Discriminating Healthy Optic Discs and Visible Optic Disc Drusen on Fundus Autofluorescence and Color Fundus Photography Using Deep Learning-A Pilot Study.

The aim of this study was to use deep learning based on a deep convolutional neural network (DCNN) for automated image classification of healthy optic discs (OD) and visible optic disc drusen (ODD) on fundus autofluorescence (FAF) and color fundus photography (CFP). In this study, a total of 400 FAF and CFP images of patients with ODD and healthy controls were used. A pre-trained multi-layer Deep Convolutional Neural Network (DCNN) was trained and validated independently on FAF and CFP images. Training and validation accuracy and cross-entropy were recorded. Both generated DCNN classifiers were tested with 40 FAF and CFP images (20 ODD and 20 controls). After the repetition of 1000 training cycles, the training accuracy was 100%, the validation accuracy was 92% (CFP) and 96% (FAF), respectively. The cross-entropy was 0.04 (CFP) and 0.15 (FAF). The sensitivity, specificity, and accuracy of the DCNN for classification of FAF images was 100%. For the DCNN used to identify ODD on color fundus photographs, sensitivity was 85%, specificity 100%, and accuracy 92.5%. Differentiation between healthy controls and ODD on CFP and FAF images was possible with high specificity and sensitivity using a deep learning approach.

Optimizing intraocular lens power calculation using adjusted conventional keratometry for cataract surgery combined with Descemet membrane endothelial keratoplasty.

PURPOSE: To evaluate the utility of intraocular lens (IOL) power calculation using adjusted conventional keratometry (K) according to postoperative posterior to preoperative anterior corneal curvature radii (PPPA) ratio for eyes with Fuch's dystrophy undergoing cataract surgery combined with Descemet membrane endothelial keratoplasty (triple DMEK). METHODS: A fictitious refractive index (FRI) was determined (Pentacam HR®) based on the PPPA ratio in 50 eyes undergoing triple DMEK. Adjusted corneal power was calculated in every eye using adjusted K values: K values determined by the IOLMaster were converted to adjusted anterior corneal radius using the mean FRI. Posterior corneal radius was calculated using the mean PPPA ratio. Adjusted corneal power was determined based on the calculated corneal radii and thick lens formula. Refractive errors calculated using the Haigis, SRK/T, and HofferQ formulae based on the adjusted corneal power were compared with those based on conventional K measurements. RESULTS: Calculated PPPA ratio and FRI were 0.801 and 1.3271. Mean prediction error based on conventional K was in the hyperopic direction (Haigis: 0.84D; SRK/T: 0.74D; HofferQ: 0.74D) and significantly higher (P < 0.001) than that based on adjusted corneal power (0.18D, 0.22D, and 15D, respectively). When calculated according to adjusted corneal power, the percentage of eyes with a hyperopic shift > 0.5D fell significantly from 64 to 30% (Haigis), 62 to 36% (SRK/T), and 58 to 26% (HofferQ), respectively. CONCLUSION: IOL power calculation based on adjusted corneal power can be used to reduce the risk of a hyperopic shift after triple DMEK and provides a more accurate refractive outcome than IOL power calculation using conventional K.

The impact of motion artifacts on quantitative optical coherence tomography angiography analysis in Parkinson's disease.

We have previously shown that OCTA imaging in PD patients can be challenging. Our data suggest that retinal perfusion is reduced in both plexuses in PD, which may serve as a noninvasive biomarker in the future. Yet, control of motion artifacts in OCTA measurements is critical in this motor-impaired cohort.

Longitudinal choriocapillaris changes in the presence of reticular pseudodrusen.

To determine longitudinal changes in choriocapillaris (CC) measures in eyes with reticular pseudodrusen (RPD) using optical coherence tomography angiography (OCTA). In this observational prospective study, 20 patients with exclusively RPD and no other alteration due to age-related macular degeneration were included. Eight RPD patients were re-examined at 5-year follow-up. Multimodal imaging was performed at baseline and at 5-year follow-up. OCTA CC images were analyzed for number, size and total area of flow deficits (FD), mean signal intensity, signal intensity standard deviation and kurtosis of signal intensity distribution in the ring area between a circle of 4 mm diameter and a circle of 6 mm diameter and in the superior ring quadrant. Area affected by RPD increased from 19.36 ± 8.39 mm2 at baseline to 37.77 ± 9.03 mm2 at 5-year follow-up. At baseline, percent of CC FD area was greater in RPD eyes (quadrant: p < 0.001; ring: p < 0.001) compared to controls. Besides, RPD eyes revealed a lower mean intensity signal (quadrant: p < 0.001; ring: p < 0.001). Evaluation of CC parameters suggested significant group × time interaction effects for CC FD (p = 0.04) and mean intensity signal (p = 0.004), in that RPD eyes presented increased CC FD and decreased mean intensity signal at follow-up. OCTA CC decorrelation signal further decreases in RPD patients over 5 years in both RPD-affected and RPD-unaffected macular areas.

CILIORETINAL ARTERIES INFLUENCE OPTIC NERVE HEAD, PERIPAPILLARY, AND MACULAR VESSEL DENSITIES IN HEALTHY EYES: An Optical Coherence Tomography Angiography Study.

BACKGROUND/PURPOSE: To analyze the influence of a cilioretinal artery (CRA) on macular and peripapillary vessel density in healthy eyes as measured using optical coherence tomography angiography. METHODS: A total of 83 eyes of 83 patients were included in this study. Optical coherence tomography angiography was performed using the RTVue XR Avanti with AngioVue (Optovue Inc). The macula was imaged with a 3 × 3-mm scan, whereas for the optic nerve head a 4.5 × 4.5-mm scan was taken. Optical coherence tomography angiography images of the optic nerve head were screened for the presence of a CRA. RESULTS: In 31 eyes, a CRA was detected (37.3%). The vessel density in eyes with a CRA was significantly lower within the optic nerve head (P = 0.005) but higher in the peripapillary capillary network (P < 0.001) and (whole en face) macular superficial capillary plexus (P = 0.025), when compared with eyes with no CRA. CONCLUSION: Our findings reveal that in eyes with a CRA, the vessel density in the peripapillary and macular superficial capillary plexus is increased, whereas the optic nerve head perfusion (as indicated by vessel density in the inside disk region) is decreased. This has to be considered when analyzing quantitative optical coherence tomography angiography parameters in scientific and clinical applications.

Applicability of optical coherence tomography angiography (OCTA) imaging in Parkinson's disease.

To evaluate the significance of motion artifacts in optical coherence tomography angiography (OCTA) images of patients with Parkinson's disease (PD) and healthy controls. In this prospective, cross-sectional study subjects with medicated PD (ON) and healthy, age- and gender-matched volunteers were recruited. Participants underwent specific ophthalmological examinations, including OCTA. Angiograms of the superficial retinal capillary plexus were evaluated for the type and frequency of artifacts using a validated motion artifact score (MAS). A total of 30 PD patients (60 eyes), average disease duration of 9.61 ± 5.55 years, and 30 matched, healthy controls (60 eyes) were recruited. Twenty percent of all eyes had an eye disease, unknown to the participant, with a significant impact on OCTA results. After cleansing the dataset by excluding subjects with confounding ocular comorbidities 42 eyes of 28 PD patients and 53 eyes of 29 healthy controls were further evaluated. Overall MAS and all five subtypes of motion artifacts were comparable without significant differences between groups. OCTA can be used in treated PD patients (ON) without a significant increase in motion artifacts. Nevertheless, special attention should be paid to image quality during the acquisition of OCTA data, for which an experienced OCTA operator is useful.

Assessing the validity of corneal power estimation using conventional keratometry for intraocular lens power calculation in eyes with Fuch's dystrophy undergoing Descemet membrane endothelial keratoplasty.

PURPOSE: The present retrospective study was designed to test the hypothesis that the postoperative posterior to preoperative anterior corneal curvature radii (PPPA) ratio in eyes with Fuch's dystrophy undergoing Descemet membrane endothelial keratoplasty (DMEK) is significantly different to the posterior to anterior corneal curvature radii (PA) ratio in virgin eyes and therefore renders conventional keratometry (K) and the corneal power derived by it invalid for intraocular lens (IOL) power calculation. METHODS: Measurement of corneal parameters was performed using Scheimpflug imaging (Pentacam HR, Oculus, Germany). In 125 eyes with Fuch's dystrophy undergoing DMEK, a fictitious keratometer index was calculated based on the PPPA ratio. The preoperative and postoperative keratometer indices and PA ratios were also determined. Results were compared to those obtained in a control group consisting of 125 eyes without corneal pathologies. Calculated mean ratios and keratometer indices were then used to convert the anterior corneal radius in each eye before DMEK to postoperative posterior and total corneal power. To assess the most appropriate ratio and keratometer index, predicted and measured powers were compared using Bland-Altman plots. RESULTS: The PPPA ratio determined in eyes with Fuch's dystrophy undergoing DMEK was significantly different (P < 0.001) to the PA ratio in eyes without corneal pathologies. Using the mean PA ratio (0.822) and keratometer index (1.3283), calculated with the control group data to convert the anterior corneal radius before DMEK to power, leads to a significant (P < 0.001) underestimation of postoperative posterior negative corneal power (mean difference (∆ = - 0.14D ± 0.30) and overestimation of total corneal power (∆ = - 0.45D ± 1.08). The lowest prediction errors were found using the geometric mean PPPA ratio (0.806) and corresponding keratometer index (1.3273) to predict the postoperative posterior (∆ = - 0.01 ± 0.30) and total corneal powers (∆ = - 0.32D ± 1.08). CONCLUSIONS: Corneal power estimation using conventional K for IOL power calculation is invalid in eyes with Fuch's dystrophy undergoing DMEK. To avoid an overestimation of corneal power and minimize the risk of a postoperative hyperopic shift, conventional K for IOL power calculation should be adjusted in eyes with Fuch's dystrophy undergoing cataract surgery combined with DMEK. The fictitious PPPA ratio and keratometer index may guide further IOL power calculation methods to achieve this.

Impact of integrated multiple image averaging on OCT angiography image quality and quantitative parameters.

PURPOSE: Multiple image averaging (MIA) is a new approach to improve OCT angiography (OCTA) imaging. The aim of this work was to analyze the impact of MIA on image quality and quantitative OCTA parameters. METHODS: Twenty eyes from 20 healthy volunteers (55.65 ± 14.8 years) were prospectively enrolled. Imaging was performed using two commercially available OCTA devices (Canon OCT HS-100, Optovue AngioVue) using a uniform imaging protocol. Each participant had two single scans of the macula (3 × 3mm, Canon and Optovue) as well as five continuous single scan imaging procedures (3 × 3mm each) using the Canon device. Three out of five of these images with highest quality were manually chosen and then automatically processed by the Canon device using MIA. The superficial retinal plexus of the single scans and of MIA images was analyzed with regard to the device' own image quality scores (IQS), peak signal-to-noise ratio (PSNR), the size of the foveolar avascular zone (FAZ), and vessel density (VD). Image acquisition times were recorded. Parameters were compared between the devices and the different imaging protocols. RESULTS: Average acquisition time was significantly higher for the MIA compared with the single measurements (29.09 ± 10.19 seconds (s) (MIA) vs. 5.56 ± 2.17 s (Canon single scan) vs. 20.28 ± 6.81 s (Optovue) (p < 0.001)). IQS showed no significant differences between the devices and between the recording protocols. PSNR was 12.38 ± 0.20 (Canon single scan), 13.01 ± 0.36 (Canon MIA), and 14.34 ± 0.60 (Optovue) (p < 0.001 between the groups). Mean FAZ area in Canon single scans was 0.29 ± 0.06 mm2, 0.27 ± 0.07 mm2 using MIA, and 0.27 ± 0.08 mm2 using the Optovue device. There was no significant difference between mean FAZ measurements before and after averaging (Canon single scan vs. MIA, p = 0.168). VD of the parafoveal area using MIA was significantly lower compared with both single scans (p < 0.001). CONCLUSIONS: MIA can improve PSNR, but it also reduces imaging speed and significantly affects VD measurements. Therefore, when comparing OCTA data, the use of uniform imaging protocols is required.

Optical coherence tomography angiography image quality assessment at varying retinal expertise levels.

PURPOSE: To compare optical coherence tomography angiography (OCT-A) image quality gradings performed by readers of varying retinal expertise levels in different retinal diseases. METHODS: Central 3 × 3 mm2 OCT-A images (AngioVue, Optovue) of 57 healthy controls (50.9 ± 22.4 years) and 148 patients (66.5 ± 14.1 years) affected by various chorioretinal diseases were retrospectively analyzed including early age-related macular degeneration (AMD, n = 26), neovascular AMD (nAMD, n = 22), and geographic atrophy due to AMD (GA, n = 6), glaucoma (n = 28), central serous chorioretinopathy (CSC, n = 14), epiretinal membrane (EM, n = 26), retinitis pigmentosa (RP, n = 16), and retinal venous occlusion (RVO, n = 10). A senior expert in medical retina (SE), an ophthalmology resident (OR), and a non-ophthalmologic medical doctor (MD) independently assessed OCT-A image quality using the motion artifact score (MAS) and the segmentation accuracy score (SAS). RESULTS: Regarding MAS, inter-reader agreement between SE and OR was 93.7% (Cohen's kappa = 0.907) and 85.4% (Cohen's kappa = 0.786) between SE and MD. Regarding SAS, inter-reader agreement between SE and OR was 95.1% (Cohen's kappa = 0.92) and 92.2% (Cohen's kappa = 0.874) between SE and MD. In the SAS analysis, signal strength index (SSI) and presence of retinal pathology had a significant influence on the overall agreement (P = 0.046; P < 0.001). CONCLUSIONS: OCT-A image quality assessment can be performed most reliably by an ophthalmologist with knowledge in retinal image analysis. Yet, well-instructed non-ophthalmologic assessors show only slightly inferior results and, thus, may be integrated in routine OCT-A image quality assessment as well.

[Physical Therapy of Non-Exudative Age-Related Macular Degeneration]. / Physikalische Therapie der nicht exsudativen altersabhängigen Makuladegeneration.

Despite the success of anti-VEGF therapy (VEGF: vascular endothelial growth factor) in neovascular age-related macular degeneration (AMD) in the last decade, many unmet needs in AMD management remain. In order to improve patient eye health and relieve the burden on health systems, the development of new intervention options appears to be of great importance if they can delay or even prevent the progression of an early form into a late form. In the field of physical treatment for non-exudative AMD, there is no recognised therapy procedure to date. It now appears appropriate to pursue further research efforts in the field of intraocular blue filter lenses and subthreshold laser treatment in prospective studies. The following article provides an overview of the current strategies of physical therapy for non-exudative AMD.

[Expression of Motion Artifacts in OCT-Angiography Imaging in Healthy Subjects Using Two Different Devices]. / Vergleich der Bildqualität zweier unterschiedlicher OCT-Angiografie-Systeme mit Fokus auf Bewegungsartefakten bei gesunden Probanden.

BACKGROUND: To compare the expression of motion artifacts in optical coherence tomography angiography (OCT-A) in healthy subjects using two different devices. METHODS: In this study, 25 eyes of 25 healthy volunteers with no history of any ocular disease or ocular surgery were included. OCT-A imaging was performed using the RTVue XR Avanti (Optovue Inc., Fremont, California, USA) and the Spectralis OCT-A (Heidelberg Engineering, Heidelberg, Deutschland). The macula was imaged twice in each proband with active eye tracking (ET) using a 3 × 3 mm2 or a 10 × 10° scan, respectively. The expression of motion artifact was analyzed by two independent readers in the superficial OCT-angiogram using the Motion Artifact Score (MAS). RESULTS: The signal strength index (SSI) was 73.0 ± 7.8 (Optovue) and 39.6 ± 3.6 (Heidelberg), which is equivalent to 73.0% (Optovue SSImax = 100 = 100%) and 79.2% (SSImax = 50 = 100%) of the maximum quality score. Both devices showed a very good image quality (mean MAS Optovue: 1.32 ± 0.551, mean MAS Heidelberg: 1.7 ± 0.789, p = 0.006). Of all measurements, quilting/banding was found in 20% of Optovue patients (10/50) and 6% of Heidelberg patients (3/50). Stretching was found in 4% of Optovue patients (2/50) and in 6% of Heidelberg patients (3/50). Vessel doubling was only seen in one Optovue angiogram (2%) as well as a displacement (2%). Blink lines only existed in three Heidelberg angiograms (6%). CONCLUSION: Despite different software and hardware approaches, both devices were able to take high-quality images with a very low prevalence of motion artifacts. Nevertheless, these artifacts still also occur in healthy subjects with good fixation. With regards to MAS, there was a high agreement between the two readers. However, the analysis of artifacts remains complex and requires experience as well as a precise assessment in evaluating OCT-A images.

Using Deep Learning in Automated Detection of Graft Detachment in Descemet Membrane Endothelial Keratoplasty: A Pilot Study.

PURPOSE: To evaluate a deep learning-based method to automatically detect graft detachment (GD) after Descemet membrane endothelial keratoplasty (DMEK) in anterior segment optical coherence tomography (AS-OCT). METHODS: In this study, a total of 1172 AS-OCT images (609: attached graft; 563: detached graft) were used to train and test a deep convolutional neural network to automatically detect GD after DMEK surgery in AS-OCT images. GD was defined as a not completely attached graft. After training with 1072 of these images (559: attached graft; 513: detached graft), the created classifier was tested with the remaining 100 AS-OCT scans (50: attached graft; 50 detached: graft). Hereby, a probability score for GD (GD score) was determined for each of the tested OCT images. RESULTS: The mean GD score was 0.88 ± 0.2 in the GD group and 0.08 ± 0.13 in the group with an attached graft. The differences between both groups were highly significant (P < 0.001). The sensitivity of the classifier was 98%, the specificity 94%, and the accuracy 96%. The coefficient of variation was 3.28 ± 6.90% for the GD group and 2.82 ± 3.81% for the graft attachment group. CONCLUSIONS: With the presented deep learning-based classifier, reliable automated detection of GD after DMEK is possible. Further work is needed to incorporate information about the size and position of GD and to develop a standardized approach regarding when rebubbling may be needed.

Evaluation of Ocular Perfusion in Alzheimer's Disease Using Optical Coherence Tomography Angiography.

BACKGROUND: There is increasing evidence for the involvement of cerebrovascular factors in Alzheimer's disease (AD). OBJECTIVE: To evaluate retinal and optic nerve head perfusion in patients with AD using optical coherence tomography angiography (OCTA), and to analyze the correlations of quantitative OCTA metrics with AD pathology and vascular cerebral lesions in AD patients. METHODS: 36 eyes of 36 patients with AD (study group) and 38 eyes of 38 healthy subjects (control group) were prospectively included in this study. OCTA was performed using RTVue XR Avanti with AngioVue. In addition, patients underwent a detailed ophthalmological and neurological examination including Mini-Mental State Examination, cerebral magnetic resonance imaging, and amyloid-ß (Aß) and tau levels in the cerebrospinal fluid (CSF). RESULTS: The flow density in the superficial retinal OCT angiogram of the macula in the study group was significantly lower compared to the control group (p = 0.001). There was a significant correlation between the flow density in the superficial retinal OCT angiogram of the macula, as measured using OCTA, and the Fazekas scale (Spearman's correlation coefficient = -0.520; p = 0.003). There was no significant correlation between the Aß or tau levels in the CSF and the flow density data. CONCLUSION: Patients with AD showed a reduced flow density in the radial peripapillary capillaries layer and in the superficial retinal OCT angiogram when compared with healthy controls. The reduced retinal flow density measured using OCTA is not specifically associated with AD pathology but is associated with the vascular cerebral lesions in AD.

Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier.

PURPOSE: To automatically detect and classify geographic atrophy (GA) in fundus autofluorescence (FAF) images using a deep learning algorithm. METHODS: In this study, FAF images of patients with GA, a healthy comparable group and a comparable group with other retinal diseases (ORDs) were used to train a multi-layer deep convolutional neural network (DCNN) (1) to detect GA and (2) to differentiate in GA between a diffuse-trickling pattern (dt-GA) and other GA FAF patterns (ndt-GA) in FAF images. 1. For the automated detection of GA in FAF images, two classifiers were built (GA vs. healthy/GA vs. ORD). The DCNN was trained and validated with 400 FAF images in each case (GA 200, healthy 200, or ORD 200). For the subsequent testing, the built classifiers were then tested with 60 untrained FAF images in each case (AMD 30, healthy 30, or ORD 30). Hereby, both classifiers automatically determined a GA probability score and a normal FAF probability score or an ORD probability score. 2. To automatically differentiate between dt-GA and ndt-GA, the DCNN was trained and validated with 200 FAF images (dt-GA 72; ndt-GA 138). Afterwards, the built classifier was tested with 20 untrained FAF images (dt-GA 10; ndt-GA 10) and a dt-GA probability score and an ndt-GA probability score was calculated. For both classifiers, the performance of the training and validation procedure after 500 training steps was measured by determining training accuracy, validation accuracy, and cross entropy. RESULTS: For the GA classifiers (GA vs. healthy/GA vs. ORD), the achieved training accuracy was 99/98%, the validation accuracy 96/91%, and the cross entropy 0.062/0.100. For the dt-GA classifier, the training accuracy was 99%, the validation accuracy 77%, and the cross entropy 0.166. The mean GA probability score was 0.981 ± 0.048 (GA vs. healthy)/0.972 ± 0.439 (GA vs. ORD) in the GA image group and 0.01 ± 0.016 (healthy)/0.061 ± 0.072 (ORD) in the comparison groups (p < 0.001). The mean dt-GA probability score was 0.807 ± 0.116 in the dt-GA image group and 0.180 ± 0.100 in the ndt-GA image group (p < 0.001). CONCLUSION: For the first time, this study describes the use of a deep learning-based algorithm to automatically detect and classify GA in FAF. Hereby, the created classifiers showed excellent results. With further developments, this model may be a tool to predict the individual progression risk of GA and give relevant information for future therapeutic approaches.

Optical Coherence Tomography Angiography Offers New Insights into Choriocapillaris Perfusion.

The choriocapillaris (CC) represents a fundamentally important vascular layer that is subject to physiologic changes with increasing age and that is also associated with a wide range of chorioretinal diseases. So far, information on blood flow in this specific layer has remained limited. With the advent of optical coherence tomography angiography (OCTA), new perspectives and possibilities of CC imaging have begun to evolve. This article shall review the opportunities and challenges of applying OCTA technology to the CC layer and summarize the current clinical efforts in OCTA CC imaging exemplarily in dry age-related macular degeneration and central serous chorioretinopathy.

Quantitative changes in flow density in patients with adult-onset foveomacular vitelliform dystrophy: an OCT angiography study.

PURPOSE: To quantitatively compare the flow density, the retinal thickness, and the area of the foveal avascular zone (FAZ) between patients with adult-onset foveomacular vitelliform dystrophy (AOFVD) and a healthy controls. METHODS: Thirteen eyes (eight patients) with AOFVD and 13 matched eyes (13 patients) without any ocular pathology were included in this study. A 6 × 6 mm optical coherence tomography angiography (OCTA) scan was performed for every included eye. The flow density (superficial retinal vascular layer, deep retinal vascular layer and choriocapillary layer), retinal thickness and FAZ (superficial retinal vascular layer and deep retinal vascular layer) were subsequently analyzed. RESULTS: The mean flow density was decreased in the AOFVD patients in all measured vascular layers. The difference from the control group was statistically significant in the parafoveal sector of the deep retinal vascular layer (P = 0.02), and a clear trend was found in the superficial retinal vascular layer (P = 0.05). Both groups had comparable FAZs in the superficial and deep retinal vascular layers. The retinal thickness values were higher in the fovea (P = 0.840) and lower in the parafoveal sectors (P = 0.125). The difference was significant in the superior parafoveal sector (P = 0.034). CONCLUSIONS: Flow densities as measured by OCTA are decreased in the superficial retinal vascular layer and the deep retinal vascular layer in patients with AOFVD. These findings could be helpful for diagnosing and understanding the pathogenesis of this disease.

Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning.

PURPOSE: Our purpose was to use deep learning for the automated detection of age-related macular degeneration (AMD) in spectral domain optical coherence tomography (SD-OCT). METHODS: A total of 1112 cross-section SD-OCT images of patients with exudative AMD and a healthy control group were used for this study. In the first step, an open-source multi-layer deep convolutional neural network (DCNN), which was pretrained with 1.2 million images from ImageNet, was trained and validated with 1012 cross-section SD-OCT scans (AMD: 701; healthy: 311). During this procedure training accuracy, validation accuracy and cross-entropy were computed. The open-source deep learning framework TensorFlow™ (Google Inc., Mountain View, CA, USA) was used to accelerate the deep learning process. In the last step, a created DCNN classifier, using the information of the above mentioned deep learning process, was tested in detecting 100 untrained cross-section SD-OCT images (AMD: 50; healthy: 50). Therefore, an AMD testing score was computed: 0.98 or higher was presumed for AMD. RESULTS: After an iteration of 500 training steps, the training accuracy and validation accuracies were 100%, and the cross-entropy was 0.005. The average AMD scores were 0.997 ± 0.003 in the AMD testing group and 0.9203 ± 0.085 in the healthy comparison group. The difference between the two groups was highly significant (p < 0.001). CONCLUSIONS: With a deep learning-based approach using TensorFlow™, it is possible to detect AMD in SD-OCT with high sensitivity and specificity. With more image data, an expansion of this classifier for other macular diseases or further details in AMD is possible, suggesting an application for this model as a support in clinical decisions. Another possible future application would involve the individual prediction of the progress and success of therapy for different diseases by automatically detecting hidden image information.