Training of a Artificial Intelligence Model to Detect Venous Diseases Using PPG Technology
A Pilot Study Using AI Algorithms and PPG Technology for the Detection of Venous Diseases
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Sergio Da Silva, PhD
- Phone Number: 01483477199
- Email: people@thewhiteleyclinic.co.uk
Study Contact Backup
- Name: Serah Duro, MSc
- Email: serah.duro@thewhiteleyclinic.co.uk
Study Locations
-
-
-
Guildford, United Kingdom, GU2 7RF
- The Whiteley Clinic
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Patients are attending for investigation of suspected venous disease. Patients must be able to walk and mobile normally and have good skin integrity of the lower leg, where the PPG is attached.
All patients attending TWC are 18 years or older.
Exclusion Criteria:
- Subjects with known arterial occlusive disease or physical disability affecting gait or ankle movement will be excluded.
Patients unable to have a PPG attached to the lower leg (ie: active ulceration) will be excluded.
Patients unable to give consent. Pregnant female.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Individuals with CVD (Treatment Group)
Participants who have been diagnosed with Chronic Venous Disease (CVD).
|
The study investigates venous competence through three distinct exercises using photoplethysmography (PPG) technology to record blood flow in the leg veins of 20 subjects, split into two groups: those with chronic venous disease (CVD) and those without.
The null hypothesis is that there will be no significant difference in venous filling times (VFT) and PPG trace variations between subjects with CVD and those without under different physical conditions.
The alternative hypothesis suggests that individuals with CVD will show distinct PPG patterns, particularly shorter VFT and varied pressure changes, indicative of venous reflux or obstruction.
This hypothesis is chosen based on prior evidence suggesting observable differences in venous function between affected and non-affected individuals.
|
|
Individuals Without CVD (Control Group)
Participants who have not been diagnosed with CVD.
|
The study investigates venous competence through three distinct exercises using photoplethysmography (PPG) technology to record blood flow in the leg veins of 20 subjects, split into two groups: those with chronic venous disease (CVD) and those without.
The null hypothesis is that there will be no significant difference in venous filling times (VFT) and PPG trace variations between subjects with CVD and those without under different physical conditions.
The alternative hypothesis suggests that individuals with CVD will show distinct PPG patterns, particularly shorter VFT and varied pressure changes, indicative of venous reflux or obstruction.
This hypothesis is chosen based on prior evidence suggesting observable differences in venous function between affected and non-affected individuals.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Diagnostic Accuracy of an AI Model for Venous Disease Detection Using PPG Signals
Time Frame: June 2024 - September 2024
|
The primary outcome measure of this study is to evaluate the diagnostic accuracy of an AI model in detecting venous disease through the using PPG signals.
This will be quantified by assessing the sensitivity and specificity of the AI model when analysing PPG signals from healthy participants without venous diease, and non-healthy participants with venous disease, without the need for direct intervention of a vascular consultant.
These results will help evaluate the AI model in terms of how accurately it can identify Venous disease.
|
June 2024 - September 2024
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Mark Whiteley, The Whiteley Clinic
Study record dates
Study Major Dates
Study Start (Estimated)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
Other Study ID Numbers
- TWC-SD-2024-05
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.