A bold step forward in cancer care blends 3D printing with artificial intelligence to speed up diagnosis in Kenya
KENYA
Researchers from Meru University of Science and Technology (MUST) are pursuing a cheaper, faster, and more accurate way to identify cancer cells by marrying 3D printing with AI-enhanced imaging. Led by Dr. Daniel Maitethia, a physics lecturer and researcher, the team’s work has gained national attention for its potential to shorten the time needed to detect cancer cells.
Their invention is a 3D-printed telepathology microscope that incorporates AI to determine whether tissue samples are malignant or benign. Maitethia credits a 2023 study in the British Journal of Healthcare and Medical Research for sparking the project, which highlighted a rising cancer burden in Kenya and flagged Meru County as a particularly affected area. While Meru County inspired the creation, Maitethia envisions this technology addressing diagnostic gaps across the country.
From Malaria to Malignancy
The concept originated from Maitethia’s master’s thesis, which focused on building an AI system to spot Plasmodium parasites (the cause of malaria) in blood samples viewed under a light microscope. That work provided the technical foundation and hands-on experience now adapted for cancer detection.
The team aimed to design a smart, cost-effective, rapid, and portable microscope suitable for use outside well-equipped laboratories. The goal was to deliver a device that could operate in African settings with minimal infrastructure while maintaining high accuracy and speed. During development, researchers realized the same platform could be repurposed to identify cancer cells, offering a practical solution to Kenya’s cancer burden, Maitethia explained.
Key components of the microscope include 3D-printed plastic housing for the mechanical structure, optical elements to magnify cellular details, imaging electronics, and a compact bare-bones computer roughly the size of a credit card. This computer coordinates image capture and runs a bespoke AI model to analyze the samples.
Global Collaboration Enabled by Cloud-Based Review
When a cancer suspicion arises, a biopsy is taken, prepared, and imaged with the telescope-like microscope to study cell morphology. A pathologist then interprets the images to render a diagnosis. A major gap identified is the shortage of pathologists, particularly in Africa. The smart microscope addresses this by supporting whole-slide imaging: technicians capture tiled images across many microscopic fields, then the system stitches them into a single, high-resolution view of the entire slide and uploads it to the cloud.
From there, pathologists around the world can log in, review the images remotely, and contribute to a diagnostic report. This approach reduces delays caused by specialist availability and eliminates long waits, since patient data can be shared with experts online regardless of location.
Inexpensive, scalable, and accessible
The system promises affordable, reliable, and timely cancer diagnostics. Locally, the microscope can be produced for roughly KES 30,000 (about US$232), making it feasible for widespread use by doctors and hospitals nationwide. The project is designed to scale, with production capable of handling both small and large volumes based on demand.
AI as a linchpin
Since January 2025, the project has undergone multiple tests, including a successful pilot at Meru Teaching and Referral Hospital. Pathologists and medical technologists praised the prototype for dramatically reducing diagnostic times. Maitethia notes that the microscope has demonstrated exceptional accuracy in cancer detection, outperforming some trained human microscopists in trials conducted to date.
Viewed through Maitethia’s lens, AI is the essential bridge between physics and practical medicine. He explains its threefold impact:
- Addressing resource shortages: AI empowers medical lab technicians to perform tasks that ordinarily require specialist pathologists, enabling remote diagnoses via cloud systems.
- Boosting efficiency and access: By automating image analysis and enabling whole-slide imaging with remote review, AI expands access to dependable diagnostics in underserved African settings.
- Democratizing expertise: The microscope exemplifies how AI can spread specialized knowledge, delivering life-saving insights to communities that would otherwise face barriers to timely care.
And this is the part that could spark debate: while the approach offers remarkable promise, questions remain about data privacy, the reliability of remote diagnoses in diverse clinical contexts, and how best to regulate and integrate AI-assisted pathology into existing healthcare systems. As more regions consider adopting similar technologies, how should policymakers balance innovation with safeguards, and whose responsibility is it to ensure constant quality and accountability in AI-driven cancer diagnoses?
