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Application of deep learning in wound size measurement using fingernail as the reference
Dun-Hao Chang1,2,3, Duc-Khanh Nguyen1,4, Thi-Ngoc Nguyen1
1Department of Information Management, Yuan Ze University, Taoyuan, Taiwan.
BMC Medical Informatics and Decision Making
|December 19, 2024
Summary
This study introduces an automated wound measurement system using deep learning and fingernails as references, offering a convenient and accessible solution for chronic wound care. The system achieved high accuracy and user satisfaction, simplifying homecare and clinical documentation.
Area of Science:
- Medical Technology
- Artificial Intelligence in Healthcare
- Wound Care Management
Background:
- Current wound measurement tools often require manual tracing and reference markers, posing challenges for patients and caregivers.
- Chronic wound management necessitates accurate and accessible size measurement for effective treatment and monitoring.
Purpose of the Study:
- To develop and evaluate an automated wound size measurement system using deep learning (DL) models and fingernails as a natural reference.
- To create a human-centered design that simplifies wound measurement for homecare settings and inexperienced users.
Main Methods:
- Combined three DL models (Mask R-CNN, Yolov5, U-net) trained on chronic wound and fingernail images.
- Utilized Mask R-CNN and Yolov5 for wound cropping and U-net for area calculation, with fingernails for scale.
- Evaluated system accuracy with 248 images and user experience with 30 participants.
Main Results:
- Achieved high accuracy in nail-width measurement (0.939 PCC) and wound detection (97.76%).
- Demonstrated strong correlation for converting nail width to wound measurements (0.875 PCC).
- Reported 90% user satisfaction regarding convenience and overall evaluation, highlighting ease of use for caregivers.
Conclusions:
- The developed system provides fast, precise, and user-friendly wound size measurement.
- Offers convenience and accessibility for homecare, inexperienced caregivers, and telemedicine applications.
- Facilitates clinical treatment, documentation, and remote patient monitoring in wound care.
