[1]董帮娟,戴卓,王金金,等.基于深度学习的烧伤患者整形术后愈合状态预测[J].中国医学物理学杂志,2026,43(2):255-260.[doi:DOI:10.3969/j.issn.1005-202X.2026.02.016]
　deep learning attention mechanism burn plastic surgery healing status.Deep learning based prediction of burn wound healing after plastic surgery[J].Chinese Journal of Medical Physics,2026,43(2):255-260.[doi:DOI:10.3969/j.issn.1005-202X.2026.02.016]
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基于深度学习的烧伤患者整形术后愈合状态预测()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
43卷
期数:
2026年第2期
页码:
255-260
栏目:
医学人工智能
出版日期:
2026-02-27

文章信息/Info

Title:
Deep learning based prediction of burn wound healing after plastic surgery
文章编号:
1005-202X（2026）02-0255-06
作者:
董帮娟; 戴卓; 王金金; 王华军; 于攀
东部战区总医院烧伤整形科， 江苏 南京 210016
Author(s):
deep learning attention mechanism burn plastic surgery healing status
Department of Burn and Plastic Surgery, General Hospital of Eastern Theater Command, Nanjing 210016, China
关键词:
深度学习; 注意力机制; 烧伤; 整形手术; 愈合状态
Keywords:
Keywords: deep learning attention mechanism burn plastic surgery healing status
分类号:
R318；R619.5
DOI:
DOI:10.3969/j.issn.1005-202X.2026.02.016
文献标志码:
A
摘要:
目的：旨在应用深度学习模型预测烧伤患者接受整形手术后的愈合状态，重点包括瘢痕形成、感染发生以及完全愈合与部分愈合的类别判断。通过对烧伤图像数据进行特征提取和分析，协助医生更准确地评估患者的术后恢复情况。方法：基于预训练的VGG模型进行烧伤图像特征提取，同时引入注意力机制以增强模型对关键区域的关注能力。所提取的特征进一步输入到多种传统机器学习分类器中，分别完成模型的训练与测试。结果：基于VGG模型并结合注意力机制的分类模型在预测烧伤愈合状态方面取得良好效果，其中VGG模型结合注意力机制与XGBoost分类器的组合模式最优，准确率达到0.853，宏平均F1分数为0.844，宏平均AUC值为0.921，说明该模型在不同愈合类别的区分中具有优越性能。结论：采用VGG特征提取并引入注意力机制的模型在烧伤愈合状态预测中显示出显著的临床应用潜力。注意力机制的加入提升了模型对关键图像特征的关注程度，从而进一步提高分类的准确率和结果的一致性。
Abstract:
Objective To predict the healing status in burn patients after plastic surgery using deep learning models, focusing on scar formation, infection occurrence, and classifications of complete and partial healing, and assist clinicians in more accurately assessing postoperative recovery by extracting and analyzing features from burn image data. Methods The pre-trained visual geometry group (VGG) model was used to extract features from burn wound images, and an attention mechanism was introduced to enhance the models ability to focus on key regions. The extracted features were further input into various traditional machine learning classifiers for model training and testing. Results The classification model combining the VGG model with an attention mechanism achieved favorable performance in predicting burn wound healing status. Among all the models, the VGG model incorporating the attention mechanism and XGBoost classifier exhibited the optimal performance, with an accuracy of 0.853, an average macro F1 score of 0.844, and an average macro AUC value of 0.921, verifying its superior performance in distinguishing different healing statuses. Conclusion The model employing VGG feature extraction and incorporating an attention mechanism demonstrates significant clinical potential in predicting burn wound healing. The application of the attention mechanism enhances the models focus on key image features, which further improves classification accuracy and result consistency.

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备注/Memo

备注/Memo:
【收稿日期】2025-09-12 【基金项目】江苏省卫健委支撑项目（K2023064） 【作者简介】董帮娟，研究方向：烧伤整形，E-mail: dbdb_bj@163.com 【通信作者】于攀，博士，副主任医师，硕士生导师，研究方向：创面修复、烧伤治疗、瘢痕整复等，E-mail: yp52@163.com
更新日期/Last Update: 2026-01-27