The Oxford Academic Health Partners is part of a nationally and globally acclaimed ecosystem that brings together the best in academia, the NHS, research, teaching and education and the life sciences sector.
To celebrate this, and to highlight the work of partners, we are launching a series of case studies. The first of these is from Optellum, a company based in Oxford which has gathered the largest real-world dataset of CT scans and, using Artificial Intelligence and Machine Learning, developed a solution to enable clinicians to identify at-risk patients and to guide lung cancer management.
Further information can be found at www.Optellum.com
Case study: How AI could revolutionise lung cancer diagnosis and treatment
A ground-breaking machine-learning imaging-based artificial intelligence neural network can recognise the signs of lung cancer, enabling early interception and giving doctors a huge opportunity to get patients treated before the disease has metastasised, crucially increasing survival rates.
The AI is part of software, Virtual Nodule Clinic, has achieved FDA clearance in the United States and is on track to receive CE marketing in 2021, which will allow it to be deployed in NHS Trusts. The Optellum technology has been developed and clinically-validated in partnership with several NHS hospitals – including Oxford University Hospitals NHS Foundation Trust (OUH).
The initial origins of the collaboration date back to a relationship between Mirada Medical which had an industry-led Innovate UK grant where Oxford University Hospitals was a clinical partner. Optellum later licenced the research proof-of-concept from Mirada and went on to develop a commercial product. Since then OUH has worked with Optellum on a number of grant funded projects including Data using Artificial Intelligence to Improve Patient Outcomes with Thoracic Diseases (DART) funded by the UK Research and Innovation’s Industrial Strategy Challenge fund, the LUCINDA project funded by EIT Health, the IDEAL project funded by NIHR, and the DOLCE project funded by NH
Further information here