can support healthcare workers at the point of care.

The AI models are being trained using real-world data to recognise multiple parasites that provide clinically relevant information.

The project will evaluate the technology in both laboratory and clinical settings across several countries in sub-Saharan Africa (Nigeria, Mozambique, Ethiopia, and Côte d’Ivoire) comparing its performance with established diagnostic methods. In parallel, social science and health economic studies will assess usability, acceptability, and cost-effectiveness in real healthcare environments. Finally, a go-to-market strategy will be implemented to deliver the final validated product to healthcare communities worldwide.

Impact

MultiplexAI aims to make accurate parasite diagnosis more accessible and scalable by bringing AI-powered image analysis to routine microscopy. The project will:

• Provide a practical way to upgrade existing microscopes with AI-assisted diagnostics.
• Enable faster and more accurate detection of parasite infections, supporting timely treatment.
• Allow a wider range of healthcare workers to perform reliable microscopy analysis.
• Strengthen digital laboratory networks and epidemiological surveillance, while expanding local capacity to develop and use AI-enabled health technologies across sub-Saharan Africa.

By improving the speed and accuracy of parasite detection, the system could enable earlier diagnosis and treatment, reducing the risk of severe infection, limiting transmission, and strengthening health systems in regions where parasitic diseases continue to pose a major global health burden.