Artificial intelligence and deep learning in glaucoma: Current state and future prospects

被引:30
作者
Girard, Michael J. A. [1 ]
Schmetterer, Leopold [2 ,3 ,4 ,5 ,6 ,7 ,8 ]
机构
[1] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Ophthalm Engn & Innovat Lab OEIL, Singapore, Singapore
[2] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Ocular Imaging, Singapore, Singapore
[3] Nanyang Technol Univ, Sch Chem & Biomed Engn, Singapore, Singapore
[4] SERI NTU Adv Ocular Engn STANCE, Singapore, Singapore
[5] Duke NUS Med Sch, Ophthalmol & Visual Sci Acad Clin Program, Singapore, Singapore
[6] Med Univ Vienna, Dept Clin Pharmacol, Vienna, Austria
[7] Med Univ Vienna, Ctr Med Phys & Biomed Engn, Vienna, Austria
[8] Inst Clin & Expt Ophthalmol, Basel, Switzerland
来源
GLAUCOMA: A NEURODEGENERATIVE DISEASE OF THE RETINA AND BEYOND - PT B | 2020年 / 257卷
基金
英国医学研究理事会;
关键词
Artificial intelligence; Deep learning; Glaucoma diagnosis; Glaucoma prognosis; Glaucoma screening; Optic nerve head; Structure and function; Optical coherence tomography; OPTICAL COHERENCE TOMOGRAPHY; OPEN-ANGLE GLAUCOMA; VISUAL-FIELD PROGRESSION; FIBER LAYER THICKNESS; NOISE-REDUCTION; LAMINA-CRIBROSA; NEURAL-NETWORK; SEGMENTATION ERRORS; AUTOMATED PERIMETRY; INCIDENT GLAUCOMA;
D O I
10.1016/bs.pbr.2020.07.002
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Over the past few years, there has been an unprecedented and tremendous excitement for artificial intelligence (AI) research in the field of Ophthalmology; this has naturally been translated to glaucoma-a progressive optic neuropathy characterized by retinal ganglion cell axon loss and associated visual field defects. In this review, we aim to discuss how AI may have a unique opportunity to tackle the many challenges faced in the glaucoma clinic. This is because glaucoma remains poorly understood with difficulties in providing early diagnosis and prognosis accurately and in a timely fashion. In the short term, AI could also become a game changer by paving the way for the first cost-effective glaucoma screening campaigns. While there are undeniable technical and clinical challenges ahead, and more so than for other ophthalmic disorders whereby AI is already booming, we strongly believe that glaucoma specialists should embrace AI as a companion to their practice. Finally, this review will also remind ourselves that glaucoma is a complex group of disorders with a multitude of physiological manifestations that cannot yet be observed clinically. AI in glaucoma is here to stay, but it will not be the only tool to solve glaucoma.
引用
收藏
页码:37 / 64
页数:28
相关论文
共 164 条
[1]   The molecular basis of retinal ganglion cell death in glaucoma [J].
Almasieh, Mohammadali ;
Wilson, Ariel M. ;
Morquette, Barbara ;
Vargas, Jorge Luis Cueva ;
Di Polo, Adriana .
PROGRESS IN RETINAL AND EYE RESEARCH, 2012, 31 (02) :152-181
[2]   Comparison of clinicians and an artificial neural network regarding accuracy and certainty in performance of visual field assessment for the diagnosis of glaucoma [J].
Andersson, Sabina ;
Heijl, Anders ;
Bizios, Dimitrios ;
Bengtsson, Boel .
ACTA OPHTHALMOLOGICA, 2013, 91 (05) :413-417
[3]   Automatically Enhanced OCT Scans of the Retina: A proof of concept study [J].
Apostolopoulos, Stefanos ;
Salas, Jazmin ;
Ordonez, Jose L. P. ;
Tan, Shern Shiou ;
Ciller, Carlos ;
Ebneter, Andreas ;
Zinkernagel, Martin ;
Sznitman, Raphael ;
Wolf, Sebastian ;
De Zanet, Sandro ;
Munk, Marion R. .
SCIENTIFIC REPORTS, 2020, 10 (01)
[4]   Relating glaucomatous visual field loss to retinal oxygen delivery and metabolism [J].
Aref, Ahmad A. ;
Maleki, Shervin ;
Tan, Ou ;
Huang, David ;
Varma, Rohit ;
Shahidi, Mahnaz .
ACTA OPHTHALMOLOGICA, 2019, 97 (07) :E968-E972
[5]   Artifacts in Spectral-Domain Optical Coherence Tomography Measurements in Glaucoma [J].
Asrani, Sanjay ;
Essaid, Luma ;
Alder, Brian D. ;
Santiago-Turla, Cecilia .
JAMA OPHTHALMOLOGY, 2014, 132 (04) :396-402
[6]   Microvascular damage assessed by optical coherence tomography angiography for glaucoma diagnosis: a systematic review of the most discriminative regions [J].
Bekkers, Amerens ;
Borren, Noor ;
Ederveen, Vera ;
Fokkinga, Ella ;
De Jesus, Danilo Andrade ;
Brea, Luisa Sanchez ;
Klein, Stefan ;
van Walsum, Theo ;
Barbosa-Breda, Joao ;
Stalmans, Ingeborg .
ACTA OPHTHALMOLOGICA, 2020, 98 (06) :537-558
[7]   Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder [J].
Berchuck, Samuel I. ;
Mukherjee, Sayan ;
Medeiros, Felipe A. .
SCIENTIFIC REPORTS, 2019, 9 (1)
[8]   Trained artificial neural network for glaucoma diagnosis using visual field data - A comparison with conventional algorithms [J].
Bizios, Dimitrios ;
Heijl, Anders ;
Bengtsson, Boel .
JOURNAL OF GLAUCOMA, 2007, 16 (01) :20-28
[9]   Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT [J].
Bizios, Dimitrios ;
Heijl, Anders ;
Hougaard, Jesper Leth ;
Bengtsson, Boel .
ACTA OPHTHALMOLOGICA, 2010, 88 (01) :44-52
[10]   Glaucoma risk index: Automated glaucoma detection from color fundus images [J].
Bock, Ruediger ;
Meier, Joerg ;
Nyul, Laszlo G. ;
Hornegger, Joachim ;
Michelson, Georg .
MEDICAL IMAGE ANALYSIS, 2010, 14 (03) :471-481