Macular Optical Coherence Tomography Images Research Articles

Factors affecting prognosis and need for anti-vascular endothelial growth factor injections in wet age-related macular degeneration.

To understand factors affecting visual prognosis and the number of intravitreal antivascular endothelial growth factor (anti-VEGF) injections needed to stabilize wet age-related macular degeneration (AMD). In this retrospective cohort, 119 treatment-naïve wet AMD patients were followed for two years. In patients with bilateral disease, the eye with worse best-corrected visual acuity (BCVA) or that received more intravitreal injections was recruited as the study eye. In all visits, BCVA was recorded, ophthalmological examination was performed including macular optical coherence tomography imaging. Twenty health status/lifestyle questions were asked to the patients via phone as potential risk factors. All patients received 3 loading doses of intravitreal bevacizumab injections and received repeat injections of aflibercept or ranibizumab when the eye had a new, active neovascular lesion. Patients who took regular micronutrition had similar visual outcome and injection numbers compared to the ones who did not. Patients with bilateral disease needed less intravitreal injections compared to unilateral AMD patients (p = 0.016) and women on hormone replacement therapy (HRT) required less injections compared to the women who were not (p = 0.024). Female patients had a mean gain of 2.7 letters while male patients lost 3.8 letters (p = 0.038). Wet AMD started at an earlier age in smokers (p = 0.002). Patients with a better education level presented earlier with better BCVA (p = 0.037). HRT and anti-VEGF injections to the fellow eye improved the prognosis of wet AMD, while male patients had slightly worse prognosis. Estrogen's protective effects and potential contribution in wet AMD needs further attention. Retrospectively registered: 2020/0622.

Accuracy of generative deep learning model for macular anatomy prediction from optical coherence tomography images in macular hole surgery

This study aims to propose a generative deep learning model (GDLM) based on a variational autoencoder that predicts macular optical coherence tomography (OCT) images following full-thickness macular hole (FTMH) surgery and evaluate its clinical accuracy. Preoperative and 6-month postoperative swept-source OCT data were collected from 150 patients with successfully closed FTMH using 6 × 6 mm2 macular volume scan datasets. Randomly selected and augmented 120,000 training and 5000 validation pairs of OCT images were used to train the GDLM. We assessed the accuracy and F1 score of concordance for neurosensory retinal areas, performed Bland–Altman analysis of foveolar height (FH) and mean foveal thickness (MFT), and predicted postoperative external limiting membrane (ELM) and ellipsoid zone (EZ) restoration accuracy between artificial intelligence (AI)-OCT and ground truth (GT)-OCT images. Accuracy and F1 scores were 94.7% and 0.891, respectively. Average FH (228.2 vs. 233.4 μm, P = 0.587) and MFT (271.4 vs. 273.3 μm, P = 0.819) were similar between AI- and GT-OCT images, within 30.0% differences of 95% limits of agreement. ELM and EZ recovery prediction accuracy was 88.0% and 92.0%, respectively. The proposed GDLM accurately predicted macular OCT images following FTMH surgery, aiding patient and surgeon understanding of postoperative macular features.

Prevalence and Distribution of Macular Fluid with Central Retinal Artery Occlusion and Anterior Ischemic Optic Neuropathy.

To examine the prevalence and distribution of fluid within a cohort of eyes with acute central retinal artery occlusion (CRAO) and non-arteritic anterior ischemic optic neuropathy (AION) using optical coherence tomography (OCT). A retrospective analysis of patient records and OCT imaging. Patients presenting with acute CRAO or AION who had available macular OCT imaging and no co-morbidities known to cause macular fluid were included in the analysis. Baseline characteristics, visual acuity (VA), and fluid presence and distribution among the retinal layers were recorded. In the 16 eyes with acute CRAO, fluid was noted in 5 eyes (31%), which was mainly subretinal (3 eyes) or intraretinal located within the outer retinal layers (3 eyes). Only one eye had inner retinal cysts. Of the 11 eyes with acute AION, fluid was present in 8 eyes (73%). Subretinal fluid was noted in 4 eyes and extended to the foveal area in 3 of them, and outer retinal versus inner retinal cysts were noted in 6 versus 3 eyes, respectively. None of the eyes showed hard exudate deposition. In the small subset of eyes with CRAO and macular fluid that were followed-up, VA improved, while in eyes with AION, VA remained stable. Macular fluid on OCT is not an uncommon feature of acute CRAO and AION and is mainly distributed within the outer retinal layers or subretinal space. Fluid is an understudied feature of retinal and optic nerve head infarction and may have a role in predicting neuronal damage extent and visual outcome.

Phenome- and genome-wide analyses of retinal optical coherence tomography images identify links between ocular and systemic health.

The human retina is a multilayered tissue that offers a unique window into systemic health. Optical coherence tomography (OCT) is widely used in eye care and allows the noninvasive, rapid capture of retinal anatomy in exquisite detail. We conducted genotypic and phenotypic analyses of retinal layer thicknesses using macular OCT images from 44,823 UK Biobank participants. We performed OCT layer cross-phenotype association analyses (OCT-XWAS), associating retinal thicknesses with 1866 incident conditions (median 10-year follow-up) and 88 quantitative traits and blood biomarkers. We performed genome-wide association studies (GWASs), identifying inherited genetic markers that influence retinal layer thicknesses and replicated our associations among the LIFE-Adult Study (N = 6313). Last, we performed a comparative analysis of phenome- and genome-wide associations to identify putative causal links between retinal layer thicknesses and both ocular and systemic conditions. Independent associations with incident mortality were detected for thinner photoreceptor segments (PSs) and, separately, ganglion cell complex layers. Phenotypic associations were detected between thinner retinal layers and ocular, neuropsychiatric, cardiometabolic, and pulmonary conditions. A GWAS of retinal layer thicknesses yielded 259 unique loci. Consistency between epidemiologic and genetic associations suggested links between a thinner retinal nerve fiber layer with glaucoma, thinner PS with age-related macular degeneration, and poor cardiometabolic and pulmonary function with a thinner PS. In conclusion, we identified multiple inherited genetic loci and acquired systemic cardio-metabolic-pulmonary conditions associated with thinner retinal layers and identify retinal layers wherein thinning is predictive of future ocular and systemic conditions.

Prediction and Detection of Glaucomatous Visual Field Progression Using Deep Learning on Macular Optical Coherence Tomography.

A deep learning model trained on macular OCT imaging studies detected clinically significant functional glaucoma progression and was also able to predict future progression. To use macular optical coherence tomography (OCT) imaging to predict the future and detect concurrent visual field progression, respectively, using deep learning. A retrospective cohort study. A pretraining data set was comprised of 7,702,201 B-scan images from 151,389 macular OCT studies. The progression detection task included 3902 macular OCT imaging studies from 1534 eyes of 828 patients with glaucoma, and the progression prediction task included 1346 macular OCT studies from 1205 eyes of 784. A novel deep learning method was developed to detect glaucoma progression and predict future progression using macular OCT, based on self-supervised pretraining of a vision transformer (ViT) model on a large, unlabeled data set of OCT images. Glaucoma progression was defined as a mean deviation (MD) rate of change of ≤ -0.5 dB/year over 5 consecutive Humphrey visual field tests, and rapid progression was defined as MD change ≤ -1 dB/year. Diagnostic performance of the ViT model for prediction of future visual field progression and detection of concurrent visual field progression using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The model distinguished stable eyes from progressing eyes, achieving an AUC of 0.90 (95% CI, 0.88-0.91). Rapid progression was detected with an AUC of 0.92 (95% CI, 0.91-0.93). The model also demonstrated high predictive ability for forecasting future glaucoma progression, with an AUC of 0.85 (95% CI 0.83-0.87). Rapid progression was predicted with an AUC of 0.84 (95% CI 0.81-0.86). A deep learning model detected clinically significant functional glaucoma progression using macular OCT imaging studies and was also able to predict future progression. Early identification of patients undergoing glaucoma progression or at high risk for future progression may aid in clinical decision-making.

Deep learning-based estimation of axial length using macular optical coherence tomography images.

This study aimed to develop deep learning models using macular optical coherence tomography (OCT) images to estimate axial lengths (ALs) in eyes without maculopathy. A total of 2,664 macular OCT images from 444 patients' eyes without maculopathy, who visited Beijing Hospital between March 2019 and October 2021, were included. The dataset was divided into training, validation, and testing sets with a ratio of 6:2:2. Three pre-trained models (ResNet 18, ResNet 50, and ViT) were developed for binary classification (AL ≥ 26 mm) and regression task. Ten-fold cross-validation was performed, and Grad-CAM analysis was employed to visualize AL-related macular features. Additionally, retinal thickness measurements were used to predict AL by linear and logistic regression models. ResNet 50 achieved an accuracy of 0.872 (95% Confidence Interval [CI], 0.840-0.899), with high sensitivity of 0.804 (95% CI, 0.728-0.867) and specificity of 0.895 (95% CI, 0.861-0.923). The mean absolute error for AL prediction was 0.83 mm (95% CI, 0.72-0.95 mm). The best AUC, and accuracy of AL estimation using macular OCT images (0.929, 87.2%) was superior to using retinal thickness measurements alone (0.747, 77.8%). AL-related macular features were on the fovea and adjacent regions. OCT images can be effectively utilized for estimating AL with good performance via deep learning. The AL-related macular features exhibit a localized pattern in the macula, rather than continuous alterations throughout the entire region. These findings can lay the foundation for future research in the pathogenesis of AL-related maculopathy.

Deep Transfer Learning for Ethnically Distinct Populations: Prediction of Refractive Error Using Optical Coherence Tomography.

The mismatch between training and testing data distribution causes significant degradation in the deep learning model performance in multi-ethnic scenarios. To reduce the performance differences between ethnic groups and image domains, we built a deep transfer learning model with adaptation training to predict uncorrected refractive errors using posterior segment optical coherence tomography (OCT) images of the macula and optic nerve. Observational, cross-sectional, multicenter study design. We pre-trained a deep learning model on OCT images from the B&VIIT Eye Center (Seoul, South Korea) (N = 2602 eyes of 1301 patients). OCT images from Poona Eye Care (Pune, India) were chronologically sorted into adaptation training data (N = 60 eyes of 30 patients) for transfer learning and test data (N = 142 eyes of 71 patients) for validation. Deep learning models were trained to predict spherical equivalent (SE) and mean keratometry (K) values via transfer learning for domain adaptation. Both adaptation models for SE and K were significantly better than those without adaptation (P < 0.001). In myopia/hyperopia classification, the model trained on circular optic disc OCT images yielded the best performance (accuracy = 74.7%). It also performed best in estimating SE with the lowest mean absolute error (MAE) of 1.58 D. For classifying the degree of corneal curvature, the optic nerve vertical algorithm performed best (accuracy = 65.7%). The optic nerve horizontal model achieved the lowest MAE (1.85 D) when predicting the Kvalue. Saliency maps frequently highlighted the retinal nerve fiber layers. Adaptation training via transfer learning is an effective technique for estimating refractive errors and K values using macular and optic nerve OCT images from ethnically heterogeneous populations. Further studies with larger sample sizes and various data sources are needed to confirm the feasibility of the proposed algorithm.

Results of a survey of ophthalmologists on the special aspects in performing and interpreting optical coherence tomography examination of the macula

BACKGROUND: Optical coherence tomography (OCT) is the main method for in vivo examination of the morphology of ocular posterior segment structures, used in modern ophthalmological practice.&#x0D; AIM: Study of special aspects in performing and interpreting the results of OCT of the macula by ophthalmologists working in the healthcare system of the Russian Federation.&#x0D; MATERIALS AND METHODS: In the anonymous survey, conducted using an original online questionnaire of 12 questions 59 respondents took part. They confirmed being ophthalmologists with experience in performing OCT and gave their consent to took part in the survey.&#x0D; RESULTS: The majority of respondents (79.6%) reported that they face the need to perform OCT without obtaining the patients clinical data. 66.1% of respondents always evaluate the correct placement of segmentation boundaries. Only 56% of the respondents always or often perform frontal (en face) scans during OCT examination of the macula. At the same time, 16.9% of specialists indicated that they did not know what segmentation boundaries are, and 8.5% noted that they did not understand the term en face scan. The principle of layer-by-layer analysis is used by 94.9% of respondents when describing images. Significant discrepancies were also revealed in the interpretation by the respondents of some parameters on specific examples of OCT scans. 71.2% of respondents have experience in using templates when describing OCT images, and 13.6% use them constantly. The proportion of specialists who consider it expedient to develop and introduce into clinical practice a standard protocol (template) for describing macular OCT images was 84.7%.&#x0D; CONCLUSIONS: Insufficient awareness of some ophthalmologists about particular principles of interpreting the results of an OCT imaging and about certain practical aspects of its application was revealed. This indicates the need in improving the quality of informational support and training of specialists. A request was received from ophthalmologists about the need to develop a standard protocol (template) for describing OCT images of the macular area with the aim of subsequent implementation into the clinical practice of the healthcare system of the Russian Federation.

Comparative analysis of alignment algorithms for macular optical coherence tomography imaging

BackgroundOptical coherence tomography (OCT) is the most important and commonly utilized imaging modality in ophthalmology and is especially crucial for the diagnosis and management of macular diseases. Each OCT volume is typically only available as a series of cross-sectional images (B-scans) that are accessible through proprietary software programs which accompany the OCT machines. To maximize the potential of OCT imaging for machine learning purposes, each OCT image should be analyzed en bloc as a 3D volume, which requires aligning all the cross-sectional images within a particular volume.MethodsA dataset of OCT B-scans obtained from 48 age-related macular degeneration (AMD) patients and 50 normal controls was used to evaluate five registration algorithms. After alignment of B-scans from each patient, an en face surface map was created to measure the registration quality, based on an automatically generated Laplace difference of the surface map–the smoother the surface map, the smaller the average Laplace difference. To demonstrate the usefulness of B-scan alignment, we trained a 3D convolutional neural network (CNN) to detect age-related macular degeneration (AMD) on OCT images and compared the performance of the model with and without B-scan alignment.ResultsThe mean Laplace difference of the surface map before registration was 27 ± 4.2 pixels for the AMD group and 26.6 ± 4 pixels for the control group. After alignment, the smoothness of the surface map was improved, with a mean Laplace difference of 5.5 ± 2.7 pixels for Advanced Normalization Tools Symmetric image Normalization (ANTs-SyN) registration algorithm in the AMD group and a mean Laplace difference of 4.3 ± 1.4.2 pixels for ANTs in the control group. Our 3D CNN achieved superior performance in detecting AMD, when aligned OCT B-scans were used (AUC 0.95 aligned vs. 0.89 unaligned).ConclusionsWe introduced a novel metric to quantify OCT B-scan alignment and compared the effectiveness of five alignment algorithms. We confirmed that alignment could be improved in a statistically significant manner with readily available alignment algorithms that are available to the public, and the ANTs algorithm provided the most robust performance overall. We further demonstrated that alignment of OCT B-scans will likely be useful for training 3D CNN models.

Deep learning-based postoperative visual acuity prediction in idiopathic epiretinal membrane

BackgroundTo develop a deep learning (DL) model based on preoperative optical coherence tomography (OCT) training to automatically predict the 6-month postoperative visual outcomes in patients with idiopathic epiretinal membrane (iERM).MethodsIn this retrospective cohort study, a total of 442 eyes (5304 images in total) were enrolled for the development of the DL and multimodal deep fusion network (MDFN) models. All eyes were randomized into a training dataset with 265 eyes (60.0%), a validation dataset with 89 eyes (20.1%), and an internal testing dataset with the remaining 88 eyes (19.9%). The input variables for prediction consisted of macular OCT images and diverse clinical data. Inception-Resnet-v2 network was utilized to estimate the 6-month postoperative best-corrected visual acuity (BCVA). Concurrently, a regression model was developed using the clinical data and OCT parameters in the training data set for predicting postoperative BCVA. The reliability of the models was subsequently evaluated using the testing dataset.ResultsThe prediction DL algorithm exhibited a mean absolute error (MAE) of 0.070 logMAR and root mean square error (RMSE) of 0.11 logMAR in the testing dataset. The DL model demonstrated a robust promising performance with R2 = 0.80, notably superior to R2 = 0.49 of the regression model. The percentages of BCVA prediction errors within ± 0.20 logMAR amounted to 94.32% in the testing dataset.ConclusionsThe OCT-based DL model demonstrated sensitivity and accuracy in predicting postoperative BCVA in iERM patients. This innovative DL model exhibits substantial potential for integration into surgical planning protocols.

Prediction of Visual Impairment in Epiretinal Membrane and Feature Analysis: A Deep Learning Approach Using Optical Coherence Tomography.

The aim was to develop a deep learning model for predicting the extent of visual impairment in epiretinal membrane (ERM) using optical coherence tomography (OCT) images, and to analyze the associated features. Six hundred macular OCT images from eyes with ERM and no visually significant media opacity or other retinal diseases were obtained. Those with best-corrected visual acuity ≤20/50 were classified as "profound visual impairment," while those with best-corrected visual acuity >20/50 were classified as "less visual impairment." Ninety percent of images were used as the training data set and 10% were used for testing. Two convolutional neural network models (ResNet-50 and ResNet-18) were adopted for training. The t-distributed stochastic neighbor-embedding approach was used to compare their performances. The Grad-CAM technique was used in the heat map generative phase for feature analysis. During the model development, the training accuracy was 100% in both convolutional neural network models, while the testing accuracy was 70% and 80% for ResNet-18 and ResNet-50, respectively. The t-distributed stochastic neighbor-embedding approach found that the deeper structure (ResNet-50) had better discrimination on OCT characteristics for visual impairment than the shallower structure (ResNet-18). The heat maps indicated that the key features for visual impairment were located mostly in the inner retinal layers of the fovea and parafoveal regions. Deep learning algorithms could assess the extent of visual impairment from OCT images in patients with ERM. Changes in inner retinal layers were found to have a greater impact on visual acuity than the outer retinal changes.

Алгоритм сегментации ОКТ изображений макулы для анализа пациентов с возрастной макулярной дегенерацией

Age-related macular degeneration (AMD) is one of the main causes of loss of sight and hypovision in people over working age. Results of optical coherence tomography (OCT) are essential for diagnostics of the disease. Developing the recommendation system to analyze OCT images will reduce the time to process visual data and decrease the probability of errors while working as a doctor. The purpose of the study was to develop an algorithm of segmentation to analyze the results of macular OCT in patients with AMD. It allows to provide a correct prediction of an AMD stage based on the form of discovered pathologies. A program has been developed in the Python programming language using the Pytorch and TensorFlow libraries. Its quality was estimated using OCT macular images of 51 patients with early, intermediate, late AMD. A segmentation algorithm of OCT images was developed based on convolutional neural network. UNet network was selected as architecture of high-accuracy neural net. The neural net is trained on macular OCT images of 125 patients (197 eyes). The author algorithm displayed 98.1% of properly segmented areas on OCT images, which are the most essential for diagnostics and determination of an AMD stage. Weighted sensitivity and specificity of AMD stage classifier amounted to 83.8% and 84.9% respectively. The developed algorithm is promising as a recommendation system that implements the AMD classification based on data that promote taking decisions regarding the treatment strategy.

Development of a deep learning algorithm for myopic maculopathy classification based on OCT images using transfer learning.

PurposeTo apply deep learning (DL) techniques to develop an automatic intelligent classification system identifying the specific types of myopic maculopathy (MM) based on macular optical coherence tomography (OCT) images using transfer learning (TL).MethodIn this retrospective study, a total of 3,945 macular OCT images from 2,866 myopic patients were recruited from the ophthalmic outpatients of three hospitals. After culling out 545 images with poor quality, a dataset containing 3,400 macular OCT images was manually classified according to the ATN system, containing four types of MM with high OCT diagnostic values. Two DL classification algorithms were trained to identify the targeted lesion categories: Algorithm A was trained from scratch, and algorithm B using the TL approach initiated from the classification algorithm developed in our previous study. After comparing the training process, the algorithm with better performance was tested and validated. The performance of the classification algorithm in the test and validation sets was evaluated using metrics including sensitivity, specificity, accuracy, quadratic-weighted kappa score, and the area under the receiver operating characteristic curve (AUC). Moreover, the human-machine comparison was conducted. To better evaluate the algorithm and clarify the optimization direction, the dimensionality reduction analysis and heat map analysis were also used to visually analyze the algorithm.ResultsAlgorithm B showed better performance in the training process. In the test set, the algorithm B achieved relatively robust performance with macro AUC, accuracy, and quadratic-weighted kappa of 0.986, 96.04% (95% CI: 0.951, 0.969), and 0.940 (95% CI: 0.909–0.971), respectively. In the external validation set, the performance of algorithm B was slightly inferior to that in the test set. In human-machine comparison test, the algorithm indicators were inferior to the retinal specialists but were the same as the ordinary ophthalmologists. In addition, dimensionality reduction visualization and heatmap visualization analysis showed excellent performance of the algorithm.ConclusionOur macular OCT image classification algorithm developed using the TL approach exhibited excellent performance. The automatic diagnosis system for macular OCT images of MM based on DL showed potential application prospects.

Retinal thickness in healthy Australian Aboriginal and Torres Strait Islander children.

BackgroundUnderstanding normative retinal thickness characteristics is critical for diagnosis and monitoring of pathology, particularly in those predisposed to retinal disease. The macular retinal layer thickness of Australian Aboriginal and/or Torres Strait Islander children was examined using spectral-domain optical coherence tomography.MethodsHigh-resolution macular optical coherence tomography imaging was performed on 100 Aboriginal and/or Torres Strait Islander children and 150 non-Indigenous visually healthy children aged 4–18 years. The imaging protocol included a 6-line radial scan centred on the fovea. Images were segmented using semi-automated software to derive thickness of the total retina, inner and outer retina, and individual retinal layers across the macular region. Repeated measures ANOVAs examined variations in thickness associated with retinal region, age, gender and Indigenous status.ResultsRetinal thickness showed significant topographical variations (p < 0.01), being thinnest in the foveal zone, and thickest in the parafovea. The retina of Aboriginal and/or Torres Strait Islander children was significantly thinner than non-Indigenous children in the foveal (p < 0.001), parafoveal (p = 0.002), and perifoveal zones (p = 0.01), with the greatest difference in the foveal zone (mean difference: 14.2 μm). Inner retinal thickness was also thinner in Aboriginal and/or Torres Strait Islander children compared to non-Indigenous children in the parafoveal zone (p < 0.001), and outer retinal thickness was thinner in the foveal (p < 0.001) and perifoveal zone (p < 0.001). Retinal thickness was also significantly greater in males than females (p < 0.001) and showed a statistically significant positive association with age (p = 0.01).ConclusionThere are significant differences in macular retinal thickness between Aboriginal and/or Torres Strait Islander children and non-Indigenous children, which has implications for interpreting optical coherence tomography data and may relate to risk of macula disease in this population.

Visual function correlates with foveal slope, retinal and choroidal thickness on optical coherence tomography in type 2 Macular Telangiectasia

ABSTRACT Purpose To correlate the foveal slope angle (FSA), retinal (RT) and choroidal (CT) thickness measured using optical coherence tomography (OCT) across different stages of type 2 macular telangiectasia (MacTel) with best-corrected visual acuity (BCVA). Methods A cross-sectional study of 182 gradable quality macular OCT images of 49 patients with type 2 MacTel was carried out. RT was measured on the Spectralis OCT machine from ETDRS (Early Treatment Diabetic Retinopathy Study) retinal thickness map and later used for calculating FSA. RT and CT were measured at 500-µm intervals up to 1500 µm from foveal centre manually. The change in RT, CT and FSA across five stages of type 2 MacTel was assessed. The measurements were correlated with BCVA. Results The FSA, RT and CT were calculated quadrantwise and stagewise. The FSA and RT showed a significant negative correlation with disease stage and BCVA (p < .05) in all four quadrants. No significant correlation was noted between CT and BCVA. Multivariate linear regression analysis identified the temporal FSA to correlate best with BCVA. Conclusion BCVA correlates best with temporal FSA. Smaller the temporal FSA, poorer the vision.

Identification of Sex and Age from Macular Optical Coherence Tomography and Feature Analysis Using Deep Learning

To develop deep learning models for identification of sex and age from macular optical coherence tomography (OCT) and to analyze the features for differentiation of sex and age. Algorithm development using database of macular OCT. We reviewed 6147 sets of macular OCT images from the healthy eyes of 3134 individuals from a single eye center in Taiwan. Deep learning-based algorithms were used to develop models for the identification of sex and age, and 10-fold cross-validation was applied. Gradient-weighted class activation mapping was used for feature analysis. The accuracy for sex prediction using deep learning from macular OCT was 85.6% ± 2.1% compared with accuracy of 61.9% using macular thickness and 61.4% ± 4.0% using deep learning from infrared fundus photography (P < .001 for both). The mean absolute error for age prediction using deep learning from macular OCT was 5.78 ± 0.29 years. A thorough analysis of the prediction accuracy and the gradient-weighted class activation mapping showed that the cross-sectional foveal contour lead to a better sex distinction than macular thickness or fundus photography, and the age-related characteristics of macula were on the whole layers of retina rather than the choroid. Sex and age could be identified from macular OCT using deep learning with good accuracy. The main sexual difference of macula lies in the foveal contour, and the whole layers of retina differ with aging. These novel findings provide useful information for further investigation in the pathogenesis of sex- and age-related macular structural diseases.

Macular Optical Coherence Tomography Imaging in Glaucoma.

The advent of spectral-domain optical coherence tomography has played a transformative role in posterior segment imaging of the eye. Traditionally, images of the optic nerve head and the peripapillary area have been used to evaluate the structural changes associated with glaucoma. Recently, there is growing evidence in the literature supporting the use of macular spectral-domain optical coherence tomography as a complementary tool for clinical evaluation and research purposes in glaucoma. Containing more than 50% of retinal ganglion cells in a multilayered pattern, macula is shown to be affected even at the earliest stages of glaucomatous structural damage. Risk assessment for glaucoma progression, earlier detection of glaucomatous structural damage, monitoring of glaucoma especially in advanced cases, and glaucoma evaluation in certain ocular conditions including eyes with high myopia, positive history of disc hemorrhage, and certain optic disc phenotypes are specific domains where macular imaging yields complementary information compared to optic nerve head and peripapillary evaluation using optical coherence tomography. Moreover, the development of artificial intelligence models in data analysis has enabled a tremendous opportunity to create an integrated representation of structural and functional alterations observed in glaucoma. In this study, we aimed at providing a brief review of the main clinical applications and future potential utility of macular spectral-domain optical coherence tomography in glaucoma.

An Optical Coherence Tomography-Based Deep Learning Algorithm for Visual Acuity Prediction of Highly Myopic Eyes After Cataract Surgery.

BackgroundDue to complicated and variable fundus status of highly myopic eyes, their visual benefit from cataract surgery remains hard to be determined preoperatively. We therefore aimed to develop an optical coherence tomography (OCT)-based deep learning algorithms to predict the postoperative visual acuity of highly myopic eyes after cataract surgery.Materials and MethodsThe internal dataset consisted of 1,415 highly myopic eyes having cataract surgeries in our hospital. Another external dataset consisted of 161 highly myopic eyes from Heping Eye Hospital. Preoperative macular OCT images were set as the only feature. The best corrected visual acuity (BCVA) at 4 weeks after surgery was set as the ground truth. Five different deep learning algorithms, namely ResNet-18, ResNet-34, ResNet-50, ResNet-101, and Inception-v3, were used to develop the model aiming at predicting the postoperative BCVA, and an ensemble learning was further developed. The model was further evaluated in the internal and external test datasets.ResultsThe ensemble learning showed the lowest mean absolute error (MAE) of 0.1566 logMAR and the lowest root mean square error (RMSE) of 0.2433 logMAR in the validation dataset. Promising outcomes in the internal and external test datasets were revealed with MAEs of 0.1524 and 0.1602 logMAR and RMSEs of 0.2612 and 0.2020 logMAR, respectively. Considerable sensitivity and precision were achieved in the BCVA < 0.30 logMAR group, with 90.32 and 75.34% in the internal test dataset and 81.75 and 89.60% in the external test dataset, respectively. The percentages of the prediction errors within ± 0.30 logMAR were 89.01% in the internal and 88.82% in the external test dataset.ConclusionPromising prediction outcomes of postoperative BCVA were achieved by the novel OCT-trained deep learning model, which will be helpful for the surgical planning of highly myopic cataract patients.

Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models.

PurposeDevelop a hierarchical longitudinal regression model for estimating local rates of change of macular ganglion cell complex (GCC) measurements with optical coherence tomography (OCT).MethodsWe enrolled 112 eyes with four or more macular OCT images and ≥2 years of follow-up. GCC thickness measurements within central 6 × 6 superpixels were extracted from macular volume scans. We fit data from each superpixel separately with several hierarchical Bayesian random-effects models. Models were compared with the Watanabe–Akaike information criterion. For our preferred model, we estimated population and individual slopes and intercepts (baseline thickness) and their correlation.ResultsMean (SD) follow-up time and median (interquartile range) baseline 24-2 visual field mean deviation were 3.6 (0.4) years and −6.8 (−12.2 to −4.3) dB, respectively. The random intercepts and slopes model with random residual variance was the preferred model. While more individual and population negative slopes were observed in the paracentral and papillomacular superpixels, superpixels in the superotemporal and inferior regions displayed the highest correlation between baseline thickness and rates of change (r = –0.43 to –0.50 for the top five correlations).ConclusionsA Bayesian linear hierarchical model with random intercepts/slopes and random variances is an optimal initial model for estimating GCC slopes at population and individual levels. This novel model is an efficient method for estimating macular rates of change and probability of glaucoma progression locally.Translational RelevanceThe proposed Bayesian hierarchical model can be applied to various macular outcomes from different OCT devices and to superpixels of variable sizes to estimate local rates of change and progression probability.

Comparison of the Humphrey Field Analyzer and Photopic Negative Response of Focal Macular Electroretinograms in the Evaluation of the Relationship Between Macula Structure and Function.

Purpose: To investigate the association between macular inner retinal layer thickness and macula visual field (VF) mean deviation as measured by the Humphrey Field Analyzer (HFA) or macular function as measured by focal macular electroretinograms (ERGs) in patients with glaucoma.Methods: The participants in this cross-sectional study were 71 patients with glaucoma and 10 healthy controls. Macular inner retinal layer thickness and function were measured in all participants using optical coherence tomography (OCT) and HFA or focal macular ERGs, respectively. Macular OCT images were segmented into the macular retinal nerve fiber layer (mRNFL), macular ganglion cell layer/inner plexiform layer (GCL/IPL), and ganglion cell complex (GCC). Spearman correlation analysis was used to assess the relationship between macular inner retinal layer thickness and function.Results: Focal macular ERGs were composed of a negative wave (N1), a positive wave (P1), and a slow negative wave (N2). The N2 response density was significantly reduced in eyes with glaucoma, and was significantly associated with the thickness of the mRNFL (R = 0.317), GCL/IPL (R = 0.372), or GCC (R = 0.367). The observed structure–function relationship was also significantly correlated with the HFA VF mean deviation for each thickness [mRNFL (R = 0.728), GCL/IPL (R = 0.603), or GCC (R = 0.754)].Conclusions: Although a significant correlation was found between the N2 response density and the thickness of the macular inner layer, the observed structure–function relationship with the mean deviation of the HFA VF was higher than that of the N2 response density.