OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications

Prahs, Philipp; Radeck, Viola; Mayer, Christian; Cvetkov, Yordan; Cvetkova, Nadezhda; Helbig, Horst; Märker, David

doi:10.1007/s00417-017-3839-y

Cited by 95 publications

(46 citation statements)

References 13 publications

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“…To our knowledge, this is the first time that DL has been shown to accurately accomplish such a challenging task in the field of ophthalmic imaging (i.e., reproducing three-dimensional clinical measurements from two-dimensional clinical images). This finding, together with what has been shown in previous studies, [18][19][20][21][22][23][24][25][26][27][28] underlines the value of DL in enhancing ophthalmic disease surveillance through an automated approach. Our study showed that DL models can accurately identify which CFPs are associated with a clinically significant level of MT.…”

Section: Discussionsupporting

confidence: 82%

“…17 In recent years, DL has gained an incredible momentum in the field of clinical ophthalmology and has opened up new possibilities for the automated detection of anomalies and grading of retinal diseases. [18][19][20][21][22][23][24][25][26][27][28] Compared to feature-based machine learning, DL has the advantage of offering an end-toend solution between raw images and a selected outcome variable. Consequently, DL does not require the specification of known clinical features to construct a detection model, as DL learns directly from raw images without being limited by a priori assumptions on the information contained by the images themselves.…”

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confidence: 99%

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Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs

Arcadu

Benmansour

Maunz

et al. 2019

Invest. Ophthalmol. Vis. Sci.

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PURPOSE. To develop deep learning (DL) models for the automatic detection of optical coherence tomography (OCT) measures of diabetic macular thickening (MT) from color fundus photographs (CFPs). METHODS. Retrospective analysis on 17,997 CFPs and their associated OCT measurements from the phase 3 RIDE/RISE diabetic macular edema (DME) studies. DL with transfer-learning cascade was applied on CFPs to predict time-domain OCT (TD-OCT)-equivalent measures of MT, including central subfield thickness (CST) and central foveal thickness (CFT). MT was defined by using two OCT cutoff points: 250 lm and 400 lm. A DL regression model was developed to directly quantify the actual CFT and CST from CFPs. RESULTS. The best DL model was able to predict CST ‡ 250 lm and CFT ‡ 250 lm with an area under the curve (AUC) of 0.97 (95% confidence interval [CI], 0.89-1.00) and 0.91 (95% CI, 0.76-0.99), respectively. To predict CST ‡ 400 lm and CFT ‡ 400 lm, the best DL model had an AUC of 0.94 (95% CI, 0.82-1.00) and 0.96 (95% CI, 0.88-1.00), respectively. The best deep convolutional neural network regression model to quantify CST and CFT had an R 2 of 0.74 (95% CI, 0.49-0.91) and 0.54 (95% CI, 0.20-0.87), respectively. The performance of the DL models declined when the CFPs were of poor quality or contained laser scars. CONCLUSIONS. DL is capable of predicting key quantitative TD-OCT measurements related to MT from CFPs. The DL models presented here could enhance the efficiency of DME diagnosis in tele-ophthalmology programs, promoting better visual outcomes. Future research is needed to validate DL algorithms for MT in the real-world.

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Section: Discussionsupporting

confidence: 82%

mentioning

confidence: 99%

Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs

Arcadu

Benmansour

Maunz

et al. 2019

Invest. Ophthalmol. Vis. Sci.

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“…At present, multiple DLSs have been developed based on OCT in ophthalmology, such as referable retina diseases detection, 27,28 glaucoma quantification and classification, [29][30][31] and antivascular endothelial growth factor treatment. 32 These studies perfectly underscored the promise of DL to lower the cost of disease interpretation from OCT images. Hence, it would be necessary to filter out ungradable images beforehand for a better precision.…”

Section: Discussionmentioning

confidence: 77%

Artificial intelligence deep learning algorithm for discriminating ungradable optical coherence tomography three-dimensional volumetric optic disc scans

et al. 2019

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“…AI has been applied to various adult ophthalmic diseases, including diabetic retinopathy [1,[74][75][76][77], AMD [78][79][80][81][82][83], sight-threatening retinal disease [2,[84][85][86][87][88][89], glaucoma [90][91][92], intraocular lens calculation [93], and keratoconus [94]. It has also been used for robotassisted repair of epiretinal membranes [95], retinal vessel segmentation [96][97][98][99], and systemic disease prediction from fundus images [100].…”

Section: Non-pediatric Applicationsmentioning

confidence: 99%

Artificial intelligence for pediatric ophthalmology

Reid

Eaton

2019

Current Opinion in Ophthalmology

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Purpose of reviewDespite the impressive results of recent artificial intelligence (AI) applications to general ophthalmology, comparatively less progress has been made toward solving problems in pediatric ophthalmology using similar techniques. This article discusses the unique needs of pediatric ophthalmology patients and how AI techniques can address these challenges, surveys recent applications of AI to pediatric ophthalmology, and discusses future directions in the field. Recent findingsThe most significant advances involve the automated detection of retinopathy of prematurity (ROP), yielding results that rival experts. Machine learning (ML) has also been successfully applied to the classification of pediatric cataracts, prediction of post-operative complications following cataract surgery, detection of strabismus and refractive error, prediction of future high myopia, and diagnosis of reading disability via eye tracking. In addition, ML techniques have been used for the study of visual development, vessel segmentation in pediatric fundus images, and ophthalmic image synthesis. SummaryAI applications could significantly benefit clinical care for pediatric ophthalmology patients by optimizing disease detection and grading, broadening access to care, furthering scientific discovery, and improving clinical efficiency. These methods need to match or surpass physician performance in clinical trials before deployment with patients. Due to widespread use of closed-access data sets and software implementations, it is difficult to directly compare the performance of these approaches, and reproducibility is poor. Open-access data sets and software implementations could alleviate these issues, and encourage further AI applications to pediatric ophthalmology.

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OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications

Cited by 95 publications

References 13 publications

Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs

Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs

Artificial intelligence deep learning algorithm for discriminating ungradable optical coherence tomography three-dimensional volumetric optic disc scans

Artificial intelligence for pediatric ophthalmology

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