A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias

Area of Science:

• Cardiology
• Biomedical Engineering
• Artificial Intelligence in Medicine

Background:

• Regular blood pressure monitoring is essential for cardiovascular disease management.
• Increased use of ambulatory BP devices raises concerns about measurement accuracy.
• Hypertension prevalence drives the need for reliable BP monitoring technologies.

Purpose of the Study:

• To survey cuff-based BP monitoring technologies and identify sources of measurement bias.
• To explore the potential of artificial intelligence (AI) in improving BP measurement accuracy.
• To propose AI-assisted methods for personalized cardiovascular risk assessment.

Main Methods:

• Review of cuff-based blood pressure monitoring technologies.
• Analysis of factors contributing to BP measurement bias (e.g., device errors, demographics).
• Exploration of AI, machine learning, and deep learning for bias correction.

Main Results:

• BP measurements exhibit significant biases and variances due to various factors.
• AI holds substantial potential for developing next-generation cuff-based BP devices.
• AI can leverage clinical literature and electronic health records to refine BP measurements.

Conclusions:

• Addressing BP measurement biases is critical for accurate cardiovascular risk assessment.
• AI-assisted technologies offer a promising future for individualized BP monitoring.
• Future research should focus on integrating AI with existing data for improved BP management.