Call for participants: Lung cancer detection reader study
Participant information
Optellum is an innovative MedTech company that has developed FDA and CE/UKCA-approved clinical decision support software for lung cancer diagnosis. Our artificial intelligence-enabled Lung Cancer Prediction (LCP) technology helps clinicians assess the likelihood of malignancy of pulmonary nodules. This validation study is being performed in collaboration with Leeds Teaching Hospitals NHS Trust.
Reader study
The study aims to evaluate the impact of Optellum’s AI-based assistance when assessing a CT scan for risk of lung cancer. Participants will be asked to:
- Localize and measure lung nodules;
- Assess the likelihood that any nodules on the CT scan represent primary lung cancer;
- Make a recommendation for the next clinical action;
- At a separate session, repeat the tasks above while having access to Optellum’s AI assistance.
The study comprises 240 CT scans, where each scan is read with and without Optellum’s assistance, as well as start and exit surveys. The total time investment will be between 30 and 34 hours (depending on experience) over a 3-month period, arranged to the full convenience of the participant (e.g. reads are performed by participants from any location using a web-based resource, at any time, and in as many separate sessions as required).
Benefits for readers
- Compensation: Receive financial compensation for your time, c.£6k-£8k depending on experience.
- Collaboration: Contribute to cutting-edge medical AI research and scientific outputs.
- Early Access: Gain exclusive insight into a new AI tool for lung cancer detection.
- Impact: Help shape the future of lung cancer detection.
Who can participate?
- Radiologists with at least 5 years of independent practice in the UK or EU.
- Available to read between June 16th 2025 and September 16th 2025.
Contact us
For further information, please contact studies@optellum.com
This study is funded by the National Institute for Health and Care Research (NIHR) and the Office for Life Sciences (OLS) under project ID NIHR207547.
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