Machine learning to diagnose PCD
Research type
Research Study
Full title
Improving Primary Ciliary Dyskinesia diagnosis using artificial intelligence
IRAS ID
259067
Contact name
Claire Hogg
Contact email
Sponsor organisation
Royal Brompton and Harefield NHS Foundation Trust
Duration of Study in the UK
2 years, 0 months, 0 days
Research summary
In the airway, cells are lined with many hair-like cilia that sweep in a coordinated way to clear mucus, bacteria and debris from the airway. In primary ciliary dyskinesia (PCD), problems with the movement of the cilia result in mucus build-up that can lead to recurring infections and damage to the lungs. Around 1 in 10,000 people have PCD in the UK, but it’s much more common in some communities. Early diagnosis is important to help prevent lung damage in childhood and to keep the lungs working as well as they can. Diagnosis is complex and currently relies on looking at cilia (taken from the nose) and checking the ciliary ultrastructure with a powerful electron microscope. It can be lengthy and difficult to make a diagnosis and experts can have different opinions on what the electron micrographs show. The aim of this project is to use Artificial Intelligence computing techniques to identify both healthy and abnormal cilia from electron microscopy images to provide a faster, more accurate and reliable approach to evaluate ciliary structure.
REC name
South Central - Oxford B Research Ethics Committee
REC reference
19/SC/0229
Date of REC Opinion
28 May 2019
REC opinion
Further Information Favourable Opinion