文献类型：期刊文献

英文题名：Exploration of machine learning models for surgical incision healing assessment based on thermal imaging: A feasibility study

作者：Li, Fanfan[1,2];Zhang, Hongyu[3];Xu, Shangqing[4];Ma, Xiaoli[1,2,5,6];Luo, Na[1,2];Yu, Youzhen[1,2];He, Wenhui[1,2];Jin, Hongying[1,2];Wang, Min[1,2];Wang, Ting[1,2];Wang, Xiaolan[1,2];Zhang, Yimei[1,2];Ma, Guojing[1,2];Zhao, Dan[1,2];Yue, Qin[1,2];Wang, Panpan[1,2];Ma, Minjie[1,2,5,6]

第一作者：Li, Fanfan

通信作者：Ma, XL[1];Ma, MJ[1]

机构：[1]Lanzhou Univ, Hosp 1, Dept Thorac Surg, 1 Donggang West Rd, Lanzhou 730000, Gansu, Peoples R China;[2]Gansu Univ Chinese Med, Sch Nursing, Lanzhou, Peoples R China;[3]Lanzhou Univ, Coll Informat Sci & Engn, Lanzhou, Peoples R China;[4]Lanzhou Univ, Clin Med Coll 1, Skills Training Ctr, Lanzhou, Peoples R China;[5]Int Sci & Technol Cooperat Base Dev & Applicat Key, Lanzhou, Peoples R China;[6]Control Ctr Thorac Surg Gansu Prov Lanzhou, Lanzhou, Gansu, Peoples R China

第一机构：Lanzhou Univ, Hosp 1, Dept Thorac Surg, 1 Donggang West Rd, Lanzhou 730000, Gansu, Peoples R China

通信机构：[1]corresponding author), Lanzhou Univ, Hosp 1, Dept Thorac Surg, 1 Donggang West Rd, Lanzhou 730000, Gansu, Peoples R China.

年份：2024

卷号：21

期号：2

外文期刊名：INTERNATIONAL WOUND JOURNAL

收录：;Scopus(收录号：2-s2.0-85183836615);WOS:【SCI-EXPANDED(收录号:WOS:001153761800001)】；

基金：We extend our sincere gratitude to Professor Minjie Ma and Professor Xiaoli Ma for their continuous guidance and support throughout this research. Special thanks to our co-first author, Hongyu Zhang, for providing professional guidance and rigorous examination on machine learning techniques. We would also like to express our appreciation to all the authors involved in data collection and annotation. The success of this research is a result of the hard work and dedication of each participant. Finally, our project is funded by the Natural Science Foundation of Gansu Province, grant/award number: 23JRRA1597, and we greatly appreciate the support for our project.

语种：英文

外文关键词：convolutional neural networks; machine learning; surgical incisions; wound assessment; wounds; YOLOV8

摘要：In this study, we explored the use of thermal imaging technology combined with computer vision techniques for assessing surgical incision healing. We processed 1189 thermal images, annotated by experts to define incision boundaries and healing statuses. Using these images, we developed a machine learning model based on YOLOV8, which automates the recognition of incision areas, lesion segmentation and healing classification. The dataset was divided into training, testing and validation sets in a 7:2:1 ratio. Our results show high accuracy rates in incision location recognition, lesion segmentation and healing classification, indicating the model's effectiveness as a precise and automated diagnostic tool for surgical incision healing assessment. Conclusively, our thermal image-based machine learning model demonstrates excellent performance in wound assessment, paving the way for its clinical application in intelligent and standardized wound management.