[1]《人工智能在视网膜液监测中的应用指南(0)》专家组,国际转化医学会眼科专业委员会,中国医药教育协会眼科影像与智能医疗分会,等.人工智能在视网膜液监测中的应用指南(2024)[J].眼科新进展,2024,44(7):505-511.[doi:10.13389/j.cnki.rao.2024.0097]
 Expert Workgroup of Application Guide of Artificial Intelligence for Retinal Fluid Monitoring (0),Ophthalmology Committee of International Association of Translational Medicine,Ophthalmic Imaging and Intelligent Medicine Branch of Chinese Medicine Education Association,et al.Application guide of artificial intelligence for retinal fluid monitoring (2024)[J].Recent Advances in Ophthalmology,2024,44(7):505-511.[doi:10.13389/j.cnki.rao.2024.0097]
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人工智能在视网膜液监测中的应用指南(2024)/HTML
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《眼科新进展》[ISSN:1003-5141/CN:41-1105/R]

卷:
44卷
期数:
2024年7期
页码:
505-511
栏目:
述评
出版日期:
2024-07-01

文章信息/Info

Title:
Application guide of artificial intelligence for retinal fluid monitoring (2024)
作者:
《人工智能在视网膜液监测中的应用指南(2024)》专家组国际转化医学会眼科专业委员会中国医药教育协会眼科影像与智能医疗分会中国眼科影像研究专家组
Author(s):
Expert Workgroup of Application Guide of Artificial Intelligence for Retinal Fluid Monitoring (2024); Ophthalmology Committee of International Association of Translational Medicine; Ophthalmic Imaging and Intelligent Medicine Branch of Chinese Medicine Education Association; Chinese Ophthalmic Imaging Study Group
关键词:
人工智能老年性黄斑变性光学相干断层扫描卷积神经网络线性混合模型
Keywords:
artificial intelligence senile macular degeneration optical coherence tomography convolutional neural network linear mixture model
分类号:
R774
DOI:
10.13389/j.cnki.rao.2024.0097
文献标志码:
A
摘要:
老年性黄斑变性(SMD)是一种复杂的、高度遗传的、多因素作用的疾病,患者黄斑区结构会发生衰老性改变,表现为视网膜进行性变性和视力逐渐丧失。全世界约有2亿人受到SMD的影响,并且随着人口老龄化的加剧,发病率不断上升。近年来人工智能(AI)技术发展迅猛,AI技术在医学领域的应用为医疗行业的发展带来新的可能。利用AI对视网膜液进行定性定量评估,不仅可以在新生血管性SMD的诊断过程中发挥重要作用,还可以在治疗过程中根据治疗效果及时调整治疗方案,为患者提供更加个性化的治疗。本指南总结了AI在SMD治疗中的应用,包括AI在视网膜液监测技术中的应用进展、临床应用及未来发展,为眼科医生评估患者病情、设计治疗方案及判断预后提供足够的帮助。
Abstract:
Senile macular degeneration (SMD) is a complex, highly heritable, and multifactorial disease that leads to the aging-related change in the macular region, characterized by progressive retinal degeneration and progressive loss of vision. About 200 million people worldwide suffer from SMD, and the incidence is increasing as the population ages. Artificial intelligence (AI) technology has developed rapidly in recent years, and its application in the medical field has brought new possibilities for the development of the medical industry. AI-based qualitative and quantitative evaluation of retinal fluid can not only facilitate the diagnosis of neovascular SMD but also help adjust the treatment plan timely according to the effect, so as to provide more targeted treatment for patients. This guide summarizes the application of AI in the treatment of SMD, including the application progress, clinical application and future development of AI in retinal fluid monitoring, to provide sufficient support for ophthalmologists to evaluate patient’s conditions, design treatment plans and estimate prognosis.

参考文献/References:

[1] HANSON R L W,AIRODY A,SIVAPRASAD S,GALE R P.Optical coherence tomography imaging biomarkers associated with neovascular age-related macular degeneration:a systematic review[J].Eye (Lond),2023,37(12):2438-2453.
[2] NAWASH B,ONG J,DRIBAN M,HWANG J,CHEN J,SELVAM A,et al.Prognostic optical coherence tomography biomarkers in neovascular age-related macular degeneration[J].J Clin Med,2023,12(9):3049.
[3] 邵毅,王珊珊,袁晴.糖尿病黄斑水肿诊治规范:2018欧洲视网膜专家协会指南解读[J].国际眼科杂志,2020,20(1):1-3.
SHAO Y,WANG S S,YUAN Q.European Society of Retina Specialists guidelines for management of diabetic macular edema[J].Int Eye Sci,2020,20(1):1-3.
[4] SCHMIDT-ERFURTH U,REITER G S,RIEDL S,SEEBCK P,VOGL W D,BLODI B A,et al.AI-based monitoring of retinal fluid in disease activity and under therapy[J].Prog Retin Eye Res,2022,86:100972.
[5] 蒋炎,许斐平,汪竟成,王莎莎,刘瑞,曹婷怡,等.OCT影像人工智能读片与医生读片对识别年龄相关性黄斑变性的一致性分析[J].国际眼科杂志,2022,22(5):741-745.
JIANG Y,XU F P,WANG J C,WANG S S,LIU R,CAO T Y,et al.Consistency analysis of OCT image by artificial intelligence recognition and ophthalmologist’s recognition for age-related macular degeneration[J].Int Eye Sci,2022,22(5):741-745.
[6] LIN M,BAO G,SANG X,WU Y.Recent advanced deep learning architectures for retinal fluid segmentation on optical coherence tomography images[J].Sensors (Basel),2022,22(8):3055.
[7] FUNG A E,LALWANI G A,ROSENFELD P J,DUBOVY S R,MICHELS S,FEUER W J,et al.An optical coherence tomography-guided,variable dosing regimen with intravitreal ranibizumab (Lucentis) for neovascular age-related macular degeneration[J].Am J Ophthalmol,2007,143(4):566-583.
[8] KOSTOLNA K,REITER G S,FRANK S,COULIBALY L M,FUCHS P,RGGLA V,et al.A systematic prospective comparison of fluid volume evaluation across OCT devices used in clinical practice[J].Ophthalmol Sci,2024,4(3):100456.
[9] WEI X,SUI R.A review of machine learning algorithms for retinal cyst segmentation on optical coherence tomography[J].Sensors (Basel),2023,23(6):3144.
[10] ALSAIH K,YUSOFF M Z,TANG T B,FAYE I,MRIAUDEAU F.Deep learning architectures analysis for age-related macular degeneration segmentation on optical coherence tomography scans[J].Comput Methods Programs Biomed,2020,195:105566.
[11] SHELHAMER E,LONG J,DARRELL T.Fully convolutional networks for semantic segmentation[J].IEEE Trans Pattern Anal Mach Intell,2017,39(4):640-651.
[12] ZUNAIR H,BEN HAMZA A.Sharp U-Net:depthwise convolutional network for biomedical image segmentation[J].Comput Biol Med,2021,136:104699.
[13] SCHAD D J,NICENBOIM B,VASISHTH S.Data aggregation can lead to biased inferences in Bayesian linear mixed models and Bayesian analysis of variance[J].Psychol Methods,2024,2024:38271007.
[14] ROBERTS P K,VOGL W D,GERENDAS B S,GLASSMAN A R,BOGUNOVIC H,JAMPOL L M,et al.Quantification of fluid resolution and visual acuity gain in patients with diabetic macular edema using deep learning:a post hoc analysis of a randomized clinical trial[J].JAMA Ophthalmol,2020,138(9):945-953.
[15] LUFT N,MOHR N,SPIEGEL E,MARCHI H,SIEDLECKI J,HARRANT L,et al.Optimizing refractive outcomes of SMILE:artificial intelligence versus conventional state-of-the-art nomograms[J].Curr Eye Res,2024,49(3):252-259.
[16] CHANG H H,ZHUANG A H,VALENTINO D J,CHU W C.Performance measure characterization for evaluating neuroimage segmentation algorithms[J].Neuroimage,2009,47(1):122-135.
[17] LANZETTA P,MITCHELL P,WOLF S,VERITTI D.Different antivascular endothelial growth factor treatments and regimens and their outcomes in neovascular age-related macular degeneration:a literature review[J].Br J Ophthalmol,2013,97(12):1497-1507.
[18] DUGEL P U,KOH A,OGURA Y,JAFFE G J,SCHMIDT-ERFURTH U,BROWN D M,et al.HAWK and HARRIER:phase 3,multicenter,randomized,double-masked trials of brolucizumab for neovascular age-related macular degeneration[J].Ophthalmology,2020,127(1):72-84.
[19] ROMO-BUCHELI D,ERFURTH U S,BOGUNOVIC H.End-to-end deep learning model for predicting treatment requirements in neovascular AMD from longitudinal retinal OCT imaging[J].IEEE J Biomed Health Inform,2020,24(12):3456-3465.
[20] MEHTA H,TUFAIL A,DAIEN V,LEE A Y,NGUYEN V,OZTURK M,et al.Real-world outcomes in patients with neovascular age-related macular degeneration treated with intravitreal vascular endothelial growth factor inhibitors[J].Prog Retin Eye Res,2018,65:127-146.
[21] HANSON R L W,AIRODY A,SIVAPRASAD S,GALE R P.Optical coherence tomography imaging biomarkers associated with neovascular age-related macular degeneration:a systematic review[J].Eye,2023,37:2438-2453.
[22] JONG J D,CUTCUTACHE I,PAGE M,ELMOUFTI S,DILLEY C,FRHLICH H,et al.Towards realizing the vision of precision medicine:AI based prediction of clinical drug response[J].Brain,2021,144(6):1738-1750.
[23] 杨卫华,邵毅,许言午,《眼科人工智能临床研究评价指南(2023)》专家组,中国医药教育协会眼科影像与智能医疗分会,中国医药教育协会智能医学专业委员会.眼科人工智能临床研究评价指南(2023)[J].国际眼科杂志,2023,23(7):1064-1071.
YANG W H,SHAO Y,XU Y W,EXPERT WORKGROUP OF GUIDELINES ON CLINICAL RESEARCH EVALUATION OF ARTIFICIAL INTELLIGENCE IN OPHTHALMOLOGY (2023),OPHTHALMIC IMAGING AND INTELLIGENT MEDICINE BRANCH OF CHINESE MEDICINE EDUCATION ASSOCIATION,INTELLIGENT MEDICINE SPECIAL COMMITTEE OF SHINESE MEDICINE EDUCATION ASSOCIATION.Guidelines on clinical research evaluation of artificial intelligence in ophthalmology(2023)[J].Int Eye Sci,2023,23(7):1064-1071.

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备注/Memo

备注/Memo:
国家自然科学基金(编号:82160195);江西省双千计划科技创新高端领军人才项目(编号:jxsq2023201036);江西省重大(重点)研发专项计划(编号:20223BBH80014)
注:本指南的国际实践指南注册号为PREPARE-2023CN686(http://www.guidelines-redistry.cn/)。
更新日期/Last Update: 2024-07-05