Artificial Intelligence for Leg Ulcer Classification
Research type
Research Study
Full title
Machine learning based classification of lower extremity ulcers: a feasibility study
IRAS ID
320491
Contact name
Pasha Normahani
Contact email
Sponsor organisation
Imperial Collge London
Duration of Study in the UK
1 years, 3 months, 20 days
Research summary
Around 50 million people around the world will develop a leg ulcer every year. It is very important that these ulcers are recognised and managed as quickly as possible. Unfortunately, a lot of ulcers are still not managed as quickly as they should be. An intelligent computer tool (also known as artificial intelligence, AI) that can identify and differentiate leg ulcers can help us reduce the delay in treatment of leg ulcers. We propose to carry out this study to train the automatic electronic tool to identify and differentiate different leg ulcers.
Participation in the study will be conducted over one appointment which will coincide with a routine clinical visit.
The following steps will be taken at the appointment, which will last approximately 10 minutes:
• Participants will be asked to sign the consent form.
• A member of the research team will take a series of photos from their leg ulcer. All photos will be pseudonymised, and no clinical information will be collected, stored or shared in our study.
• Approximately 50 photos will be taken of the leg ulcer. This should take no longer than 10 minutes.
• The pictures will be stored and analysed on NHS computers which are password protected.Participants can decide to leave the study at any time. They do not need to give a reason.
The information from this study will be used to train an intelligent computer tool (artificial intelligence, AI) to identify and differnetiate leg ulcers diagnosis. Early identification and classification can help us reduce the delay in treatment of leg ulcers.
REC name
South West - Cornwall & Plymouth Research Ethics Committee
REC reference
23/SW/0005
Date of REC Opinion
19 Jan 2023
REC opinion
Favourable Opinion