Automated Diagnosis of Pulmonary Embolism on CTPA
Research type
Research Study
Full title
Developing a deep learning model for the automated detection of pulmonary embolism on computed tomography pulmonary angiogram.
IRAS ID
341910
Contact name
Stephanie Lua
Contact email
Sponsor organisation
NHS Golden Jubilee National Hospital
Duration of Study in the UK
1 years, 0 months, 1 days
Research summary
This study aims to develop a deep learning model using artificial intelligence (AI) that can independently detect pulmonary thromboembolism (clots in the lungs) on CT scans.
This AI model can be run on a standard CT scan obtained in patients who are undergoing normal treatment for their condition ( standard of care), and does not require any additional radiation or scanning requirements.
This will be performed in conjunction with the medical research arm of Canon Medical (a company which makes CT scanners).The Canon engineers will develop a computer model trained on publicly available CT scans, and we will validate this model on retrospective scans of patients with known clots in the lungs, under the care of the Scottish Pulmonary Vascular Unit. This data will be anonymized prior to transferring to Canon Medical.
We aim to compare the results of the scans between an official radiology report, and the AI model, to calculate the sensitivity and specificity of this model.
REC name
London - Hampstead Research Ethics Committee
REC reference
24/PR/1481
Date of REC Opinion
16 Jan 2025
REC opinion
Further Information Favourable Opinion