Developing a robust AI pipeline for clinical trials using digital devices: Integrating sample size estimation, data quality assessment, and AI for enhanced medical screening

Cardiovascular disease is a major global health challenge, and early prevention is crucial to reducing its impact. This project explores how wearable devices and artificial intelligence (AI) can improve early detection and prevention of heart disease. Wearable sensors continuously collect health data, but challenges such as missing information and inaccurate readings limit their reliability in clinical practice.

This research aims to develop a robust system that improves the quality and reliability of wearable health data, ensuring AI-driven risk assessment models provide accurate and actionable insights. By addressing challenges in data reliability and sample size estimation, the project will help healthcare professionals better trust and adopt digital health technologies. The findings will be tested using real-world data from an ongoing clinical trial.

This project will directly benefit healthcare organizations by improving the effectiveness of digital health tools for heart disease prevention. It will also help researchers and clinicians integrate AI-driven solutions into medical decision-making, leading to more personalized and timely patient care.

Faculty Supervisor:

Abhinav Sharma

Student:

Partner:

McGill University Health Centre;McGill University Health Centre Foundation

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology

University:

Research Institute of the McGill University Health Centre

Program: