Deep learning for HRCT classification of fibrotic lung disease
Research type
Research Study
Full title
Deep learning for HRCT classification of fibrotic lung disease
IRAS ID
234527
Contact name
Simon Walsh
Contact email
Sponsor organisation
Royal Brompton and Harefield Foundation Trust
Duration of Study in the UK
0 years, 5 months, 25 days
Research summary
The overall aim of this study is to use a number of different convoluted neural network architectures to provide a computer-based diagnosis in fibrotic lung disease. The study will involve 2 steps.
1. Training the computer algorithm: First, a number of different convoluted neural network (CNN) architectures will be trained on a training set of HRCT images in JPEG format. The training set will comprise of retrospective, clinically indicated HRCT scans of a patients with fibrotic lung disease performed since 2007 at the Royal Brompton and Harefield Foundation Trust (RB&HFT). These will be collected from the hospital PACS system and stored on an encrypted hard drive. Training of the computer algorithms will take place on a specialised "deep learning" computer owned by the chief investigator (Dr Simon Walsh). What makes this computer specialised is that it uses multiple high specification graphics processor unit cards (GPUs) which are capable of the massive numerical computation required for deep learning. This process is intractable on a standard (even high specification) desktop computer.
2. Testing the computer algorithm: These CNN's will then be tested classifying the anonymised HRCTs from a separate cohort test cases based on the ATS/ERS/JRS/ALAT HRCT categories for a UIP pattern (usual interstitial pneumonia (UIP), possible UIP and inconsistent with UIP), again, drawn from the PACs of the RB&HFT. Lastly, the best performing algorithm will be tested on a cohort of 150 cases of fibrotic lung disease already scored by 116 radiologists in a previously published (1).
(1) Interobserver agreement for the ATS/ERS/JRS/ALAT criteria for a UIP pattern on CT.
Walsh SL, Calandriello L, Sverzellati N, Wells AU, Hansell DM; UIP Observer Consort.Thorax. 2016 Jan;71(1):45-51.REC name
London - West London & GTAC Research Ethics Committee
REC reference
18/LO/1392
Date of REC Opinion
2 Aug 2018
REC opinion
Favourable Opinion