Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Burn Injuries01:22

Burn Injuries

4.2K
Burn injuries occur when the skin and underlying tissues are damaged due to exposure to heat, electricity, chemicals, radiation, or friction. They can vary in severity, from minor superficial burns to severe deep burns that can be life-threatening.
The damage results in the death of skin cells, which can lead to a massive loss of fluid. Dehydration, electrolyte imbalance, and renal and circulatory failure follow, which can be fatal. Burn patients are treated with intravenous fluids to offset...
4.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Research on Covert Communication in Satellite-Ground-Integrated Sensor Networks Based on FH-DL-MPWFRFT.

Sensors (Basel, Switzerland)·2026
Same author

Case Report: Multidisciplinary collaboration for the treatment of severe necrotizing fasciitis in the perineum caused by rectal cancer perforation.

Frontiers in surgery·2026
Same author

Study on the limestone fracturing characteristics of PDC cutters in high-temperature environments.

Science progress·2026
Same author

Intracellular Vesicle Transport Impairment as a Candidate Systems-Level Bottleneck in Chronic Diabetic Foot Ulcers: Network Medicine Identifies KIF13A as a Potential Therapeutic Vulnerability.

Biomedicines·2026
Same author

Interpretative machine learning for predicting 60-day mortality in burn patients with suspected infection.

World journal of emergency medicine·2026
Same author

A 12-year hospital-based surveillance of syphilis in Hanzhong, China: shifting epidemics and implications for targeted prevention.

BMC public health·2026

相关实验视频

Updated: Jan 16, 2026

Author Spotlight: A Multi-Depth Porcine Model for Comprehensive Study of Burn Injuries and Healing Processes
02:49

Author Spotlight: A Multi-Depth Porcine Model for Comprehensive Study of Burn Injuries and Healing Processes

Published on: February 23, 2024

1.9K

一个集成的深度学习和大型语言模型,用于识别烧伤伤深度.

Haitao Ren1, Yongan Xu2, Hang Hu3

  • 1Department of Vascular Surgery, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310009, PR China.

Journal of burn care & research : official publication of the American Burn Association
|September 27, 2025
PubMed
概括

一个新的人工智能 (AI) 系统使用深度学习和大型语言模型 (LLM) 准确地分类燃烧深度. 这种人工智能工具的表现优于医学学生和一般的LLM,帮助烧伤护理专家.

关键词:
人工智能的人工智能是人工智能.烧伤伤口的分类 烧伤的分类烧伤,烧伤的伤口.大型语言模型

更多相关视频

Chessboard-like Burn Wound Healing Model of Mice Based on Digital Heating Device
04:04

Chessboard-like Burn Wound Healing Model of Mice Based on Digital Heating Device

Published on: December 27, 2024

1.5K
Author Spotlight: Studying Host-Microbe Interactions in Wound Biofilm Formation
07:16

Author Spotlight: Studying Host-Microbe Interactions in Wound Biofilm Formation

Published on: June 16, 2023

2.4K

相关实验视频

Last Updated: Jan 16, 2026

Author Spotlight: A Multi-Depth Porcine Model for Comprehensive Study of Burn Injuries and Healing Processes
02:49

Author Spotlight: A Multi-Depth Porcine Model for Comprehensive Study of Burn Injuries and Healing Processes

Published on: February 23, 2024

1.9K
Chessboard-like Burn Wound Healing Model of Mice Based on Digital Heating Device
04:04

Chessboard-like Burn Wound Healing Model of Mice Based on Digital Heating Device

Published on: December 27, 2024

1.5K
Author Spotlight: Studying Host-Microbe Interactions in Wound Biofilm Formation
07:16

Author Spotlight: Studying Host-Microbe Interactions in Wound Biofilm Formation

Published on: June 16, 2023

2.4K

科学领域:

  • 医疗技术 医疗技术 医学技术
  • 人工智能在医学中的应用
  • 皮肤病学 皮肤病学

背景情况:

  • 准确的燃烧深度评估至关重要,但具有挑战性,特别是在紧急情况下.
  • 由于缺乏专门的烧伤护理专业人员,需要创新的诊断工具.

研究的目的:

  • 开发一个基于人工智能 (AI) 的,具有成本效益的系统来分类烧伤伤口.
  • 整合用于图像分析的深度学习和用于临床指南遵守的大型语言模型 (LLM).

主要方法:

  • 将397张烧伤图像扩展到7156张,根据深度分类它们.
  • 使用PaddlePaddle训练了一个深度学习分类模型.
  • 根据临床指导方针开发了一种特定于烧伤的LLM.

主要成果:

  • 人工智能系统实现了96.82%的准确性和96.70%的F1得分.
  • 在燃烧深度分类中表现优于医学学生 (F1: 76.63%) 和普通法学士 (F1: 68.75%-73.75%) 的学生.
  • 人工智能模型在基于指南的问题上显示出100%的准确性,而学生则为64%.

结论:

  • 集成的AI模型提供了准确的燃烧深度识别.
  • 提供基于指南的治疗建议,解决专家短缺问题.
  • 支持医疗教育,改善烧伤护理的可用性.