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PurposeTo compare convolutional neural network (CNN) analysis of en face vessel density images to gradient boosting classifier (GBC) analysis of instrument-provided, feature-based optical coherence tomography angiography (OCTA) vessel density measurements and OCT retinal nerve fiber layer (RNFL) thickness measurements for classifying healthy and glaucomatous eyes.DesignComparison of diagnostic approaches.MethodsA total of 130 eyes of 80 healthy individuals and 275 eyes of 185 glaucoma patients with optic nerve head (ONH) OCTA and OCT imaging were included. Classification performance of a VGG16 CNN trained and tested on entire en face 4.5×4.5-mm radial peripapillary capillary OCTA ONH images was compared to the performance of separate GBC models trained and tested on standard OCTA and OCT measurements. Five-fold cross-validation was used to test predictions for CNNs and GBCs. Areas under the precision recall curves (AUPRC) were calculated to control for training/test set size imbalance and were compared.ResultsAdjusted AUPRCs for GBC models were 0.89 (95% CI=0.82, 0.92) for whole image vessel density GBC, 0.89 (0.83, 0.92) for whole image capillary density GBC, 0.91 (0.88, 0.93) for combined whole image vessel and whole image capillary density GBC, and 0.93 (0.91, 095) for RNFL thickness GBC. The adjusted AUPRC using CNN analysis of en face vessel density images was 0.97 (0.95, 0.99) resulting in significantly improved classification compared to GBC OCTA-based results and GBC OCT-based results (P ≤ 0.01 for all comparisons).ConclusionDeep learning en face image analysis improves on feature-based GBC models for classifying healthy and glaucoma eyes.

Uveal melanoma, the most common intraocular primary cancer in adults, is characterized by striking variability in metastatic tendencies. BAP1 deletion in the primary tumor is associated with uveal melanoma metastasis, but it cannot always be resolved by bulk DNA sequencing of heterogeneous tumors. Here, we show that assessment of BAP1 methylation is an accurate and readily clinically actionable assay to accurately identify high-risk uveal melanoma patients.

Purpose To compare gradient-boosting classifier (GBC) analysis of optical coherence tomography angiography (OCTA)-measured vessel density (VD) and OCT-measured tissue thickness to standard OCTA VD and OCT thickness parameters for classifying healthy eyes and eyes with early to moderate glaucoma. Design Comparison of diagnostic tools. Methods A total of 180 healthy eyes and 193 glaucomatous eyes with OCTA and OCT imaging of the macula and optic nerve head (ONH) were studied. Four GBCs were evaluated that combined 1) all macula VD and thickness measurements (Macula GBC), 2) all ONH VD and thickness measurements (ONH GBC), 3) all VD measurements from the macula and ONH (vessel density GBC), and 4) all thickness measurements from the macula and ONH (thickness GBC). ROC curve (AUROC) analyses compared the diagnostic accuracy of GBCs to that of standard instrument-provided parameters. A fifth GBC that combined all parameters (full GBC) also was investigated. Results GBCs had better diagnostic accuracy than standard OCTA and OCT parameters with AUROCs ranging from 0.90 to 0.93 and 0.64 to 0.91, respectively. The full GBC (AUROC = 0.93) performed significantly better than the ONH GBC (AUROC = 0.91; P = .036) and the vessel density GBC (AUROC = 0.90; P = .010). All other GBCs performed similarly. The mean relative influence of each parameter included in the full GBC identified a combination of macular thickness and ONH VD measurements as the greatest contributors. Conclusions GBCs that combine OCTA and OCT macula and ONH measurements can improve diagnostic accuracy for glaucoma detection compared to most but not all instrument provided parameters.

Purpose To assess the accuracy of deep learning models to predict glaucoma development from fundus photographs several years before disease onset. Design Algorithm development for predicting glaucoma using data from a prospective longitudinal study. Participants A total of 66 721 fundus photographs from 3272 eyes of 1636 subjects who participated in the Ocular Hypertension Treatment Study (OHTS) were included. Main Outcome Measures Accuracy and area under the curve (AUC). Methods Fundus photographs and visual fields were carefully examined by 2 independent readers from the optic disc and visual field reading centers of the OHTS. When an abnormality was detected by the readers, the subject was recalled for retesting to confirm the abnormality and for further confirmation by an end point committee. By using 66 721 fundus photographs, deep learning models were trained and validated using 85% of the fundus photographs and further retested (validated) on the remaining (held-out) 15% of the fundus photographs. Results The AUC of the deep learning model in predicting glaucoma development 4 to 7 years before disease onset was 0.77 (95% confidence interval [CI], 0.75–0.79). The accuracy of the model in predicting glaucoma development approximately 1 to 3 years before disease onset was 0.88 (95% CI, 0.86–0.91). The accuracy of the model in detecting glaucoma after onset was 0.95 (95% CI, 0.94–0.96). Conclusions Deep learning models can predict glaucoma development before disease onset with reasonable accuracy. Eyes with visual field abnormality but not glaucomatous optic neuropathy had a higher tendency to be missed by deep learning algorithms.

To investigate the diagnostic accuracy of deep learning (DL) algorithms to detect primary open-angle glaucoma (POAG) trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS). 66,715 photographs from 3,272 eyes were used to train and test a ResNet-50 model to detect the OHTS Endpoint Committee POAG determination based on optic disc (n=287 eyes, 3,502 photographs) and/or visual field (n=198 eyes, 2,300 visual fields) changes. OHTS training, validation and testing sets were randomly determined using an 85-5-10 percentage split by subject. Three independent test sets were used to estimate the generalizability of the model: UCSD Diagnostic Innovations in Glaucoma Study (DIGS, USA), ACRIMA (Spain) and Large-scale Attention-based Glaucoma (LAG, China). The DL model achieved an AUROC (95% CI) of 0.88 (0.82, 0.92) for the overall OHTS POAG endpoint. For the OHTS endpoints based on optic disc changes or visual field changes, AUROCs were 0.91 (0.88, 0.94) and 0.86 (0.76, 0.93), respectively. False-positive rates (at 90% specificity) were higher in photographs of eyes that later developed POAG by disc or visual field (19.1%) compared to eyes that did not develop POAG (7.3%) during their OHTS follow-up. The diagnostic accuracy of the DL model developed on the OHTS optic disc endpoint applied to 3 independent datasets was lower with AUROC ranging from 0.74 to 0.79. High diagnostic accuracy of the current model suggests that DL can be used to automate the determination of POAG for clinical trials and management. In addition, the higher false-positive rate in early photographs of eyes that later developed POAG suggests that DL models detected POAG in some eyes earlier than the OHTS Endpoint Committee.

Author(s): Bowd, Christopher; Belghith, Akram; Christopher, Mark; Goldbaum, Michael H; Fazio, Massimo A; Girkin, Christopher A; Liebmann, Jeffrey M; de Moraes, Carlos Gustavo; Weinreb, Robert N; Zangwill, Linda M | Abstract: PurposeTo compare change over time in eye-specific optical coherence tomography (OCT) retinal nerve fiber layer (RNFL)-based region-of-interest (ROI) maps developed using unsupervised deep-learning auto-encoders (DL-AE) to circumpapillary RNFL (cpRNFL) thickness for the detection of glaucomatous progression.MethodsForty-four progressing glaucoma eyes (by stereophotograph assessment), 189 nonprogressing glaucoma eyes (by stereophotograph assessment), and 109 healthy eyes were followed for ≥3 years with ≥4 visits using OCT. The San Diego Automated Layer Segmentation Algorithm was used to automatically segment the RNFL layer from raw three-dimensional OCT images. For each longitudinal series, DL-AEs were used to generate individualized eye-based ROI maps by identifying RNFL regions of likely progression and no change. Sensitivities and specificities for detecting change over time and rates of change over time were compared for the DL-AE ROI and global cpRNFL thickness measurements derived from a 2.22-mm to 3.45-mm annulus centered on the optic disc.ResultsThe sensitivity for detecting change in progressing eyes was greater for DL-AE ROIs than for global cpRNFL annulus thicknesses (0.90 and 0.63, respectively). The specificity for detecting not likely progression in nonprogressing eyes was similar (0.92 and 0.93, respectively). The mean rates of change in DL-AE ROI were significantly faster than for cpRNFL annulus thickness in progressing eyes (-1.28 µm/y vs. -0.83 µm/y) and nonprogressing eyes (-1.03 µm/y vs. -0.78 µm/y).ConclusionsEye-specific ROIs identified using DL-AE analysis of OCT images show promise for improving assessment of glaucomatous progression.Translational relevanceThe detection and monitoring of structural glaucomatous progression can be improved by considering eye-specific regions of likely progression identified using deep learning.

A 68-year-old woman with a recent history of blurring in the left eye had undergone mastectomy for breast cancer 20 years ago. A series of bone metastases started 5 years after her diagnosis. Examination of the optic nerve head of the left eye revealed an isolated peripapillary mass. Indocyanine green angiography displayed vessels within the mass, and fluorescein angiography demonstrated hyperfluorescence of the mass from vascular leakage plus lobular spots of blocked fluorescence. B-scan ultrasound revealed a hyperechoic-elevated nodular mass on the optic disc. Spectral-domain optical coherence tomography displayed a mass of spherules. Magnetic resonance imaging of the brain demonstrated metastatic tumors. She was diagnosed with an optic disk metastasis from her breast carcinoma.

G protein mutations are common in uveal melanomas, and the vast majority target amino acid residue Q209 in either GNAQ or GNA11. The GNAQ R183Q mutation is found in a small fraction of uveal melanomas. We report a patient with an unusual presentation of uveal melanoma arising at an early age in the setting of congenital skin and ocular surface melanosis. A 34-year-old Hispanic female with congenital bilateral nevus of Ota and ocular surface melanosis presented with progressive loss of visual acuity and was found to have a juxtapapillary uveal melanoma. She was treated with brachytherapy, but the tumor relapsed. She underwent enucleation that revealed mixed spindle and epithelioid uveal melanoma cells with no extraocular or lymphovascular spread. Next-generation sequencing performed on DNA isolated from the enucleation specimen identified a GNAQ R183Q mutation and a PMS1 truncation mutation. Cytogenetic profiling revealed no monosomy 3. These findings raise the possibility that uveal melanomas bearing G protein R183 mutations may have distinct clinicopathologic profiles compared to those with Q209 mutations. Furthermore, this is the first reported case of a mutation in the mismatch repair gene PMS1 associated with uveal melanoma.

We develop an Artificial Intelligence (AI) framework for glaucoma precognition from baseline confocal scanning laser ophthalmoscopy imaging data, using a convolutional neural network (CNN) model. The proposed framework extracts ‘deep features’ from convolutional layers of the CNN model, which are used as input to the ensemble learning classifier in order to identify patients that will likely convert to glaucoma after few years. The prediction model achieved area under the receiver operating characteristic curve (AUC) of 0.83 using the data from baseline visit. The model predicted the onset of glaucoma more accurately than known glaucoma risk factors, Glaucoma Probability Score (GPS) and Moorfields Regression Analysis (MRA) parameters of the Heidelberg Retinal Tomograph (HRT) software. The proposed AI construct provides a highly specific and sensitive model that can predict the onset of glaucoma from baseline HRT parameters and has the potential to provide clinicians valuable information regarding the onset of glaucoma.

IMPORTANCE: Automated deep learning (DL) analyses of fundus photographs potentially can reduce the cost and improve the efficiency of reading center assessment of end points in clinical trials. OBJECTIVE: To investigate the diagnostic accuracy of DL algorithms trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS) to detect primary open-angle glaucoma (POAG). DESIGN, SETTING, AND PARTICIPANTS: In this diagnostic study, 1636 OHTS participants from 22 sites with a mean (range) follow-up of 10.7 (0-14.3) years. A total of 66 715 photographs from 3272 eyes were used to train and test a ResNet-50 model to detect the OHTS Endpoint Committee POAG determination based on optic disc (287 eyes, 3502 photographs) and/or visual field (198 eyes, 2300 visual fields) changes. Three independent test sets were used to evaluate the generalizability of the model. MAIN OUTCOMES AND MEASURES: Areas under the receiver operating characteristic curve (AUROC) and sensitivities at fixed specificities were calculated to compare model performance. Evaluation of false-positive rates was used to determine whether the DL model detected POAG before the OHTS Endpoint Committee POAG determination. RESULTS: A total of 1147 participants were included in the training set (661 [57.6%] female; mean age, 57.2 years; 95% CI, 56.6-57.8), 167 in the validation set (97 [58.1%] female; mean age, 57.1 years; 95% CI, 55.6-58.7), and 322 in the test set (173 [53.7%] female; mean age, 57.2 years; 95% CI, 56.1-58.2). The DL model achieved an AUROC of 0.88 (95% CI, 0.82-0.92) for the OHTS Endpoint Committee determination of optic disc or VF changes. For the OHTS end points based on optic disc changes or visual field changes, AUROCs were 0.91 (95% CI, 0.88-0.94) and 0.86 (95% CI, 0.76-0.93), respectively. False-positive rates (at 90% specificity) were higher in photographs of eyes that later developed POAG by disc or visual field (27.5% [56 of 204]) compared with eyes that did not develop POAG (11.4% [50 of 440]) during follow-up. The diagnostic accuracy of the DL model developed on the optic disc end point applied to 3 independent data sets was lower, with AUROCs ranging from 0.74 (95% CI, 0.70-0.77) to 0.79 (95% CI, 0.78-0.81). CONCLUSIONS AND RELEVANCE: The model’s high diagnostic accuracy using OHTS photographs suggests that DL has the potential to standardize and automate POAG determination for clinical trials and management. In addition, the higher false-positive rate in early photographs of eyes that later developed POAG suggests that DL models detected POAG in some eyes earlier than the OHTS Endpoint Committee, reflecting the OHTS design that emphasized a high specificity for POAG determination by requiring a clinically significant change from baseline.

Deep archetypal analysis (DAA) has recently been proposed as an unsupervised approach for discovering latent structures in data. However, while a few approaches have used classical archetypal analysis (AA), DAA has not been incorporated in medical image analysis as yet. The purpose of this study is to develop a precognition framework to identify preclinical signs of glaucomatous vision loss using convex representations derived from DAA. We first develop an AA structure and a novel DAA framework to recognize hidden patterns of visual functional loss, and then project visual field data over the identified patterns to obtain a representation for glaucoma precognition several years prior to disease onset. We then develop a glaucoma classification framework using class-balanced bagging with neural networks to address the class imbalance problem. In contrast to other classification approaches, DAA, applied to a unique prospective longitudinal dataset with approximately eight years of visual field tests from normal eyes that developed glaucoma, has allowed visualization of the early signs of glaucoma and development of a construct for glaucoma precognition. Our findings suggest that our proposed glaucoma precognition approach could significantly advance state-of-the-art glaucoma prediction.

Author(s): Christopher, Mark; Nakahara, Kenichi; Bowd, Christopher; Proudfoot, James A; Belghith, Akram; Goldbaum, Michael H; Rezapour, Jasmin; Weinreb, Robert N; Fazio, Massimo A; Girkin, Christopher A; Liebmann, Jeffrey M; De Moraes, Gustavo; Murata, Hiroshi; Tokumo, Kana; Shibata, Naoto; Fujino, Yuri; Matsuura, Masato; Kiuchi, Yoshiaki; Tanito, Masaki; Asaoka, Ryo; Zangwill, Linda M | Abstract: PurposeTo compare performance of independently developed deep learning algorithms for detecting glaucoma from fundus photographs and to evaluate strategies for incorporating new data into models.MethodsTwo fundus photograph datasets from the Diagnostic Innovations in Glaucoma Study/African Descent and Glaucoma Evaluation Study and Matsue Red Cross Hospital were used to independently develop deep learning algorithms for detection of glaucoma at the University of California, San Diego, and the University of Tokyo. We compared three versions of the University of California, San Diego, and University of Tokyo models: original (no retraining), sequential (retraining only on new data), and combined (training on combined data). Independent datasets were used to test the algorithms.ResultsThe original University of California, San Diego and University of Tokyo models performed similarly (area under the receiver operating characteristic curve = 0.96 and 0.97, respectively) for detection of glaucoma in the Matsue Red Cross Hospital dataset, but not the Diagnostic Innovations in Glaucoma Study/African Descent and Glaucoma Evaluation Study data (0.79 and 0.92; P l .001), respectively. Model performance was higher when classifying moderate-to-severe compared with mild disease (area under the receiver operating characteristic curve = 0.98 and 0.91; P l .001), respectively. Models trained with the combined strategy generally had better performance across all datasets than the original strategy.ConclusionsDeep learning glaucoma detection can achieve high accuracy across diverse datasets with appropriate training strategies. Because model performance was influenced by the severity of disease, labeling, training strategies, and population characteristics, reporting accuracy stratified by relevant covariates is important for cross study comparisons.Translational relevanceHigh sensitivity and specificity of deep learning algorithms for moderate-to-severe glaucoma across diverse populations suggest a role for artificial intelligence in the detection of glaucoma in primary care.

Purpose Uveal melanomas are associated with characteristic genetic changes. Germline mutations in mismatch repair (MMR) genes and microsatellite instability have been implicated in the development of numerous malignant neoplasms such as colon and ovarian cancers. The frequency of MMR defects in uveal melanomas has yet to be determined. Methods Here, we analyzed the frequency of MMR gene mutations in uveal melanoma specimens from the University of California, San Diego (UCSD), The Cancer Genome Atlas (TGCA), and the Catalogue of Somatic Mutations in Cancer (COSMIC). Results We identified only two mutations in a MMR gene: one premature stop codon in the PMS gene within the UCSD cohort (0.5% frequency) and one in-frame deletion in MSH3 within the COSMIC database (0.8% frequency). We report copy number variation of MLH1 in monosomy 3 and show decreased mRNA expression of MLH1 in uveal melanoma specimens with monosomy 3. Expression levels of MLH1 were not found to correlate with the observed number of total mutations. Conclusion Overall, we show that mutations in MMR genes in uveal melanoma specimens are exceedingly rare, and although one copy of MLH1 is lost in monosomy 3, it does not seem to have pathologic consequences in uveal melanoma pathogenesis.

Goal: The purpose of this study was to identify clinically relevant patterns of glaucomatous vision loss through convex representation to predict glaucoma several years prior to disease onset. Methods: We developed a deep archetypal analysis to identify patterns of glaucomatous vision loss, and then projected visual fields over the identified patterns. Projections provided a representation that was more accurate in detecting glaucomatous vision loss, thus, more appropriate for recognizing preclinical signs of glaucoma prior to disease development. To overcome the class imbalance in prediction, we implemented a class-balanced bagging with neural networks. Results: Using original visual field as features of the class-balanced bagging classification provided an area under the receiver-operating characteristic curve (AUC) of 0.55 for predicting glaucoma approximately four years prior to disease development. Using convex representation of the visual fields as input features provided an AUC of 0.61 while using deep convex representation as input features improved the AUC to 0.71. Relevance vector machine (RVM) achieved an AUC of 0.64. Conclusion: Deep archetypal analysis representation of visual functional features with balanced bagging classification could serve as an automated tool for predicting glaucoma. Significance: Glaucoma is the second leading cause of worldwide blindness. Most people with glaucoma have no early symptoms or pain, delaying diagnosis in many patients until they reach late irreversible vision loss stages. In fact, about 50% of people with glaucoma are unaware they have the disease. Deep archetypal analysis models may impact clinical practice in effectively identifying at-risk glaucoma patients well prior to disease development., Glaucoma is the second leading cause of worldwide blindness. Most people with glaucoma have no early symptoms or pain, delaying diagnosis in many patients until they have irreversible vision loss. In fact, about 50% of people with glaucoma are unaware they have the disease. Deep archetypal analysis models may impact clinical practice in effectively identifying at-risk glaucoma patients well prior to disease development.

PURPOSE: To compare the characteristics of the retinal and choroidal lesions including choroidal nevus, choroidal melanoma and congenital hypertrophy of the retina pigment epithelium using conventional color fundus photography (CFP) and multicolor imaging (MCI). METHODS: The paired images of patients with retinal or choroidal lesions were assessed for the visibility of lesion’s border, halo and drusen using a grading scale (0–2). The area of the lesion was measured on both imaging modalities. The same grading was also done on the individual color channels of MCI for a further evaluation. RESULTS: Thirty-three eyes of 33 patients were included. There were no significant differences in the mean border, drusen and halo visibility scores between the two imaging modalities (p=0.12, p=0.70, p=0.35). However, the mean area of the lesion was significantly smaller on MCI than that on CFP (14.9±3.3 versus 18.7±3.4 mm(2), p=0.01). CONCLUSION: The appearance of choroidal and/ or retinal lesions on MCI may be different than that on CFP. Though MCI can provide similar information with CFP for the features of retinal and/ or choroidal lesions including border, halo and drusen; the infrared light reflection on MCI underestimates the extent of the choroidal lesion by 33%.

PurposeTo assess the accuracy of deep learning models to predict glaucoma development from fundus photographs several years prior to disease onset.DesignA deep learning model for prediction of glaucomatous optic neuropathy or visual field abnormality from color fundus photographs.ParticipantsWe retrospectively included 66,721 fundus photographs from 3,272 eyes of 1,636 subjects to develop deep leaning models.MethodFundus photographs and visual fields were carefully examined by two independent readers from the optic disc and visual field reading centers of the ocular hypertension treatment study (OHTS). When an abnormality was detected by the readers, subject was recalled for re-testing to confirm the abnormality and further confirmation by an endpoint committee. Using OHTS data, deep learning models were trained and tested using 85% of the fundus photographs and further validated (re-tested) on the remaining (held-out) 15% of the fundus photographs.Main Outcome MeasuresAccuracy and area under the receiver-operating characteristic curve (AUC).ResultsThe AUC of the deep learning model in predicting glaucoma development 4-7 years prior to disease onset was 0.77 (95% confidence interval 0.75, 0.79). The accuracy of the model in predicting glaucoma development about 1-3 years prior to disease onset was 0.88 (0.86, 0.91). The accuracy of the model in detecting glaucoma after onset was 0.95 (0.94, 0.96).ConclusionsDeep learning models can predict glaucoma development prior to disease onset with reasonable accuracy. Eyes with visual field abnormality but not glaucomatous optic neuropathy had a higher tendency to be missed by deep learning algorithms.

PurposeTo develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SDOCT) optic nerve head images.DesignEvaluation of a diagnostic technology.ParticipantsA total of 9765 visual field (VF) SD OCT pairs collected from 1194 participants with and without GVFD (1909 eyes).MethodsDeep learning models were trained to use SD OCT retinal nerve fiber layer (RNFL) thickness maps, RNFL en face images, and confocal scanning laser ophthalmoscopy (CSLO) images to identify eyes with GVFD and predict quantitative VF mean deviation (MD), pattern standard deviation (PSD), and mean VF sectoral pattern deviation (PD) from SD OCT data.Main outcome measuresDeep learning models were compared with mean RNFL thickness for identifying GVFD using area under the curve (AUC), sensitivity, and specificity. For predicting MD, PSD, and mean sectoral PD, models were evaluated using R2 and mean absolute error (MAE).ResultsIn the independent test dataset, the deep learning models based on RNFL en face images achieved an AUC of 0.88 for identifying eyes with GVFD and 0.82 for detecting mild GVFD significantly (P < 0.001) better than using mean RNFL thickness measurements (AUC= 0.82 and 0.73, respectively). Deep learning models outperformed standard RNFL thickness measurements in predicting all quantitative VF metrics. In predicting MD, deep learning models based on RNFL en face images achieved an R2 of 0.70 and MAE of 2.5 decibels (dB) compared with 0.45 and 3.7 dB for RNFL thickness measurements. In predicting mean VF sectoral PD, deep learning models achieved high accuracy in the inferior nasal (R2= 0.60) and superior nasal (R2= 0.67) sectors, moderate accuracy in inferior (R2= 0.26) and superior (R2= 0.35) sectors, and lower accuracy in the central (R2=0.15) and temporal (R2= 0.12) sectors.ConclusionsDeep learning models had high accuracy in identifying eyes with GFVD and predicting theseverity of functional loss from SD OCT images. Accurately predicting the severity of GFVD from SD OCT imaging can help clinicians more effectively individualize the frequency of VF testing to the individual patient.

A hierarchical approach to learn from visual field data was adopted to identify glaucomatous visual field defect patterns and to detect glaucomatous progression. The analysis pipeline included three stages, namely, clustering, glaucoma boundary limit detection, and glaucoma progression detection testing. First, cross-sectional visual field tests collected from each subject were clustered using a mixture of Gaussians and model parameters were estimated using expectation maximization. The visual field clusters were further estimated to recognize glaucomatous visual field defect patterns by decomposing each cluster into several axes. The glaucoma visual field defect patterns along each axis then were identified. To derive a definition of progression, the longitudinal visual fields of stable glaucoma eyes on the abnormal cluster axes were projected and the slope was approximated using linear regression (LR) to determine the confidence limit of each axis. For glaucoma progression detection, the longitudinal visual fields of each eye on the abnormal cluster axes were projected and the slope was approximated by LR. Progression was assigned if the progression rate was greater than the boundary limit of the stable eyes; otherwise, stability was assumed. The proposed method was compared to a recently developed progression detection method and to clinically available glaucoma progression detection software. The clinical accuracy of the proposed pipeline was as good as or better than the currently available methods. © 1964-2012 IEEE.

PurposeTo determine whether there is an association between hepatic lipase (LIPC) and age-related macular degeneration (AMD) in two independent Caucasian cohorts.MethodsA discovery cohort of 1626 patients with advanced AMD and 859 normal controls and a replication cohort of 2159 cases and 1150 controls were genotyped for two single-nucleotide polymorphisms (SNPs) in the promoter region of LIPC. The associations between the SNPs and AMD were examined by χ(2) tests.ResultsIn the discovery cohort, rs493258 and rs10468017 were both associated with advanced AMD (P=9.63E-3 and P=0.048, respectively). The association was corroborated in the replication cohort (P=4.48E-03 for rs493258 and P=0.015 for rs10468017). Combined analysis resulted in even more significant associations (P=1.21E-04 for rs493258 and P=1.67E-03 for rs10468017).ConclusionThe LIPC promoter variants rs493258 and rs10468017 were associated with advanced AMD in two independent Caucasian populations, confirming that LIPC polymorphisms may be a genetic risk factor for AMD in the Caucasian population.

Tuberous sclerosis or tuberous sclerosis complex (TSC), one of the phakomatoses, is characterized by hamartomas of the heart, kidney, brain, skin and eyes. Ophthalmologic examinations are required in all cases of TSC. Retinal hamartomas are the most common ocular finding in tuberous sclerosis. The majority of hamartomas are non-progressive; however, lesions with subretinal fluid and progression have been reported. This paper details the genetics, clinical features and ocular findings of TSC and reviews potential therapeutic options for ophthalmic manifestations.

Author(s): Christopher, Mark; Belghith, Akram; Weinreb, Robert N; Bowd, Christopher; Goldbaum, Michael H; Saunders, Luke J; Medeiros, Felipe A; Zangwill, Linda M | Abstract: Purpose:To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT) images to identify novel, glaucoma-related structural features and improve detection of glaucoma and prediction of future glaucomatous progression. Methods:Wide-angle SS-OCT, OCT circumpapillary retinal nerve fiber layer (cpRNFL) circle scans spectral-domain (SD)-OCT, standard automated perimetry (SAP), and frequency doubling technology (FDT) visual field tests were completed every 3 months for 2 years from a cohort of 28 healthy participants (56 eyes) and 93 glaucoma participants (179 eyes). RNFL thickness maps were extracted from segmented SS-OCT images and an unsupervised machine learning approach based on principal component analysis (PCA) was used to identify novel structural features. Area under the receiver operating characteristic curve (AUC) was used to assess diagnostic accuracy of RNFL PCA for detecting glaucoma and progression compared to SAP, FDT, and cpRNFL measures. Results:The RNFL PCA features were significantly associated with mean deviation (MD) in both SAP (R2 = 0.49, P l 0.0001) and FDT visual field testing (R2 = 0.48, P l 0.0001), and with mean circumpapillary RNFL thickness (cpRNFLt) from SD-OCT (R2 = 0.58, P l 0.0001). The identified features outperformed each of these measures in detecting glaucoma with an AUC of 0.95 for RNFL PCA compared to an 0.90 for mean cpRNFLt (P = 0.09), 0.86 for SAP MD (P = 0.034), and 0.83 for FDT MD (P = 0.021). Accuracy in predicting progression was also significantly higher for RNFL PCA compared to SAP MD, FDT MD, and mean cpRNFLt (P = 0.046, P = 0.007, and P = 0.044, respectively). Conclusions:A computational approach can identify structural features that improve glaucoma detection and progression prediction.

The purposes of this study were to evaluate with spectral domain-optical coherence tomography the relationship between the retina and overlying silicone oil tamponade after macular hole surgery and to evaluate how this relationship changes with patient positioning.We studied a retrospective consecutive case series of 10 eyes from 9 patients who underwent macular hole surgery with silicone oil tamponade and subsequent spectral domain-optical coherence tomography scans. Four of the included eyes were also imaged with patients in face-down posture to determine whether the silicone-retina apposition changes with prone positioning. Finally, a single patient was also scanned in the lateral and supine positions.The posterior surface of the silicone oil bubble was well visualized in all 10 eyes. In the majority of eyes (7 of 10), the oil tamponade bridged the macular hole, creating a prefoveal fluid space, but in 3 eyes the silicone oil filled the macular hole and was seen in touch with the underlying foveal depression or retinal pigment epithelium. In 75% of eyes (3 of 4), the silicone oil-retinal approximation did not vary with face-down position. Supine positioning clearly floated the silicone tamponade anteriorly and off the retinal surface.Silicone oil tamponade can either bridge macular holes or, in a novel finding, fill the underlying foveal depression or macular hole space. Generally, the oil position is stable between face-forward and prone spectral-domain optical coherence tomography images, suggesting that either of these patient positions allows waterproofing of the underlying macular hole. Finally, our images confirm that supine positioning should be avoided postoperatively because it leads to loss of oil-retinal tamponade.

We propose a system that learns from the STARE (STructured Analysis of REtina) database and exploits the experience of ophthalmologists to assist in decision-making regarding the presence or absence of retinal diseases. The developed system automatically detects diseases given a description (a set of manifestations) of a retinal image. The manifestations in the retinal image are usually fed sequentially into the system where the manifestation dependences and order must be learned by the system. We apply naive Bayes classifier which is a simple case of Bayesian network to learn the conditional probabilities and to establish an approximate lookup table for sequential manifestation input. The system interacts with the ophthalmologist in determining the sequence of manifestations for inferring the correct disease. The overall performance of the system is found to be satisfactory and useful by ophthalmologists.

Purpose To report a case of likely subretinal foveal parasite, either ophthalmomyiasis from botfly infestation, Angiostrongylus or Gnathostoma. Methods Observational case report of a patient with decreased vision in the left eye since age 4, with examination showing a golden subretinal foveal lesion. She was monitored up to age 24. Results A young woman originally from Vietnam presented at age 11 with vision loss in the left eye since age 4 after a severe febrile illness. Fundus examination revealed a subretinal golden foveal lesion. History, angiography, and spectral domain optical coherence tomography revealed the possibility of parasitic infestation with an entrance site. Conclusion In patients with focal lesions of unknown etiology, careful history, exam, angiography and OCT may indicate a parasitic organism and the entrance site.

We describe an automated method to locate the optic nerve in images of the ocular fundus. Our method uses a novel algorithm we call fuzzy convergence to determine the origination of the blood vessel network. We evaluate our method using 31 images of healthy retinas and 50 images of diseased retinas, containing such diverse symptoms as tortuous vessels, choroidal neovascularization, and hemorrhages that completely obscure the actual nerve. On this difficult data set, our method achieved 89% correct detection. We also compare our method against three simpler methods, demonstrating the performance improvement. All our images and data are freely available for other researchers to use in evaluating related methods.

Melanoma of the iris is a rare condition compared to posterior ocular tumors and in this case report we present a 51-year-old female patient with diffuse iris melanoma. Traditional COMS (Collaborative Ocular Melanoma Study) plaques are used at our institution for radiation therapy, so a novel modification of the traditional plaque was required to allow better conformance with placement on the cornea. The usual silastic insert was machined to dimensions in compliance with the cornea, placed without incident, and treatment delivered with excellent patient tolerance of the modified plaque.

Refractive error (RE) is a complex, multifactorial disorder characterized by a mismatch between the optical power of the eye and its axial length that causes object images to be focused off the retina. The two major subtypes of RE are myopia (nearsightedness) and hyperopia (farsightedness), which represent opposite ends of the distribution of the quantitative measure of spherical refraction. We performed a fixed effects meta-analysis of genome-wide association results of myopia and hyperopia from 9 studies of European-derived populations: AREDS, KORA, FES, OGP-Talana, MESA, RSI, RSII, RSIII and ERF. One genome-wide significant region was observed for myopia, corresponding to a previously identified myopia locus on 8q12 (p = 1.25×10-8), which has been reported by Kiefer et al. as significantly associated with myopia age at onset and Verhoeven et al. as significantly associated to mean spherical-equivalent (MSE) refractive error. We observed two genome-wide significant associations with hyperopia. These regions overlapped with loci on 15q14 (minimum p value = 9.11×10-11) and 8q12 (minimum p value 1.82×10-11) previously reported for MSE and myopia age at onset. We also used an intermarker linkage- disequilibrium-based method for calculating the effective number of tests in targeted regional replication analyses. We analyzed myopia (which represents the closest phenotype in our data to the one used by Kiefer et al.) and showed replication of 10 additional loci associated with myopia previously reported by Kiefer et al. This is the first replication of these loci using myopia as the trait under analysis. "Replication-level" association was also seen between hyperopia and 12 of Kiefer et al.'s published loci. For the loci that show evidence of association to both myopia and hyperopia, the estimated effect of the risk alleles were in opposite directions for the two traits. This suggests that these loci are important contributors to variation of refractive error across the distribution.

Glaucoma is a potentially blinding optic neuropathy that results in a decrease in visual sensitivity. Visual field abnormalities (decreased visual sensitivity on psychophysical tests) are the primary means of glaucoma diagnosis. One form of visual field testing is Frequency Doubling Technology (FDT) that tests sensitivity at 52 points within the visual field. Like other psychophysical tests used in clinical practice, FDT results yield specific patterns of defect indicative of the disease. We used Gaussian Mixture Model with Expectation Maximization (GEM), (EM is used to estimate the model parameters) to automatically separate FDT data into clusters of normal and abnormal eyes. Principal component analysis (PCA) was used to decompose each cluster into different axes (patterns). FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal (i.e., glaucomatous) FDT results, recruited from a university-based, longitudinal, multi-center, clinical study on glaucoma. The GEM input was the 52-point FDT threshold sensitivities for all eyes. The optimal GEM model separated the FDT fields into 3 clusters. Cluster 1 contained 94% normal fields (94% specificity) and clusters 2 and 3 combined, contained 77% abnormal fields (77% sensitivity). For clusters 1, 2 and 3 the optimal number of PCA-identified axes were 2, 2 and 5, respectively. GEM with PCA successfully separated FDT fields from healthy and glaucoma eyes and identified familiar glaucomatous patterns of loss.

Purpose: The variational Bayesian independent component analysis-mixture model (VIM), an unsupervised machine-learning classifier, was used to automatically separate Matrix Frequency Doubling Technology (FDT) perimetry data into clusters of healthy and glaucomatous eyes, and to identify axes representing statistically independent patterns of defect in the glaucoma clusters. Methods: FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal FDT results from the UCSD-based Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES). For all eyes, VIM input was 52 threshold test points from the 24-2 test pattern, plus age. Results: FDT mean deviation was -1.00 dB (S.D. = 2.80 dB) and -5.57 dB (S.D. = 5.09 dB) in FDT-normal eyes and FDT-abnormal eyes, respectively (p

Age-related macular degeneration (AMD) is a leading cause of visual impairment in aging populations in industrialized countries. Here we investigated whether the genotype of vascular endothelial growth factor A (VEGFA) gene is associated with response to anti-VEGF therapy. 223 eyes with neovascular AMD were treated with intravitreal anti-VEGF therapy. Responders were defined as patients who had an improvement in best corrected visual acuity (BCVA) of at least 5 letters or one line on the EDTRS visual acuity chart along with resolution of intraretinal or subretinal fluid over 12 months. Patients who did not meet the definition of responders were classified as poor-responders. The vision of responders (n = 148) improved while the vision of poor-responders (n = 75) worsened (P

Purpose We demonstrate foveal hypoplasia by optical coherence tomography (OCT). Design Observational case report. Methods Foveal hypoplasia was demonstrated in a 79-year-old man with decreased vision since childhood using fundus photography, fluorescein angiography, and OCT scans through four meridians of the central foveal. Results The OCT showed extension of all neurosensory retinal layers through the area in which the fovea would normally be located. Conclusion Optical coherence tomography can be used to demonstrate foveal hypoplasia.

PURPOSE. We evaluated Progression of Patterns (POP) for its ability to identify progression of glaucomatous visual field (VF) defects. METHODS. POP uses variational Bayesian independent component mixture model (VIM), a machine learning classifier (MLC) developed previously. VIM separated Swedish Interactive Thresholding Algorithm (SITA) VFs from a set of 2,085 normal and glaucomatous eyes into nine axes (VF patterns): seven glaucomatous. Stable glaucoma was simulated in a second set of 55 patient eyes with five VFs each, collected within four weeks. A third set of 628 eyes with 4,186 VFs (mean 6 SD of 6.7 6 1.7 VFs over 4.0 6 1.4 years) was tested for progression. Tested eyes were placed into suspect and glaucoma categories at baseline, based on VFs and disk stereoscopic photographs; a subset of eyes had stereophotographic evidence of progressive glaucomatous optic neuropathy (PGON). Each sequence of fields was projected along seven VIM glaucoma axes. Linear regression (LR) slopes generated from projections onto each axis yielded a degree of confidence (DOC) that there was progression. At 95% specificity, progression cutoffs were established for POP, visual field index (VFI), and mean deviation (MD). Guided progression analysis (GPA) was also compared. RESULTS. POP identified a statistically similar number of eyes (P > 0.05) as progressing compared with VFI, MD, and GPA in suspects (3.8%, 2.7%, 5.6%, and 2.9%, respectively), and more eyes than GPA (P ¼ 0.01) in glaucoma (16.0%, 15.3%, 12.0%, and 7.3%, respectively), and more eyes than GPA (P ¼ 0.05) in PGON eyes (26.3%, 23.7%, 27.6%, and 14.5%, respectively). CONCLUSIONS. POP, with its display of DOC of progression and its identification of progressing VF defect pattern, adds to the information available to the clinician for detecting VF progression. (Invest Ophthalmol Vis Sci. 2012;53:6557‐6567) DOI:10.1167/iovs.11-8363

Many clinical entities may be associated with the development of peripheral retinal neovascularization. In this paper, we review the mechanisms of normal and abnormal angiogenesis in the retina. Specific disease entities associated with peripheral proliferative retinopathies are discussed. These include vascular disease with ischemia, inflammatory diseases with possible ischemia and a variety of miscellaneous causes, including hereditary diseases and tumors. Basic principles for the clinical evaluation of patients with retinal neovascularization are described. Finally, the treatments for retinal neovascularization, including cryopexy and local and panretinal photocoagulation are reviewed, and techniques and possible mechanisms of the beneficial results of treatment are described

Objectives To evaluate the independent and joint effects of genetic factors and environmental variables on advanced forms of age-related macular degeneration (AMD), including geographic atrophy and choroidal neovascularization, and to develop a predictive model with genetic and environmental factors included. Methods Demographic information, including age at onset, smoking status, and body mass index, was collected for 1844 participants. Genotypes were evaluated for 8 variants in 5 genes related to AMD. Unconditional logistic regression analyses were performed to generate a risk predictive model. Results All genetic variants showed a strong association with AMD. Multivariate odds ratios were 3.52 (95% confidence interval, 2.08-5.94) for complement factor H, CFH rs1061170 CC, 4.21 (2.30-7.70) for CFH rs2274700 CC, 0.46 (0.27-0.80) for C2 rs9332739 CC/CG, 0.44 (0.30-0.66) for CFB rs641153 TT/CT, 10.99 (6.04-19.97) for HTRA1/LOC387715 rs10490924 TT, and 2.66 (1.43-4.96) for C3 rs2230199 GG. Smoking was independently associated with advanced AMD after controlling for age, sex, body mass index, and all genetic variants. Conclusion CFH confers more risk to the bilaterality of geographic atrophy, whereas HTRA1/LOC387715 contributes more to the bilaterality of choroidal neovascularization. C3 confers more risk for geographic atrophy than choroidal neovascularization. Risk models with combined genetic and environmental factors have notable discrimination power. Clinical Relevance Early detection and risk prediction of AMD could help to improve the prognosis of AMD and to reduce the outcome of blindness. Targeting high-risk individuals for surveillance and clinical interventions may help reduce disease burden.

Purpose To investigate whether combining optic disc topography and short-wavelength automated perimetry (SWAP) data improves the diagnostic accuracy of relevance vector machine (RVM) classifiers for detecting glaucomatous eyes compared with using each test alone. Methods One eye of 144 glaucoma patients and 68 healthy controls from the Diagnostic Innovations in Glaucoma Study were included. RVM were trained and tested with cross-validation on optimized (backward elimination) SWAP features (thresholds plus age; pattern deviation; and total deviation) and on Heidelberg retina tomograph II (HRT) optic disc topography features, independently and in combination. RVM performance was also compared with 2 HRT linear discriminant functions and to SWAP mean deviation and pattern standard deviation. Classifier performance was measured by the area under the receiver operating characteristic curves (AUROCs) generated for each feature set and by the sensitivities at set specificities of 75%, 90%, and 96%. Results RVM trained on combined HRT and SWAP thresholds plus age had significantly higher AUROC (0.93) than RVM trained on HRT (0.88) and SWAP (0.76) alone. AUROCs for the SWAP global indices (mean deviation: 0.68; pattern standard deviation: 0.72) offered no advantage over SWAP thresholds plus age, whereas the linear discriminant functions AUROCs were significantly lower than RVM trained on the combined SWAP and HRT feature set and on HRT alone feature set. Conclusions Training RVM on combined optimized HRT and SWAP data improved diagnostic accuracy compared with training on SWAP and HRT parameters alone. Future research may identify other combinations of tests and classifiers that can also improve diagnostic accuracy.

Aims: To compare the non-decanted (standard) 4 mg versus the decanted 20 mg intravitreal triamcinolone acetonide (IVTA) injections and to assess their effect on intraocular pressure (IOP). Methods: We retrospectively reviewed the records of 92 consecutive eyes, which received an intravitreal injection of either dose of triamcinolone acetonide, at a single retina centre. The change in IOP (elevation of at least 5 mm Hg from baseline or above 21 mm Hg) was analysed with a multivariate logistic analysis. The mean follow-up period in both groups was 27 weeks. A subgroup analysis comparing vitrectomised to non-vitrectomised eyes in both groups was also performed. Results: Of the 92 eyes, 46% (23 of 51) in the 4 mg group versus 30% (12 of 41) in the 20 mg group had an IOP >21 mm of Hg (p = 0.14) after a mean follow-up period of 27 weeks. The vitrectomised eyes (3 of 24) in the 20 mg group had a significantly lower rate of IVTA induced IOP elevation than the non-vitrectomised eyes (9 of 17) (p = 0.013). The IOP elevation occurred significantly earlier in the 4 mg group (vitrectomised eyes 27 (SD 43) days and non-vitrectomised eyes 61 (52) days) than in the 20 mg group (vitrectomised eyes 104 (56) days and non-vitrectomised eyes 119 (82) days), independent of the vitreous status (vitrectomised p = 0.05 and non-vitrectomised p = 0.04). The mean value of initial high IOP in the non-vitrectomised eyes was higher in the 4 mg group than in the corresponding 20 mg group (p = 0.048). Conclusion: Decanted 20 mg IVTA may not pose a significantly greater risk of IOP elevation than the 4 mg non-decanted IVTA.

A 50-year-old man presented with uncontrolled erythroclastic glaucoma and recurrent vitreous hemorrhage from a previously irradiated choroidal melanoma. Despite trabeculectomy, intraocular pressure became uncontrolled due to increasing bleeding from the melanoma. The vitreous hemorrhage became voluminous and could not be stopped with multiple vitrectomies with endolaser, transpupillary thermotherapy, and transscleral laser. Endoresection of the regressed tumor and its bleeding vasculature resulted in immediate cessation of the bleeding and control of the intraocular pressure.

Media opacification in eyes filled with silicone oil makes the evaluation of recurrent retinal detachment difficult. Ultrasonography through silicone oil is subject to significant imaging artifacts. We performed magnetic resonance imaging on six patients with unilateral intravitreal silicone oil to determine if the technique would detect detached retina and subretinal oil. All patients had undergone pars plana vitrectomy with silicone oil injection for proliferative vitreoretinopathy; five patients had encircling solid silicone scleral buckles. In five patients the media were clear, and ophthalmoscopic findings were correlated with magnetic resonance findings. Four patients had recurrence of inferior retinal detachment; magnetic resonance imaging demonstrated subretinal oil in three of these patients. One patient had a concentric, shallow, anterior retinal detachment; magnetic resonance scanning demonstrated a globular hyperintensity suggestive of subretinal oil. In the sixth patient, who had an opaque cornea, magnetic resonance imaging suggested that the retina was attached preoperatively; this was confirmed at subsequent surgery. A chemical shift artifact was helpful in defining the contour of retinal detachments and the presence of subretinal oil by outlining the silicone oil within the eye.

PURPOSE. Clustering by unsupervised learning with machine learning classifiers was shown to segment clusters of patterns in standard automated perimetry (SAP) for glaucoma in previous publications. In this study, unsupervised learning by independent component analysis decomposed SAPfield patterns into axes, and the information represented by these axes was evaluated. METHODS. SAP fields were used that were obtained with the Humphrey Visual Field Analyzer (Carl Zeiss Meditec, Dublin, CA) from 189 normal eyes and 156 eyes with glaucomatous optic neuropathy (GON) determined by masked review with stereoscopic optic disc photographs. The variational Bayesian independent component analysis mixture model (vB-ICA-mm) partitioned the SAP fields into the most informative number of clusters. Simultaneously, the model learned an optimal number of maximally independent axes for each cluster. RESULTS. The most informative number of clusters in the SAP set was two. vB-ICA-mm placed 68.6% of the eyes with GON in a cluster labeled G and 98.4% of the eyes with normal optic discs in a cluster labeled N. Cluster G optimally contained six axes. Post hoc analysis of patterns generated at 1S D and2 SD from the cluster G mean on the six axes revealed defects similar to those identified by experts as indicative of glaucoma. SAP fields associated with an axis showed increasing severity, as they were located farther in the positive direction from the cluster G mean. CONCLUSIONS. vB-ICA-mm represented the SAP fields with patterns that were meaningful for glaucoma experts. This process also captured severity in the patterns uncovered. These findings should validate vB-ICA-mm as a data-mining technique for new and unfamiliar complex tests. (Invest Ophthalmol Vis Sci. 2005;46:3676‐3683) DOI:10.1167/iovs.04-1167 T his study focuses on the problem faced by physicians in interpreting tests for the diagnosis and management of diseases. The discipline of glaucoma diagnosis is particularly rich with data-intense tests that are currently being applied or developed to determine the status of glaucoma in an eye. Diagnosing and staging glaucoma both necessitate the use of several tests, including structural and functional assessment of the optic nerve. One of these, visual field testing, has been used by experts for several decades as the prime indicator for both diagnosis of glaucoma and the detection of progression. This wide expertise can be used to evaluate the ability of machine learning classifiers to find information that can be valuable in clinical practice. The hypothesis proposed is that machine learning classifiers can learn useful information about visual field patterns. This information can be validated against the prevailing experience of experts. Such validation would give credence to the application of the same process to new and unfamiliar tests. Visual fields have been used to diagnose glaucoma since

Purpose. To determine whether a variational Bayesian independent component analysis mixture model (vB-ICA-mm), a form of unsupervised machine learning, can be used to identify and quantify areas of progression in standard automated perimetry fields. Methods. In an earlier study, it was shown that a model using vB-ICA-mm can separate normal fields from fields with six different patterns of visual field loss related to glaucomatous optic neuropathy (GON) along maximally independent axes. In the present study, an independent group of 191 patient eyes (66 with ocular hypertension (OHT), 12 with suspected glaucoma by field, 61 with suspected glaucoma by disc, and 52 with glaucoma) with five or more standard visual fields under observation for a mean of 6.24 ± 2.65 years and 8.11 ± 2.42 visual fields were evaluated with the vB-ICA-mm. In addition, eyes with progressive GON (PGON) were identified (n = 39). Each participant had a series of fields tested, with each field entered independently and placed along the axes of the previously developed model. This allowed change in one pattern of visual field defect (along one axis) to be assessed relative to results other areas of that same field (no change along other axes). Progression was based on a slope falling outside the 5th and the 95th percentile limits of all slopes, with at least two axes not showing such a deviation in a given individual’s series of fields. Fields were also scored using Advanced Glaucoma Intervention Study (AGIS) and the Early Manifest Glaucoma Treatment Trial (EMGT) criteria. Results. Thirty-two of 191 eyes progressed on vB-ICA-mm by this definition. Of the 32, 22 had field loss at baseline, 7 had only GON, 3 were OHTs and 12 were from the 39 eyes (31%) with PGON. The vB-ICA-mm identified a higher percentage of progressing eyes in each diagnostic category than did AGIS or and the EMGT. Conclusions. The vB-ICA-mm can quantitatively identify progression in eyes with glaucoma by evaluating change in one or more patterns of the visual field loss while other areas or patterns remain stable. This may enable each eye to contribute to the determination of whether change is caused by true progression or by variability.

PURPOSE. To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP). METHODS. Seventy-two eyes of 72 healthy control subjects (average age 64.3 8.8 years, visual field mean deviation 0.71 1.2 dB) and 92 eyes of 92 patients with glaucoma (average age 66.9 8.9 years, visual field mean deviation 5.32 4.0 dB) were imaged with SLP with variable corneal compensation (GDx VCC; Laser Diagnostic Technologies, San Diego, CA). RVM and SVM learning classifiers were trained and tested on SLP-determined RNFL thickness measurements from 14 standard parameters and 64 sectors (approximately 5.6° each) obtained in the circumpapillary area under the instrument-defined measurement ellipse (total 78 parameters). Tenfold cross-validation was used to train and test RVM and SVM classifiers on unique subsets of the full 164-eye data set and areas under the receiver operating characteristic (AUROC) curve for the classification of eyes in the test set were generated. AUROC curve results from RVM and SVM were compared to those for 14 SLP software-generated global and regional RNFL thickness parameters. Also reported was the AUROC curve for the GDx VCC software-generated nerve fiber indicator (NFI). RESULTS. The AUROC curves for RVM and SVM were 0.90 and 0.91, respectively, and increased to 0.93 and 0.94 when the training sets were optimized with sequential forward and backward selection (resulting in reduced dimensional data sets). AUROC curves for optimized RVM and SVM were significantly larger than those for all individual SLP parameters. The AUROC curve for the NFI was 0.87. CONCLUSIONS. Results from RVM and SVM trained on SLP RNFL thickness measurements are similar and provide accurate classification of glaucomatous and healthy eyes. RVM may be preferable to SVM, because it provides a Bayesian-derived probability of glaucoma as an output. These results suggest that these machine learning classifiers show good potential for glaucoma diagnosis. (Invest Ophthalmol Vis Sci. 2005;46: 1322‐1329) DOI:10.1167/iovs.04-1122

Objective The authors performed a study to determine the effectiveness and safety of silicone oil as a substitute for gas to fill the vitreous cavity to treat macular holes. Design Multicenter, nonrandomized, interventional trial. Participants Thirty-seven consecutive patients chose vitrectomy with silicone tamponade instead of gas to treat 40 eyes with stage-2 to stage-4 idiopathic age-related macular holes. Stage-2 holes constituted 40% of the holes, and stage-3 and stage-4 holes made up 60%. Intervention All eyes were treated with vitrectomy, manual detachment of the posterior vitreous face (not done for stage-4 holes), autologous serum instillation, and silicone fill of the vitreous cavity. After insertion of the oil, the patients resumed normal activity with no restriction of head or eye position except to avoid faceup position. The oil was removed after approximately 6 weeks. Main outcome measures The authors considered the seal of the macular hole and the preoperative and postoperative logarithm of the minimum angle of resolution (logMAR) visions the most significant measures for comparison to other studies. Results Eighty percent of all holes and 86% of holes not treated previously were sealed with a single silicone tamponade of the vitreous cavity. The logMAR value of visual acuity improved an average of 0.26 (2.6 lines) to 0.61 (20/81) for all eyes and 0.34 (3.4 lines) to 0.52 (20/66) when the macular hole sealed. Completeness of fill of the vitreous cavity with silicone affected seal of the macular hole. Three of eight eyes in which open holes developed after oil removal had less than 90% fill of the vitreous cavity by silicone. Sixty-nine percent of lenses increased opacity one grade or were removed after silicone tamponade. There were no significant adverse effects arising from silicone tamponade. Conclusions Silicone oil tamponade of macular holes is effective and safe. Silicone may be optimal for the treatment of macular holes in persons who must travel, who cannot maintain facedown positioning, or who have monocular vision. The most important factor in the successful closure of the macular hole was the completeness of fill of the vitreous cavity with silicone oil.

The goal of this study was to determine if glaucomatous progression in suspect eyes can be predicted from baseline confocal scanning laser ophthalmoscope (CSLO) and standard automated perimetry (SAP) measurements analyzed with relevance vector machine (RVM) classifiers.Two hundred sixty-four eyes of 193 participants were included. All eyes had normal SAP results at baseline with five or more SAP tests over time. Eyes were labeled progressed (n = 47) or stable (n = 217) during follow-up based on SAP Guided Progression Analysis or serial stereophotograph assessment. Baseline CSLO-measured topographic parameters (n = 117) and baseline total deviation values from the 24-2 SAP test-grid (n = 52) were selected from each eye. Ten-fold cross-validation was used to train and test RVMs using the CSLO and SAP features. Receiver operating characteristic (ROC) curve areas were calculated using full and optimized feature sets. ROC curve results from RVM analyses of CSLO, SAP, and CSLO and SAP combined were compared to CSLO and SAP global indices (Glaucoma Probability Score, mean deviation and pattern standard deviation).The areas under the ROC curves (AUROCs) for RVMs trained on optimized feature sets of CSLO parameters, SAP parameters, and CSLO and SAP parameters combined were 0.640, 0.762, and 0.805, respectively. AUROCs for CSLO Glaucoma Probability Score, SAP mean deviation (MD), and SAP pattern standard deviation (PSD) were 0.517, 0.513, and 0.620, respectively. No CSLO or SAP global indices discriminated between baseline measurements from progressed and stable eyes better than chance.In our sample, RVM analyses of baseline CSLO and SAP measurements could identify eyes that showed future glaucomatous progression with a higher accuracy than the CSLO and SAP global indices. (ClinicalTrials.gov numbers, NCT00221897, NCT00221923.).

INJECTION of an inert gas into the vitreous of the eye is commonly performed as a treatment for retinal detachment, either as an office procedure (pneumatic retinopexy) or as part of a surgical repair. 1,2 Nitrous oxide (N 2 O) has the potential to diffuse into closed gas-containing spaces, resulting in dramatic increases in pressure and/or volume. We report two cases of permanent postoperative blindness resulting from expansion of an intraocular gas bubble during administration of an N 2 O-containing anesthetic.

In the STARE project (structured analysis of the retina) we are developing a system that will automatically diagnose images of the ocular fundus, compare sequential images for change, and make clinically significant measurements of lesions and anatomical structures in the images. Ophthalmologists need to compare color images, fluorescein angiograms, indocyanine angiograms, and scanning laser ophthalmoscopy for onset of disease and changes in lesions. The images are made from fundus cameras of different manufacture and at different magnification. Consequently we designed our system to register images of different magnification or appearance automatically.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A system for comparing sequential fundus photographs and angiograms has been developed using a personal computer and commercially available image processing software. This process provides rapid results and a high degree of accuracy in image correlation, even when images come from different cameras with different magnifications. Though no concentrated effort was made to compare the superiority of this method to the manual technique, the value of the manual results is fully acknowledged, and this technique is offered as another method of achieving the same end. This technique can be used to determine the adequacy of treatment of subretinal neovascularization, recurrence of subretinal neovascularization in subsequent angiograms, pigment epithelial creep, and changes in fundus lesions.

PURPOSE.To determine whether combining structural (optical coherence tomography, OCT) and functional (standard automated perimetry, SAP) measurements as input for machine learning classifiers (MLCs; relevance vector machine, RVM; and subspace mixture of Gaussians, SSMoG) improves diagnostic accuracy for detecting glaucomatous eyes compared with using each measurement method alone. METHODS.Sixty-nine eyes of 69 healthy control subjects (average age, 62.0, SD 9.7 years; visual field mean deviation [MD], !0.70, SD 1.41 dB) and 156 eyes of 156 patients with glaucoma (average age, 66.4, SD 10.2 years; visual field MD,!3.12, SD 3.43 dB) were imaged with OCT (Stratus OCT, Carl Zeiss Meditec, Inc., Dublin, CA) and tested with SAP (Humphrey Field Analyzer II with Swedish Interactive Thresholding Algorithm, SITA; Carl Zeiss Meditec, Inc.) within 3 months of each other. RVM and SSMoG MLCs were trained and tested on OCT-determined RNFL thickness measurements from 32 sectors ("11.25° each) obtained in the circumpapillary area under the instrument-defined measurement ellipse and SAP pattern deviation values from 52 points from the 24-2 grid, independently and in combination. Tenfold cross-validation was used to train and test classifiers on unique subsets of the full 225-eye data set, and areas under the receiver operating characteristic curve (AUROC) for the classification of eyes in the test set were generated. AUROC results from classifiers trained on OCT and SAP alone and those trained on OCT and SAP in combination were compared. In addition, these results were compared to currently available OCT measurements (mean retinal nerve fiber layer [RNFL] thickness, inferior RNFL thickness, and superior RNFL thickness) and SAP indices (MD and pattern standard deviation [PSD]). RESULTS.The AUROCs for RVM trained on OCT parameters alone, SAP parameters alone and OCT and SAP parameters combined were 0.809, 0.815, and 0.845, respectively. The AUROCs for SSMoG trained on OCT parameters alone, SAP parameters alone, and OCT and SAP parameters combined were 0.817, 0.841, and 0.869, respectively. Combining techniques using both RVM and SSMoG significantly improved on MLC analysis of OCT, but not SAP, measurements alone. Classification performance using RVM and SSMoG was statistically similar. C ONCLUSIONS.RVM and SSMoG Bayesian MLCs trained on OCT and SAP data can successfully discriminate between healthy and early glaucomatous eyes. Combining OCT and SAP measurements using RVM and SSMoG increased diagnostic performance marginally compared with MLC analysis of data obtained using each technology alone. (Invest Ophthalmol Vis Sci.2008;49:945‐953) DOI:10.1167/iovs.07-1083

PURPOSE. To determine whether topographical measurements of the parapapillary region analyzed by machine learning classifiers can detect early to moderate glaucoma better than similarly processed measurements obtained within the disc margin and to improve methods for optimization of machine learning classifier feature selection. METHODS. One eye of each of 95 patients with early to moderate glaucomatous visual field damage and of each of 135 normal subjects older than 40 years participating in the longitudinal Diagnostic Innovations in Glaucoma Study (DIGS) were included. Heidelberg Retina Tomograph (HRT; Heidelberg Engineering, Dossenheim, Germany) mean height contour was measured in 36 equal sectors, both along the disc margin and in the parapapillary region (at a mean contour line radius of 1.7 mm). Each sector was evaluated individually and in combination with other sectors. Gaussian support vector machine (SVM) learning classifiers were used to interpret HRT sector measurements along the disc margin and in the parapapillary region, to differentiate between eyes with normal and glaucomatous visual fields and to compare the results with global and regional HRT parameter measurements. The area under the receiver operating characteristic (ROC) curve was used to measure diagnostic performance of the HRT parameters and to evaluate the cross-validation strategies and forward selection and backward elimination optimization techniques that were used to generate the reduced feature sets. RESULTS. The area under the ROC curve for mean height contour of the 36 sectors along the disc margin was larger than that for the mean height contour in the parapapillary region (0.97 and 0.85, respectively). Of the 36 individual sectors along the disc margin, those in the inferior region between 240° and 300°, had the largest area under the ROC curve (0.85‐ 0.91). With SVM Gaussian techniques, the regional parameters showed the best ability to discriminate between normal eyes and eyes with glaucomatous visual field damage, followed by the global parameters, mean height contour measures along the disc margin, and mean height contour measures in the parapapillary region. The area under the ROC curve was 0.98, 0.94, 0.93, and 0.85, respectively. Cross-validation and optimization techniques demonstrated that good discrimination (99% of peak area under the ROC curve) can be obtained with a reduced number of HRT parameters. CONCLUSIONS. Mean height contour measurements along the disc margin discriminated between normal and glaucomatous eyes better than measurements obtained in the parapapillary region. (Invest Ophthalmol Vis Sci. 2004;45:3144 ‐3151) DOI

PURPOSE To determine whether Heidelberg Retina Tomograph (HRT; Heidelberg Engineering, Dossenheim, Germany) classification techniques and investigational support vector machine (SVM) analyses can detect optic disc abnormalities in glaucoma-suspect eyes before the development of visual field abnormalities. METHODS Glaucoma-suspect eyes (n = 226) were classified as converts or nonconverts based on the development of repeatable (either two or three consecutive) standard automated perimetry (SAP)-detected abnormalities over the course of the study (mean follow-up, approximately 4.5 years). Hazard ratios for development of SAP abnormalities were calculated based on baseline classification results, follow-up time, and end point status (convert, nonconvert). Classification techniques applied were HRT classification (HRTC), Moorfields Regression Analysis, forward-selection optimized SVM (SVM fwd) and backward elimination-optimized SVM (SVM back) analysis of HRT data, and stereophotograph assessment. RESULTS Univariate analyses indicated that all classification techniques were predictors of the development of two repeatable abnormal SAP results, with hazards ratios (95% confidence interval [CI]) ranging from 1.32 (1.00-1.75) for HRTC to 2.0 (1.48-2.76) for stereophotograph assessment (all P < or = 0.05). Only SVM (SVM fwd and SVM back) analysis of HRT data and stereophotograph assessment were univariate predictors of the development of three repeatable abnormal SAP results, with hazard ratios (95% CI) ranging from 1.73 (1.16-2.82) for SVM fwd to 1.82 (1.19-3.12) for SVM back (both P < 0.007). Multivariate analyses including each classification technique individually in a model with age, baseline SAP pattern standard deviation [PSD], and baseline IOP indicated that all classification techniques except HRTC (P = 0.06) were predictors of the development of two repeatable abnormal SAP results with hazards ratios ranging from 1.30 (0.99, 1.73) for HRTC to 1.90 (1.37, 2.69) for stereophotograph assessment. Only SVM (SVM fwd and SVM back) analysis of HRT data and stereophotograph assessment were significant predictors of the development of three repeatable abnormal SAP results in multivariate analyses; hazard ratios of 1.57 (1.03, 2.59) and 1.70 (1.18, 2.51), respectively. SAP PSD was a significant predictor of two repeatable abnormal SAP results in multivariate models with all classification techniques, with hazard ratios ranging from 3.31 (1.39, 7.89) to 4.70 (2.02, 10.93) per 1-dB increase. CONCLUSIONS HRT classifications techniques and stereophotograph assessment can detect optic disc topography abnormalities in glaucoma-suspect eyes before the development of SAP abnormalities. These data support strongly the importance of optic disc examination for early glaucoma diagnosis.

SUMMARY The unique problems encountered in sickle patients include the need to remove peripheral vitreous if perfusing sea fans - which can bleed after vitrectomy - are present at the time of surgery. However, peripheral vitrectomy is riskier than central vitrectomy alone. If visualization of the sea fans is sufficient, it is safer to close the sea fans prior to vitrectomy in order to obviate the need for peripheral vitrectomy, then, only central vitrectomy is performed. When sea fans cannot be closed prior to vitrectomy, peripheral vitreous is removed to allow early photocoagulation of the sea fans before they bleed again. Four case presentations illustrate these principles. Vitrectomy relieved severe vitreous traction that complicated retinal detachment in the fifth patient, thereby eliminating the need for a tight encircling structure, which can be poorly tolerated in patients with SC hemoglobin.