Application of deep learning in wound size measurement using fingernail as the reference.

OBJECTIVE

Most current wound size measurement devices or applications require manual wound tracing and reference markers. Chronic wound care usually relies on patients or caregivers who might have difficulties using these devices. Considering a more human-centered design, we propose an automatic wound size measurement system by combining three deep learning (DL) models and using fingernails as a reference.

MATERIALS AND METHODS

DL models (Mask R-CNN, Yolov5, U-net) were trained and tested using photographs of chronic wounds and fingernails. Nail width was obtained through using Mask R-CNN, Yolov5 to crop the wound from the background, and U-net to calculate the wound area. The system's effectiveness and accuracy were evaluated with 248 images, and users' experience analysis was conducted with 30 participants.

RESULTS

Individual model training achieved a 0.939 Pearson correlation coefficient (PCC) for nail-width measurement. Yolov5 had the highest mean average precision (0.822) with an Intersection-over-Union threshold of 0.5. U-net achieved a mean pixel accuracy of 0.9523. The proposed system recognized 100% of fingernails and 97.76% of wounds in the test datasets. PCCs for converting nail width to measured and default widths were 0.875 and 0.759, respectively. Most inexperienced caregivers consider convenience is the most important factor when using a size-measuring tool. Our proposed system yielded 90% satisfaction in the convenience aspect as well as the overall evaluation.

CONCLUSION

The proposed system performs fast and easy-to-use wound size measurement with acceptable precision. Its novelty not only allows for conveniences and easy accessibility in homecare settings and for inexperienced caregivers; but also facilitates clinical treatments and documentation, and supports telemedicine.