Construction and verification of an intelligent measurement model for diabetic foot ulcer.
10.11817/j.issn.1672-7347.2021.200938
- Author:
Nan ZHAO
1
;
Qiuhong ZHOU
2
;
Jianzhong HU
3
;
Weihong HUANG
3
;
Jingcan XU
4
;
Min QI
5
;
Min PENG
6
;
Wenjing LUO
4
;
Xinyi LI
4
;
Jiaojiao BAI
7
;
Liaofang WU
4
;
Ling YU
4
;
Xiaoai FU
4
Author Information
1. Teaching and Research Section of Clinical Nursing, Xiangya Hospital, Central South University, Changsha 410008. nancy1223@foxmail.com.
2. Teaching and Research Section of Clinical Nursing, Xiangya Hospital, Central South University, Changsha 410008. 928555448@qq.com.
3. Mobile Health" Ministry of Education-China Mobile Joint Laboratory, Chengdu 610041.
4. Teaching and Research Section of Clinical Nursing, Xiangya Hospital, Central South University, Changsha 410008.
5. Department of Plastic and Cosmetic Surgery, Xiangya Hospital, Central South University, Changsha 410008.
6. Third Comprehensive Division, Guangdong Provincial People's Hospital, Guangdong Medical Institute, Guangdong Research Institute of Geriatric Medicine, Guangzhou 510080.
7. Department of Nursing, Huadong Hospital Affiliated to Fudan University, Shanghai 200040, China.
- Publication Type:Journal Article
- Keywords:
chronic wound;
deep learning;
diabetic foot ulcers;
intelligent measurement
- MeSH:
Artificial Intelligence;
Diabetes Mellitus;
Diabetic Foot;
Humans
- From:
Journal of Central South University(Medical Sciences)
2021;46(10):1138-1146
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:The measurement of diabetic foot ulcers is important for the success in diabetic foot ulcer management. At present, it lacks the accurate and convenient measurement tools in clinical. In recent years, artificial intelligence technology has demonstrated the potential application value in the field of image segmentation and recognition. This study aims to construct an intelligent measurement model of diabetic foot ulcers based on the deep learning method, and to conduct preliminary verification.
METHODS:The data of 1 042 diabetic foot ulcers clinical samples were collected. The ulcers and color areas were manually labeled, of which 782 were used as the training data set and 260 as the test data set. The Mask RCNN ulcer tissue color semantic segmentation and RetinaNet scale digital scale target detection were used to build a model. The training data set was input into the model and iterated. The test data set was used to verify the intelligent measurement model.
RESULTS:This study established an intelligent measurement model of diabetic foot ulcers based on deep learning. The mean average precision@.5 intersection over union (mAP@.5IOU) of the color region segmentation in the training set and the test set were 87.9% and 63.9%, respectively; the mAP@.5IOU of the ruler scale digital detection in the training set and the test set were 96.5% and 83.4%, respectively. Compared with the manual measurement result of the test sample, the average error of the intelligent measurement result was about 3 mm.
CONCLUSIONS:The intelligent measurement model has good accuracy and robustness in measuring the diabetic foot ulcers. Future research can further optimize the model with larger-scale data samples.
