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is a global leader in medical imaging, designing, manufacturing, and supplying innovative imaging solutions that enhance and accelerate clinical diagnostic processes. The company focuses on improving well-being through cutting-edge innovation, with intelligent solutions aimed specifically at pathology and diagnostic efficiency.

The healthcare sector faces a growing shortage of pathologists and biomedical scientists, compounded by increasing demand for early detection testing and an ageing population. This workforce pressure can delay patient treatment and reduce outcomes. Cirdan sought solutions to improve diagnostic speed and accuracy, particularly in cancer detection, by integrating AI-driven analytics into their medical devices.

"At Cirdan, we are passionate about improving well-being through innovation and using digital health technology to assist clinicians in accelerating patient diagnosis. This project has demonstrated real benefits for the patients and clinicians we support, which will be the real testament to the great work achieved from this partnership."

— Hugh Cormican, CEO, Cirdan

Queen’s University ¶¶Òõ¶ÌÊÓƵ provided world-class expertise in AI, machine learning, and medical image analysis through the School of Electronics, Electrical Engineering and Computer Science. Academics Dr Paul Miller and Dr Jesus Martinez del Rincon supervised the project, offering guidance on deep learning models, algorithm validation, and cloud deployment.

The Knowledge Transfer Partnership (KTP) embedded Associate at Cirdan to lead the development of AI-powered diagnostic tools. The collaboration included:

• Developing an AI algorithm for Cirdan’s x-ray product, expediting breast cancer diagnosis globally.
• Creating a cloud-based AI algorithm for tumour detection in colon polyp whole slide images with 95% accuracy, in collaboration with consultant pathologist from the Royal Victoria Hospital, ¶¶Òõ¶ÌÊÓƵ.
• Deploying AI models via Azure cloud to ensure scalability, secure image transfer, and accessibility for clinicians worldwide.
• Regular meetings between Jonathan, Cirdan staff, and Queen’s academics to refine models, incorporate clinical expertise, and ensure regulatory readiness.

Jonathan’s work transformed theoretical AI concepts into practical, deployable healthcare solutions while embedding deep learning expertise within Cirdan.

The KTP transformed Cirdan’s AI concept into clinically relevant, scalable products that accelerate diagnostic workflows and support earlier detection of cancer. Clinicians and patients benefit from faster, more accurate results, while Cirdan has strengthened its position as a global provider of AI-enabled pathology solutions.

The collaboration also established a long-term capability within the company, embedding advanced analytics expertise, promoting data-driven innovation, and fostering ongoing partnerships with Queen’s University ¶¶Òõ¶ÌÊÓƵ. 

Impact related to the UN Sustainable Development Goals

Learn more about Queen¡¯s University¡¯s commitment to nurturing a culture of sustainability and achieving the Sustainable Development Goals (SDGs) through research and education.