[1]任章军,余进海,桑泽曦,等.人工智能深度学习在眼眶病及眼肿瘤疾病诊疗中的应用研究现状[J].眼科新进展,2024,44(2):163-168.[doi:10.13389/j.cnki.rao.2024.0032]
　REN Zhangjun,YU Jinhai,SANG Zexi,et al.Research advancement of the application of artificial intelligence deep learning in the diagnosis and treatment of orbital diseases and ocular tumors[J].Recent Advances in Ophthalmology,2024,44(2):163-168.[doi:10.13389/j.cnki.rao.2024.0032]
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人工智能深度学习在眼眶病及眼肿瘤疾病诊疗中的应用研究现状/HTML
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《眼科新进展》[ISSN:1003-5141/CN:41-1105/R]

卷:
44卷
期数:
2024年2期
页码:
163-168
栏目:
文献综述
出版日期:
2024-01-26

文章信息/Info

Title:
Research advancement of the application of artificial intelligence deep learning in the diagnosis and treatment of orbital diseases and ocular tumors
作者:
任章军; 余进海; 桑泽曦; 王耀华; 廖洪斐
330006　江西省南昌市，南昌大学附属眼科医院眼科
Author(s):
REN Zhangjun; YU Jinhai; SANG Zexi; WANG Yaohua; LIAO Hongfei
Department of Ophthalmology,the Affiliated Hospital of Nanchang University,Nanchang 330006,Jiangxi Province,China
关键词:
眼眶病; 眼肿瘤; 深度学习; 人工智能
Keywords:
orbital disease;  ocular tumor;  deep learning;  artificial intelligence
分类号:
R770.4
DOI:
10.13389/j.cnki.rao.2024.0032
文献标志码:
A
摘要:
近年来，深度学习作为人工智能机器学习的关键分支，在医学领域的应用取得了显著进展。它通过分析医学图像，实现了多种疾病的准确检测、诊断和预后评估。在眼科领域，深度学习技术已经广泛应用于甲状腺相关眼病、眼眶爆裂性骨折、黑色素瘤、基底细胞癌、眼眶脓肿、淋巴瘤、视网膜母细胞瘤等疾病的诊断和预测。这项技术利用计算机断层扫描、磁共振成像甚至病理切片等获得的图像，能够高度准确地进行眼眶病及眼肿瘤疾病的诊断、鉴别和分期分类，其准确度已足以与专业医师媲美。这一技术的应用前景巨大，有望提升相关疾病的诊疗水平，同时减少临床实践所需的时间和成本。本综述汇总了近年来关于人工智能深度学习在眼眶病及眼肿瘤疾病领域应用的最新研究进展，旨在为临床医师提供有关这一领域的最新信息和发展趋势，并进一步促进该技术的临床应用及普及推广。
Abstract:
In recent years, deep learning, a pivotal subset of artificial intelligence machine learning, has achieved noteworthy advancements in the medical domain. It facilitates precise detection, diagnosis and prognostic assessment of various diseases through the analysis of medical images. Within ophthalmology, deep learning techniques have found widespread application in the diagnosis and prediction of thyroid-related eye diseases, orbital blowout fracture, melanoma, basal cell carcinoma, orbital abscess, lymphoma, retinoblastoma and other diseases. Leveraging images from computed tomography, magnetic resonance imaging and even pathological sections, this technology demonstrates a capacity to diagnose, differentiate and stage orbital diseases and ocular tumors with a high level of accuracy comparable to that of expert clinicians. The promising prospects of this technology are expected to enhance the diagnosis and treatment of related diseases, concurrently reducing the time and cost associated with clinical practices. This review consolidates the latest research progress on the application of artificial intelligence deep learning in orbital diseases and ocular tumors, aiming to furnish clinicians with up-to-date information and developmental trends in this field, thereby furthering the clinical application and widespread adoption of this technology.

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备注/Memo

备注/Memo:
江西省自然科学基金项目（编号：20232ACB206030）
更新日期/Last Update: 2024-02-05