Artificial intelligence and deep learning in ophthalmology - present and future (Review)

被引：43
作者：
Moraru, Andreea Dana [1 ,2 ]
Costin, Danut [1 ,2 ]
Moraru, Radu Lucian [3 ]
Branisteanu, Daniel Constantin [1 ,4 ]
机构：
[1] Grigore T Popa Univ Med & Pharm, Dept Ophthalmol, 16 Univ St, Iasi 700115, Romania
[2] N Oblu Clin Hosp, Dept Ophthalmol, Iasi 700309, Romania
[3] Dept Otorhinolaryngol, Transmed Expert, Iasi 700011, Romania
[4] Retina Ctr, Eye Clin, Iasi 700126, Romania
来源：
EXPERIMENTAL AND THERAPEUTIC MEDICINE | 2020年 / 20卷 / 04期
关键词：
artificial intelligence; deep learning; convolutional neural networks; machine learning; ophthalmology; OCT; image processing; image analysis; RETINAL LAYER BOUNDARIES; DIABETIC-RETINOPATHY; MACULAR DEGENERATION; AUTOMATED IDENTIFICATION; IMAGING BIOMARKERS; PLUS DISEASE; SEGMENTATION; DIAGNOSIS; PREMATURITY; ALGORITHM;
D O I：
10.3892/etm.2020.9118
中图分类号：
R-3 [医学研究方法]; R3 [基础医学];
学科分类号：
1001 ;
摘要：
Since its introduction in 1959, artificial intelligence technology has evolved rapidly and helped benefit research, industries and medicine. Deep learning, as a process of artificial intelligence (AI) is used in ophthalmology for data analysis, segmentation, automated diagnosis and possible outcome predictions. The association of deep learning and optical coherence tomography (OCT) technologies has proven reliable for the detection of retinal diseases and improving the diagnostic performance of the eye's posterior segment diseases. This review explored the possibility of implementing and using AI in establishing the diagnosis of retinal disorders. The benefits and limitations of AI in the field of retinal disease medical management were investigated by analyzing the most recent literature data. Furthermore, the future trends of AI involvement in ophthalmology were analyzed, as AI will be part of the decision-making regarding the scientific investigation, diagnosis and therapeutic management.
引用
收藏
页码：3469 / 3473
页数：5
相关论文
共 55 条
[1]   Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features [J].
Abbas, Qaisar ;
Fondon, Irene ;
Sarmiento, Auxiliadora ;
Jimenez, Soledad ;
Alemany, Pedro .
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2017, 55 (11) :1959-1974
[2]  
Alexe O, 2015, INTERDISCIPLINARY APPROACHES IN DIABETIC CHRONIC KIDNEY DISEASE, P76
[3]  
Armstrong Grayson W, 2020, Int Ophthalmol Clin, V60, P57, DOI 10.1097/IIO.0000000000000298
[4]   Use of a Neural Net to Model the Impact of Optical Coherence Tomography Abnormalities on Vision in Age-related Macular Degeneration [J].
Aslam, Tariq M. ;
Zaki, Haider R. ;
Mahmood, Sajjad ;
Ali, Zaria C. ;
Ahmad, Nur A. ;
Thorell, Mariana R. ;
Balaskas, Konstantinos .
AMERICAN JOURNAL OF OPHTHALMOLOGY, 2018, 185 :94-100
[5]   Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the "i-ROP'' System and Image Features Associated With Expert Diagnosis [J].
Ataer-Cansizoglu, Esra ;
Bolon-Canedo, Veronica ;
Campbell, J. Peter ;
Bozkurt, Alican ;
Erdogmus, Deniz ;
Kalpathy-Cramer, Jayashree ;
Patel, Samir ;
Jonas, Karyn ;
Chan, R. V. Paul ;
Ostmo, Susan ;
Chiang, Michael F. .
TRANSLATIONAL VISION SCIENCE & TECHNOLOGY, 2015, 4 (06)
[6]   A comprehensive review of diagnostic imaging technologies to evaluate the retina and the optic disk [J].
Bajwa, Asima ;
Aman, Rabia ;
Reddy, Ashvini K. .
INTERNATIONAL OPHTHALMOLOGY, 2015, 35 (05) :733-755
[7]   Prediction of Anti-VEGF Treatment Requirements in Neovascular AMD Using a Machine Learning Approach [J].
Bogunovic, Hrvoje ;
Waldstein, Sebastian M. ;
Schlegl, Thomas ;
Langs, Georg ;
Sadeghipour, Amir ;
Liu, Xuhui ;
Gerendas, Bianca S. ;
Osborne, Aaron ;
Schmidt-Erfurth, Ursula .
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2017, 58 (07) :3240-3248
[8]   Machine Learning of the Progression of Intermediate Age-Related Macular Degeneration Based on OCT Imaging [J].
Bogunovic, Hrvoje ;
Montuoro, Alessio ;
Baratsits, Magdalena ;
Karantonis, Maria G. ;
Waldstein, Sebastian M. ;
Schlanitz, Ferdinand ;
Schmidt-Erfurth, Ursula .
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2017, 58 (06) :BIO141-BIO150
[9]   Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks [J].
Brown, James M. ;
Campbell, J. Peter ;
Beers, Andrew ;
Chang, Ken ;
Ostmo, Susan ;
Chan, R. V. Paul ;
Dy, Jennifer ;
Erdogmus, Deniz ;
Ioannidis, Stratis ;
Kalpathy-Cramer, Jayashree ;
Chiang, Michael F. .
JAMA OPHTHALMOLOGY, 2018, 136 (07) :803-810
[10]   Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis [J].
Burlina, Philippe ;
Pacheco, Katia D. ;
Joshi, Neil ;
Freund, David E. ;
Bressler, Neil M. .
COMPUTERS IN BIOLOGY AND MEDICINE, 2017, 82 :80-86
123456