A skin-conformable wireless sensor to objectively quantify symptoms of pruritus

Area of Science:

• Biomedical Engineering
• Wearable Technology
• Computational Medicine

Background:

• Itch is a prevalent symptom causing significant morbidity.
• Objective measurement of itch remains a clinical challenge.
• Current methods for quantifying itch are often subjective.

Purpose of the Study:

• To introduce a novel noninvasive sensor for objective scratch quantification.
• To evaluate the performance of the sensor using machine learning.
• To validate the technology in patients with atopic dermatitis.

Main Methods:

• A soft, flexible, wireless sensor was developed to capture scratching's acousto-mechanic signatures.
• A machine learning algorithm was trained and validated on data from healthy subjects.
• Clinical validation was performed on pediatric patients with atopic dermatitis over 46 sleep-nights.

Main Results:

• The sensor system demonstrated excellent performance compared to smartwatch approaches in healthy subjects.
• In atopic dermatitis patients, the system achieved 99.0% accuracy, with 84.3% sensitivity and 99.3% specificity.
• The technology successfully quantified scratching behavior during sleep.

Conclusions:

• The developed sensor provides an objective and accurate method for measuring scratching.
• This technology has potential applications in drug efficacy assessment and disease severity monitoring.
• The noninvasive, wearable sensor offers a significant advancement in itch quantification.