Multimodal Diagnosis of Anemia using AI Technology V1.0
Research type
Research Study
Full title
Multimodal Diagnosis of Anemia using AI Technology
IRAS ID
339575
Contact name
Miguel Rodrigues
Contact email
Sponsor organisation
University College London
Clinicaltrials.gov Identifier
Z6364106/2024/02/125, UCL Data Protection Registration Number
Duration of Study in the UK
1 years, 0 months, 30 days
Research summary
Anemia is a widespread blood disorder affecting approximately 1.6 billion people globally. The worldwide prevalence of anemia among all age groups in 2019 was 22.8%. Traditional anemia diagnosis involves measuring hemoglobin concentration through venous blood samples, a process that is invasive and requires a clinical or outpatient setting. Moreover, this method can cause pain, localized infection, and generate medical waste.
We are currently developing a AI model that is able to ingest multiple data modalities -- ranging from EHR data to PPG sensor data to conjunctival images -- to achieve personalized anemia prediction, personalized treatment planning and personalized monitoring. This model will be non-invasive, fast and accurate.
We plan to recruit about 50 participants from UCLH. Each participant will be involved for data collection 3-4 times over two weeks, with each collection lasting about 10 minutes. All data collection is non-intrusive and does not pose any risk.At the end of the study, we will build a multimodal AI model to diagnose anemia based on the collected data. Successful development of this model will provide strong support for intelligent mobile device-based healthcare systems. It will significantly improve the efficiency of the healthcare system and patient wellbeing in England.
REC name
London - Stanmore Research Ethics Committee
REC reference
24/LO/0652
Date of REC Opinion
9 Oct 2024
REC opinion
Further Information Favourable Opinion