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中华损伤与修复杂志(电子版) ›› 2025, Vol. 20 ›› Issue (05) : 426 -430. doi: 10.3877/cma.j.issn.1673-9450.2025.05.010

综述

浅析人工智能在海战烧伤诊疗中的应用前景
张家乐1,2, 田璐1,2, 伍国胜1, 刘莹莹1, 李志1, 吴琼1, 纪世召1,()   
  1. 1 200433 上海,海军军医大学第一附属医院烧伤外科
    2 200433 上海,海军军医大学基础医学院
  • 收稿日期:2025-06-03 出版日期:2025-10-01
  • 通信作者: 纪世召
  • 基金资助:
    上海市重中之重研究中心建设项目(2023ZZ02013)

Application of artificial intelligence in the diagnosis and treatment of burns in naval warfare

Jiale Zhang1,2, Lu Tian1,2, Guosheng Wu1, Yingying Liu1, Zhi Li1, Qiong Wu1, Shizhao Ji1,()   

  1. 1 Department of Burn Surgery,the First Affiliated Hospital of Naval Medical University,Shanghai 200433,China
    2 School of Basic Medical Sciences,Naval Medical University,Shanghai 200433,China
  • Received:2025-06-03 Published:2025-10-01
  • Corresponding author: Shizhao Ji
引用本文:

张家乐, 田璐, 伍国胜, 刘莹莹, 李志, 吴琼, 纪世召. 浅析人工智能在海战烧伤诊疗中的应用前景[J/OL]. 中华损伤与修复杂志(电子版), 2025, 20(05): 426-430.

Jiale Zhang, Lu Tian, Guosheng Wu, Yingying Liu, Zhi Li, Qiong Wu, Shizhao Ji. Application of artificial intelligence in the diagnosis and treatment of burns in naval warfare[J/OL]. Chinese Journal of Injury Repair and Wound Healing(Electronic Edition), 2025, 20(05): 426-430.

在远离陆基医疗体系的海战场环境中,专业医疗人员短缺和救治条件有限,导致烧伤患者的早期诊断及应急处置面临严峻挑战。随着人工智能(AI)技术的迅猛发展,其在战伤救治领域展现出突破性应用价值。本文系统梳理了近年来AI在烧伤面积评估、深度诊断、并发症预测及个性化治疗中的突破性进展,并聚焦未来海战特殊场景,探讨AI技术在复杂战场环境下的应用前景与技术挑战。

The scarcity of professional medical personnel and the limitations of treatment facilities present significant challenges for the early diagnosis and emergency treatment of burn patients at the battlefield of naval warfare. With the rapid development of artificial intelligence technology (AI),it has demonstrated remarkable application potential in combat injury treatment. This article systematically reviews the recent breakthroughs of AI in burn area assessment,depth diagnosis,complication prediction,and personalized treatment. Meanwhile,the application prospects and technical challenges of AI technology in complex battlefield environments were explored in this review.

[1]
宗兆文,李楠. 马岛战争和二战中海战伤发生特点及其对我军海战伤救治的启示[J]. 第三军医大学学报201739(24):2341-2344.
[2]
Lillicrap DMorrissey JH. Artificial intelligence,science,and learning[J]. J Thromb Haemost202321(4):709.
[3]
S GB L. Deep convolutional generative adversarial network for improved cardiac image classification in heart disease diagnosis[J]. J Imaging Inform Med202438(4):2146-2169.
[4]
Li SZhang LCai Y,et al. Deep learning assists detection of esophageal cancer and precursor lesions in a prospective,randomized controlled study[J]. Sci Transl Med202416(743):eadk5395.
[5]
Giretzlehner MGanitzer IHaller H. Technical and medical aspects of burn size assessment and documentation[J]. Medicina (Kaunas)202157(3):242.
[6]
Chong HPQuinn LJeeves A,et al. A comparison study of methods for estimation of a burn surface area: Lund and Browder,e-burn and Mersey Burns[J]. Burns202046(2):483-489.
[7]
Godwin ZTan JBockhold J,et al. Development and evaluation of a novel smart device-based application for burn assessment and management[J]. Burns201541(4):754-760.
[8]
Xu XBu QXie J,et al. On-site burn severity assessment using smartphone-captured color burn wound images[J]. Comput Biol Med2024182:109171.
[9]
Chang CWWang HLai F,et al. Comparison of 3D and 2D area measurement of acute burn wounds with LiDAR technique and deep learning model[J]. Front Artif Intell20258:1510905.
[10]
Malkoff NCannata BWang S,et al. FireSync EMS: a novel mobile application for burn surface area calculation[J]. J Burn Care Res202546(1):101-106.
[11]
Choi JPatil AVendrow E,et al. Practical computer vision application to compute total body surface area burn: reappraising a fundamental burn injury formula in the Modern Era[J]. JAMA Surg2022157(2):129-135.
[12]
Brekke RLAlmeland SKHufthammer KO,et al. Agreement of clinical assessment of burn size and burn depth between referring hospitals and burn centres: a systematic review[J]. Burns202349(3):493-515.
[13]
Li HBu QShi X,et al. Non-invasive medical imaging technology for the diagnosis of burn depth[J]. Int Wound J202421(1):e14681.
[14]
Zuo KJMedina ATredget EE. Important developments in burn care[J]. Plast Reconstr Surg2017139(1):120e-138e.
[15]
Nunez JMironov SWan B,et al. Novel multi-spectral short-wave infrared imaging for assessment of human burn wound depth[J]. Wound Repair Regen202432(6):979-991.
[16]
Thatcher JEYi FNussbaum AE,et al. Clinical investigation of a rapid non-invasive multispectral imaging device utilizing an artificial intelligence algorithm for improved burn assessment[J]. J Burn Care Res202344(4):969-981.
[17]
Lee SRahulYe H,et al. Real-time burn classification using ultrasound imaging[J]. Sci Rep202010(1):5829.
[18]
Lee SRahulLukan J,et al. A deep learning model for burn depth classification using ultrasound imaging[J]. J Mech Behav Biomed Mater2022125:104930.
[19]
Jacobson MJMasry MEArrubla DC,et al. Autonomous multi-modality burn wound characterization using artificial intelligence[J]. Mil Med2023188(Suppl 6):674-681.
[20]
Lu JDeegan AJCheng Y,et al. OCT-based angiography and surface topography in burn-damaged skin[J]. Lasers Surg Med202153(6):849-860.
[21]
Rangaraju LPKunapuli GEvery D,et al. Classification of burn injury using Raman spectroscopy and optical coherence tomography: an ex-vivo study on porcine skin[J]. Burns201945(3):659-670.
[22]
Ganapathy PTamminedi TQin Y,et al. Dual-imaging system for burn depth diagnosis[J]. Burns201440(1):67-81.
[23]
Wu JMa QZhou X,et al. Segmentation and quantitative analysis of optical coherence tomography (OCT) images of laser burned skin based on deep learning[J]. Biomed Phys Eng Express202410(4).
[24]
Yıldız MSarpdağı YOkuyar M,et al. Segmentation and classification of skin burn images with artificial intelligence: development of a mobile application[J]. Burns202450(4):966-979.
[25]
Lee JJAbdolahnejad MMorzycki A,et al. Comparing artificial intelligence guided image assessment to current methods of burn assessment[J]. J Burn Care Res202546(1):6-13.
[26]
Elsarta AFathalla HNasser M,et al. Integrating multi-source data for skin burn classification using deep learning[J]. Comput Biol Med2025195:110556.
[27]
Chang CWHo CYLai F,et al. Application of multiple deep learning models for automatic burn wound assessment[J]. Burns202349(5):1039-1051.
[28]
Jiao CSu KXie W,et al. Burn image segmentation based on Mask Regions with Convolutional Neural Network deep learning framework: more accurate and more convenient[J]. Burns Trauma20197:6.
[29]
Chen LLiang JWang C,et al. Adversarial attacks and adversarial training for burn image segmentation based on deep learning[J]. Med Biol Eng Comput202462(9):2717-2735.
[30]
Holley ADReade MCLipman J,et al. There is no fire without smoke! pathophysiology and treatment of inhalational injury in burns: a narrative review[J]. Anaesth Intensive Care202048(2):114-122.
[31]
Dou ZZhang GA. Systematic review of the epidemiological characteristics of inhalation injury in burn patients in China[J]. Zhonghua Shao Shang Za Zhi202137(7):654-660.
[32]
Yang SHuang CYen C,et al. Machine learning approach for predicting inhalation injury in patients with burns[J]. Burns202349(7):1592-1601.
[33]
Legrand MClark ATNeyra JA,et al. Acute kidney injury in patients with burns[J]. Nat Rev Nephrol202420(3):188-200.
[34]
Rashidi HHMakley APalmieri TL,et al. Enhancing military burn- and trauma-related acute kidney injury prediction through an automated machine learning platform and point-of-care testing[J]. Arch Pathol Lab Med2021145(3):320-326.
[35]
Luo WXiong LWang J,et al. Development and performance evaluation of a clinical prediction model for sepsis risk in burn patients[J]. Medicine (Baltimore)2024103(48):e40709.
[36]
海医会烧创伤暨组织修复专委会,纪世召,胡晓燕,等. 舰船环境下烧伤早期救治专家共识(2024版)[J]. 海军医学杂志202546(1):1-5.
[37]
中华医学会烧伤外科学分会康复与护理学组,上海护理学会重症监护专委会,冯苹,等. 吸入性损伤人工气道护理的专家共识[J]. 海军医学杂志202344(1):1-6.
[38]
Li HZhen NLin S,et al. Deployable machine learning-based decision support system for tracheostomy in acute burn patients[J]. Burns Trauma202513:tkaf010.
[39]
张东海,柴家科. 烧伤补液Parkland公式的研究与应用现状[J]. 中华烧伤杂志201531(3):235-237.
[40]
Romanowski KSPalmieri TL. Pediatric burn resuscitation: past,present,and future[J]. Burns Trauma20175:26.
[41]
Yamamura SKawada KTakehira R,et al. Prediction of aminoglycoside response against methicillin-resistant Staphylococcus aureus infection in burn patients by artificial neural network modeling[J]. Biomed Pharmacother200862(1):53-58.
[42]
Rambhatla SHuang STrinh L,et al. DL4Burn: burn surgical candidacy prediction using multimodal deep learning[J]. AMIA Annu Symp Proc20212021:1039-1048.
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