Technology of Automatic Determination of Indications for 2RT-Laser Treatment of AMD from SD-OCT Images Based on Artificial Intelligence Methods

被引:0
作者
Ionov, A. Yu. [1 ]
Ilyasova, N. Yu. [1 ,2 ]
Demin, N. S. [1 ,2 ]
Zamytskiy, E. A. [3 ]
Zubkova, E. Yu. [3 ]
机构
[1] Samara Natl Res Univ, Samara 443086, Russia
[2] NRC Kurchatov Inst, Image Proc Syst Inst, Samara 443001, Russia
[3] Eroshevsky Samara Reg Ophtalm Hosp, Samara 443068, Russia
关键词
neural networks; convolutional networks; segmentation; age-related macular degeneration; OPTICAL COHERENCE TOMOGRAPHY; MACULAR DEGENERATION; SEGMENTATION; RETINOPATHY; DIAGNOSIS;
D O I
10.3103/S1060992X24700565
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The aim of this work is to develop and study the technology of automatic determination of indications for 2RT-laser treatment of AMD by SD-OCT images based on artificial intelligence methods. This is necessary to improve the accuracy and efficiency of AMD diagnosis, as well as to provide faster and more accurate treatment assignment to each patient. The U-Net architecture was chosen as the neural network architecture to extract the area of interest in the retinal OCT image. The VGG16 architecture was used as the neural network architecture for classification. These architectures are well established. As a result of training, the model showed a fairly high accuracy of 90% for segmentation and 98% for classification. Automatic localization and classification based on SD-OST images will allow the most accurate determination of indications for 2RT laser treatment. This will significantly reduce the burden on physicians and make diagnostics more accessible.
引用
收藏
页码:S277 / S284
页数:8
相关论文
共 31 条
[1]  
Alwiyah A., 2023, Int. Trans. Artif. Intell. (ITALIC), V2, P90, DOI [10.33050/italic.v2i1.438, DOI 10.33050/ITALIC.V2I1.438]
[2]  
Avetisov S.E., 2014, Ophthalmology
[3]  
Budzinskaia M V, 2014, Vestn Oftalmol, V130, P56
[4]   AUTOMATED DIAGNOSIS OF RETINOPATHY BY CONTENT-BASED IMAGE RETRIEVAL [J].
Chaum, Edward ;
Karnowski, Thomas P. ;
Govindasamy, V. Priya ;
Abdelrahman, Mohamed ;
Tobin, Kenneth W. .
RETINA-THE JOURNAL OF RETINAL AND VITREOUS DISEASES, 2008, 28 (10) :1463-1477
[5]   Lutein plus Zeaxanthin and Omega-3 Fatty Acids for Age-Related Macular Degeneration The Age-Related Eye Disease Study 2 (AREDS2) Randomized Clinical Trial [J].
Chew, Emily Y. ;
Clemons, Traci E. ;
SanGiovanni, John Paul ;
Danis, Ronald ;
Ferris, Frederick L., III ;
Elman, Michael ;
Antoszyk, Andrew ;
Ruby, Alan ;
Orth, David ;
Bressler, Susan ;
Fish, Gary ;
Hubbard, Baker ;
Klein, Michael ;
Chandra, Suresh ;
Blodi, Barbara ;
Domalpally, Amitha ;
Friberg, Thomas ;
Wong, Wai ;
Rosenfeld, Philip ;
Agron, Elvira ;
Toth, Cynthia ;
Bernstein, Paul ;
Sperduto, Robert .
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2013, 309 (19) :2005-2015
[6]   Application of artificial intelligence in ophthalmology for solving the problem of semantic segmentation of fundus images [J].
Demin, N. S. ;
Ilyasova, N. Y. ;
Paringer, R. A. ;
Kirsh, D. V. .
COMPUTER OPTICS, 2023, 47 (05) :824-831
[7]  
Demin N.S., 2019, Proceedings of, VITNT-2019, P21
[8]   Age-related macular degeneration: Epidemiology, genetics, pathophysiology, diagnosis, and targeted therapy [J].
Deng, Yanhui ;
Qiao, Lifeng ;
Du, Mingyan ;
Qu, Chao ;
Wan, Ling ;
Li, Jie ;
Huang, Lulin .
GENES & DISEASES, 2022, 9 (01) :62-79
[9]  
Gabor H., 2022, The Tversky loss function and its modifications for medical image segmentation
[10]   Subthreshold Nanosecond Laser Intervention in Age-Related Macular Degeneration The LEAD Randomized Controlled Clinical Trial [J].
Guymer, Robyn H. ;
Wu, Zhichao ;
Hodgson, Lauren A. B. ;
Caruso, Emily ;
Brassington, Kate H. ;
Tindill, Nicole ;
Aung, Khin Zaw ;
McGuinness, Myra B. ;
Fletcher, Erica L. ;
Chen, Fred K. ;
Chakravarthy, Usha ;
Arnold, Jennifer J. ;
Heriot, Wilson J. ;
Durkin, Shane R. ;
Lek, Jia Jia ;
Harper, Colin A. ;
Wickremasinghe, Sanjeewa S. ;
Sandhu, Sukhpal S. ;
Baglin, Elizabeth K. ;
Sharangan, Pyrawy ;
Braat, Sabine ;
Luu, Chi D. .
OPHTHALMOLOGY, 2019, 126 (06) :829-838