Hypotony Failure Criteria in Glaucoma Surgical Studies and Their Influence on Surgery Success.

PURPOSE: Review hypotony failure criteria used in glaucoma surgical outcome studies and evaluate their impact on success rates. DESIGN: Systematic literature review and application of hypotony failure criteria to 2 retrospective cohorts. PARTICIPANTS: A total of 934 eyes and 1765 eyes undergoing trabeculectomy and deep sclerectomy (DS) with a median follow-up of 41.4 and 45.4 months, respectively. METHODS: Literature-based hypotony failure criteria were applied to patient cohorts. Intraocular pressure (IOP)-related success was defined as follows: (A) IOP ≤ 21 mmHg with ≥ 20% IOP reduction; (B) IOP ≤ 18 mmHg with ≥ 20% reduction; (C) IOP ≤ 15 mmHg with ≥ 25% reduction; and (D) IOP ≤ 12 mmHg with ≥ 30% reduction. Failure was defined as IOP exceeding these criteria in 2 consecutive visits > 3 months after surgery, loss of light perception, additional IOP-lowering surgery, or hypotony. Cox regression estimated failure risk for different hypotony criteria, using no hypotony as a reference. Analyses were conducted for each criterion and hypotony type (i.e., numerical [IOP threshold], clinical [clinical manifestations], and mixed [combination of numerical or clinical criteria]). MAIN OUTCOME MEASURES: Hazard ratio (HR) for failure risk. RESULTS: Of 2503 studies found, 278 were eligible, with 99 studies (35.6%) lacking hypotony failure criteria. Numerical hypotony was predominant (157 studies [56.5%]). Few studies used clinical hypotony (3 isolated [1.1%]; 19 combined with low IOP [6.8%]). Forty-nine different criteria were found, with IOP < 6 mmHg, IOP < 6 mmHg on ≥ 2 consecutive visits after 3 months, and IOP < 5 mmHg being the most common (41 [14.7%], 38 [13.7%], and 13 [4.7%] studies, respectively). In both cohorts, numerical hypotony posed the highest risk of failure (HR, 1.51-1.21 for criteria A to D; P < 0.001), followed by mixed hypotony (HR, 1.41-1.20 for criteria A to D; P < 0.001), and clinical hypotony (HR, 1.12-1.04; P < 0.001). Failure risk varied greatly with various hypotony definitions, with the HR ranging from 1.02 to 10.79 for trabeculectomy and 1.00 to 8.36 for DS. CONCLUSIONS: Hypotony failure criteria are highly heterogenous in the glaucoma literature, with few studies focusing on clinical manifestations. Numerical hypotony yields higher failure rates than clinical hypotony and can underestimate glaucoma surgery success rates. Standardizing failure criteria with an emphasis on clinically relevant hypotony manifestations is needed. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Tic-Tac: A Translational Approach in Mechanisms Associated with Irregular Heartbeat and Sinus Rhythm Restoration in Atrial Fibrillation Patients.

Atrial fibrillation (AF) is a prevalent cardiac condition predominantly affecting older adults, characterized by irregular heartbeat rhythm. The condition often leads to significant disability and increased mortality rates. Traditionally, two therapeutic strategies have been employed for its treatment: heart rate control and rhythm control. Recent clinical studies have emphasized the critical role of early restoration of sinus rhythm in improving patient outcomes. The persistence of the irregular rhythm allows for the progression and structural remodeling of the atria, eventually leading to irreversible stages, as observed clinically when AF becomes permanent. Cardioversion to sinus rhythm alters this progression pattern through mechanisms that are still being studied. In this review, we provide an in-depth analysis of the pathophysiological mechanisms responsible for maintaining AF and how they are modified during sinus rhythm restoration using existing therapeutic strategies at different stages of clinical investigation. Moreover, we explore potential future therapeutic approaches, including the promising prospect of gene therapy.

Computational Simulation of Colorectal Cancer Biomarker Particle Mobility in a 3D Model.

Even though some methods for the detection of colorectal cancer have been used clinically, most of the techniques used do not consider the in situ detection of colorectal cancer (CRC) biomarkers, which would favor in vivo real-time monitoring of the carcinogenesis process and consequent studies of the disease. In order to give a scientific and computational framework ideal for the evaluation of diagnosis techniques based on the early detection of biomarker molecules modeled as spherical particles from the computational point of view, a computational representation of the rectum, stool and biomarker particles was developed. As consequence of the transport of stool, there was a displacement of CRC biomarker particles that entered the system as a result of the cellular apoptosis processes in polyps with a length lower than 1 cm, reaching a maximum velocity of 3.47×10-3 m/s. The biomarkers studied showed trajectories distant to regions of the polyp of origin in 1 min of simulation. The research results show that the biomarker particles for CRC respond to the variations in the movements of the stool with trajectories and speeds that depend on the location of the injury, which will allow locating the regions with the highest possibilities of catching particles through in situ measurement instruments in the future.

Forecasting the COVID-19 Epidemic by Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study.

BACKGROUND: Previous studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions. OBJECTIVE: The aim of this study is to investigate the relationship between Google Trends searches of symptoms associated with COVID-19 and confirmed COVID-19 cases and deaths. We aim to develop predictive models to forecast the COVID-19 epidemic based on a combination of Google Trends searches of symptoms and conventional COVID-19 metrics. METHODS: An open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal component analysis (PCA) and time series modeling. The application facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected the data of nine countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (error, trend, seasonality; autoregressive integrated moving average; and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root mean square error (RMSE) of the first principal component (PC1). The predictive abilities of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only. RESULTS: The degree of correlation and the best time lag varied as a function of the selected country and topic searched; in general, the optimal time lag was within 15 days. Overall, predictions of PC1 based on both search terms and COVID-19 traditional metrics performed better than those not including Google searches (median 1.56, IQR 0.90-2.49 versus median 1.87, IQR 1.09-2.95, respectively), but the improvement in prediction varied as a function of the selected country and time frame. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median 0.90, IQR 0.50-1.53 versus median 2.27, IQR 1.62-3.74, respectively). CONCLUSIONS: The inclusion of digital online searches in statistical models may improve the nowcasting and forecasting of the COVID-19 epidemic and could be used as one of the surveillance systems of COVID-19 disease. We provide a free web application operating with nearly real-time data that anyone can use to make predictions of outbreaks, improve estimates of the dynamics of ongoing epidemics, and predict future or rebound waves.

Longitudinal Macular Structure-Function Relationships in Glaucoma.

PURPOSE: To investigate the relationship between longitudinal changes in macular thickness measurements from OCT and changes in central visual field (VF) in patients with glaucoma with central or advanced damage at baseline. DESIGN: Longitudinal cohort study. PARTICIPANTS: A total of 116 eyes with ≥3 years of follow-up and ≥5 macular OCT images and central 10° VF tests were selected. METHODS: OCT superpixels and VF locations were matched correcting for retinal ganglion cell (RGC) displacement. Superpixel thickness and VF total deviation (TD) values, in both logarithmic and linear scales, were averaged within 3 eccentricities (3.4°, 5.6°, and 6.8°) and superior and inferior hemiretinas and hemifields. We estimated pointwise TD rates of change and rates of change at superpixels for full macular thickness (FMT), ganglion cell complex (GCC), ganglion cell inner plexiform layer (GCIPL), and ganglion cell layer (GCL). Correlation of structure-function (SF) rates of change was investigated with parametric tests. We compared the proportion of worsening and positive slopes for superpixels and VF test locations (negative vs. positive rates of change with P < 0.05) throughout the follow-up period. Permutation analyses were used to control specificity. MAIN OUTCOME MEASURES: Magnitude of correlation between structural and functional rates of change and proportion of worsening and positive slopes as a function of follow-up time. RESULTS: The median (interquartile range) follow-up and number of exams were 4.2 (3.7-4.6) years and 8 (7-9), respectively. The highest correlation of change rates was observed at 3.4° and 5.6° eccentricities (r = 0.24, 0.41, 0.40, and 0.40 for FMT, GCC, GCIPL, and GCL for 3.4° eccentricity and r = 0.28, 0.32, 0.31, and 0.32 for FMT, GCC, GCIPL, and GCL for 5.6° eccentricity, respectively). Although GCC measures demonstrated the highest overall longitudinal SF correlations, the differences were not statistically significant. Significant structural worsening was more frequently detected than functional deterioration at 3- and 5-year time points (P < 0.025). Permutation analyses also confirmed this finding. CONCLUSIONS: Correlations between central structural and functional rates of change were weak to fair in this cohort. Structural changes were detected more frequently than functional changes. Measurements of both structure and function are required for optimal detection of central progression.

Predictors of Long-Term Visual Field Fluctuation in Glaucoma Patients.

PURPOSE: To identify predictive factors for visual field (VF) fluctuation in glaucoma patients. DESIGN: Retrospective cohort study. PARTICIPANTS: A total of 1392 eyes (816 patients) with 6 or more VFs and 3 years or more of follow-up. METHODS: For each eye, the VF mean deviation (MD) and the pointwise sensitivities were regressed against time to model the series trend, and the root mean square error (RMSE) was estimated as a measure of variability. Potential predictors were selected with least absolute shrinkage and selection operator regression and included eye laterality, ethnicity, glaucoma type, intraocular pressure (IOP) fluctuation, baseline best corrected-visual acuity, intervening cataract or glaucoma surgery, length of follow-up, frequency of testing, baseline MD, rates of VF progression, and median false positive (FP) and false negative (FN) responses. MAIN OUTCOME MEASURES: Predictors of global and pointwise VF long-term fluctuation. RESULTS: In the global model, left eye (0.063 dB; P = 0.022), Asian descent (0.265 dB; P = 0.006), larger IOP fluctuation (0.051 dB; P < 0.001), intervening cataract surgery (0.090 dB; P = 0.023), longer follow-up (0.130 dB; P < 0.001), worse baseline MD (-0.145 dB; P < 0.001), faster VF decay rate (-0.090 dB; P < 0.001), and higher FP rate (0.145 dB; P < 0.001) and FN rate (0.220 dB; P < 0.001) were predictors of VF fluctuation. In the pointwise model, larger IOP fluctuation (0.039 dB; P = 0.022), longer follow-up (0.340 dB; P < 0.001), higher VF frequency (0.238 dB; P = 0.002), intervening glaucoma surgery (0.190 dB; P = 0.01), worse baseline MD (-0.535 dB; P < 0.001), faster VF decay rate (-0.340 dB; P < 0.001), and higher FP rate (0.255 dB; P < 0.001) and FN rate (0.395 dB; P < 0.001) were associated with increased fluctuation. The multivariable model explained 57% and 28% of the pointwise and global variances, respectively. CONCLUSIONS: This study identified novel predictors of VF fluctuation, and explains nearly 60% of the pointwise variance. In the presence of factors predictive of high fluctuation, increased frequency of testing and better analytics will help to identify VF progression more accurately.

Intersection Intelligence: Supporting Urban Platooning with Virtual Traffic Lights over Virtualized Intersection-Based Routing.

The advent of the autonomous car is paving the road to the realization of ideas that will help optimize traffic flows, increase safety and reduce fuel consumption, among other advantages. We present one proposal to bring together Virtual Traffics Lights (VTLs) and platooning in urban scenarios, leaning on vehicle-to-vehicle (V2V) communication protocols that turn intersections into virtual containers of data. Newly-introduced protocols for the combined management of VTLs and platoons are validated by simulation, comparing a range of routing protocols for the vehicular networks with the baseline given by common deployments of traditional traffic lights ruled by state-of-the-art policies. The simulation results show that the combination of VTLs and platoons can achieve significant reductions in travel times and fuel consumption, provided that proper algorithms are used to handle the V2V communications.

Factors Associated to Invasive Fungal Infection in Hispanic Patients with Hematological Malignancies.

Fungal infections represent a serious complication for immunosuppressed patients resulting in an increased morbidity and mortality. A non-concurrent prospective study was performed to evaluate the factors associated to invasive fungal infection (IFI) in patients with hematological malignancies admitted to the University Hospital in San Juan, Puerto Rico from January 1st, 2011 through June 15th, 2014. The medical records of 84 patients were evaluated. Fifty-nine patients with IFI and twenty-five without IFI. The majority were men between 35 to 55 years old. The main hematological diagnosis was acute myelogenous leukemia (AML) followed by acute lymphoblastic leukemia (ALL). Seventy-percent developed IFI. The most common fungi were C. albicans followed by non-albicans species, Fusarium and, Aspergillus species, respectively. About 63% of the patients with AML and 81% without AML had IFI. Those who received steroids were more likely to develop IFI. After adjusting for AML and age, the odds of IFI among patients using steroids were 3.33 higher than those not using steroids. Patients who were exposed to different antifungal medication had 72% lower odds to develop IFI.

Trabeculectomy Can Improve Long-Term Visual Function in Glaucoma.

PURPOSE: To measure the magnitude and direction of visual field (VF) rates of change in glaucoma patients after intraocular pressure (IOP) reduction with trabeculectomy. DESIGN: Retrospective, comparative, longitudinal cohort study. PARTICIPANTS: Patients with open-angle glaucoma. METHODS: Patients who underwent trabeculectomy (Trab) with mitomycin-C (74 eyes of 64 patients) with ≥4 reliable VF measurements before and after trabeculectomy and at least 4 years of follow-up before and after surgery were included. Decay or improvement exponential models were used to calculate pointwise rates of perimetric change before and after surgery. A separate comparison (Comp) group with unoperated glaucoma (71 eyes of 65 patients) with similar baseline damage, number of VF tests, and follow-up was used to address possible regression to the mean. Proportions of VF locations decaying or improving before and after surgery in the Trab group, and during the first and second halves of follow-up in the Comp group, were calculated. A multivariate analysis was used to explore variables associated with VF improvement. MAIN OUTCOME MEASURES: The rate of pointwise VF change before and after surgery in the Trab group and Comp group. RESULTS: Patients in the Trab group were followed for 5.1±2.1 years (mean ± standard deviation) before and 5.4±2.3 years after surgery, with 8.9±4.7 VF tests before and 9.0±4.4 VF tests after surgery. The mean rate of change for all VF locations slowed from -2.5±9.3%/year before surgery to -0.10±13.1%/year after surgery (P < 0.001). In the Trab group, 70% of locations decayed and 30% improved preoperatively; postoperatively, 56% decayed and 44% improved. The differences between the Trab and Comp groups were significant (P < 0.0001, chi-square test). The magnitude of IOP reduction correlated with the excess number of VF locations that exhibited long-term improvement postoperatively (P = 0.009). In the Trab group, 57% of eyes had ≥10 improving VF locations postoperatively. CONCLUSIONS: The results show that trabeculectomy slows the rate of perimetric decay and provide evidence of sustained, long-term improvement of visual function in glaucoma. These findings suggest the possibility of reversal of glaucomatous dysfunction of retinal ganglion cells and their central projections.

Multiple Simultaneous Gastrointestinal Parasitic Infections in a Patient with Human Immunodeficiency Virus.

Patients with the human immunodeficiency virus (HIV) infection are at high risk for gastrointestinal infections causing diarrhea, particularly when those infections are parasitic in nature. This propensity is more pronounced in AIDS, where opportunistic parasitic infections may cause severe diarrhea, marked absorptive dysfunction, and significant risk of mortality. There are scant data regarding parasitic infections among HIV patients in the developed world; most studies and research come from povertystricken areas of South Africa, India, Iran, and the South Pacific. Although multiple infections with the same or different parasites have been reported, simultaneous infections are rare. We present the case of a 35-year-old man who developed a co-infection with Giardia, Cryptosporidium, and Strongyloides, simultaneously, the diagnosis being made after the judicious evaluation of a stool sample. Given the associated morbidity, prompt diagnosis and treatment are needed to avoid further complications in patients with HIV. To our knowledge this is the first reported case of triple parasitic infection in a patient with HIV.

Sporadic Creutzfeldt-Jakob disease in a native Puerto Rican patient.

The diagnosis of Creutzfeldt-Jakob disease (CJD) is often a challenge for most physicians given its extremely low incidence and different clinico-pathological presentations. We report the case of a 56-year old patient native to Puerto Rico suspected of sporadic Creutzfeldt-Jakob disease (sCD). The symptoms at onset were notorious for bilateral cortical blindness followed by rapidly progressive cognitive decline, visual deficit, increased levels of CSF 14-3-3 and tau along with positive brain MRI and EEG, are highly indicative of CJD. The definite diagnosis was confirmed by the National Prion Disease Pathology Surveillance Center (NPDPSC), in Cleveland, Ohio, USA. Lack of genetic mutations in the prion protein (PrP) gene, widespread histopathological changes and the accumulation of scrapie PrP (PrPSc) in the brain confirmed the diagnosis of sCJD. The patient, admitted to our institution in 2011, represents the first detailed report of sCJD in a native Puerto Rican patient living in Puerto Rico.

Cephalic tetanus following penetrating eye trauma: a case report.

Tetanus is a potentially life-threatening infection characterized by muscle spasms. Cephalic tetanus is limited to muscles and nerves in the head and can occur after trauma to this area. Because of the rarity of cephalic tetanus clinicians may be unfamiliar with the clinical presentation unsuspecting of the diagnosis.

Predictors of Glaucomatous Progression in Individuals with Small and Large Optic Discs.

PURPOSE: To identify factors associated with glaucomatous progression in individuals with small and large optic discs. DESIGN: Retrospective review. SUBJECTS: 4505 individuals with glaucoma at UCLA; 233 (59.7%) with small discs, 157 (40.3%) with large discs. METHODS: Small and large disc sizes were defined by OCT or Heidelberg Retinal Tomography as disc area ≤ 5% (≤ 1.3 mm2) and ≥ 95% (≥ 2.9 mm2), respectively. Medical records were reviewed for demographics, systemic comorbidities, glaucoma type, ocular comorbidities, and ocular surgery. Logistic regression was used to identify predictors of visual field (VF) progression in individuals with small and large discs and predictors of large versus small discs. MAIN OUTCOME MEASURES: The VF deterioration with mean deviation, pointwise linear regression, and glaucoma rate index (GRI); large vs. small disc. RESULTS: In individuals with small discs, Asian versus non-Hispanic White ethnicity was associated with increased progression (adjusted odds ratio [aOR] = 4.05; 95% confidence interval [CI] = 1.12-14.59 for GRI). Higher intraocular pressure (IOP) range and peak were associated with increased progression in individuals with both small discs (aOR = 1.12; 95% CI = 1.00-1.27 and aOR = 1.05; 95% CI = 1.00-1.10 per 1 mmHg for range and peak with GRI) and large discs (aOR = 1.35; 95% CI = 1.12-1.66 and aOR = 1.11; 95% CI = 1.03-1.20 per 1 mmHg for range and peak with GRI). Multivariable predictors of having large vs. small discs included vasospastic phenotype (aOR = 2.58; 95% CI = 1.35-5.19) and Black (aOR = 20.46; 95% CI = 8.33-61.84), Hispanic/Latino (aOR = 9.65; 95% CI = 4.14-25.39), Asian (aOR = 4.87; 95% CI = 2.96-8.1), and other (aOR = 2.79; 95% CI = 1.69-4.63) versus non-Hispanic White ethnicity. CONCLUSIONS: Increased odds of glaucomatous progression were associated with Asian vs. non-Hispanic White ethnicity in glaucoma patients with small optic discs, as well as with increased IOP range and peak in those with small and large discs. Individuals with a vasospastic phenotype and those from racial and ethnic minority backgrounds had increased odds of having large vs. small optic discs. Further characterization of discernible phenotypes would improve disease prognostication and help individualize glaucoma treatment. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Efficacy of Smoothing Algorithms to Enhance Detection of Visual Field Progression in Glaucoma.

Purpose: To evaluate and compare the effectiveness of nearest neighbor (NN)- and variational autoencoder (VAE)-smoothing algorithms to reduce variability and enhance the performance of glaucoma visual field (VF) progression models. Design: Longitudinal cohort study. Subjects: 7150 eyes (4232 patients), with ≥ 5 years of follow-up and ≥ 6 visits. Methods: Vsual field thresholds were smoothed with the NN and VAE algorithms. The mean total deviation (mTD) and VF index rates, pointwise linear regression (PLR), permutation of PLR (PoPLR), and the glaucoma rate index were applied to the unsmoothed and smoothed data. Main Outcome Measures: The proportion of progressing eyes and the conversion to progression were compared between the smoothed and unsmoothed data. A simulation series of noiseless VFs with various patterns of glaucoma damage was used to evaluate the specificity of the smoothing models. Results: The mean values of age and follow-up time were 62.8 (standard deviation: 12.6) years and 10.4 (standard deviation: 4.7) years, respectively. The proportion of progression was significantly higher for the NN and VAE smoothed data compared with the unsmoothed data. VF progression occurred significantly earlier with both smoothed data compared with unsmoothed data based on mTD rates, PLR, and PoPLR methods. The ability to detect the progressing eyes was similar for the unsmoothed and smoothed data in the simulation data. Conclusions: Smoothing VF data with NN and VAE algorithms improves the signal-to-noise ratio for detection of change, results in earlier detection of VF progression, and could help monitor glaucoma progression more effectively in the clinical setting. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Prediction of Visual Field Progression with Baseline and Longitudinal Structural Measurements Using Deep Learning.

PURPOSE: Identifying glaucoma patients at high risk of progression based on widely available structural data is an unmet task in clinical practice. We test the hypothesis that baseline or serial structural measures can predict visual field (VF) progression with deep learning (DL). DESIGN: Development of a DL algorithm to predict VF progression. METHODS: 3,079 eyes (1,765 patients) with various types of glaucoma and ≥5 VFs, and ≥3 years of follow-up from a tertiary academic center were included. Serial VF mean deviation (MD) rates of change were estimated with linear-regression. VF progression was defined as negative MD slope with p<0.05. A Siamese Neural Network with ResNet-152 backbone pre-trained on ImageNet was designed to predict VF progression using serial optic-disc photographs (ODP), and baseline retinal nerve fiber layer (RNFL) thickness. We tested the model on a separate dataset (427 eyes) with RNFL data from different OCT. The Main Outcome Measure was Area under ROC curve (AUC). RESULTS: Baseline average (SD) MD was 3.4 (4.9)dB. VF progression was detected in 900 eyes (29%). AUC (95% CI) for model incorporating baseline ODP and RNFL thickness was 0.813 (0.757-0.869). After adding the second and third ODPs, AUC increased to 0.860 and 0.894, respectively (p<0.027). This model also had highest AUC (0.911) for predicting fast progression (MD rate <1.0 dB/year). Model's performance was similar when applied to second dataset using RNFL data from another OCT device (AUC=0.893; 0.837-0.948). CONCLUSIONS: DL model predicted VF progression with clinically relevant accuracy using baseline RNFL thickness and serial ODPs and can be implemented as a clinical tool after further validation.

Long-Term Visual Field Outcomes After Ahmed Glaucoma Valve Implantation.

PURPOSE: To measure visual field (VF) rates of change after Ahmed Glaucoma Valve (AGV) implantation and to investigate risk factors for progression. DESIGN: Retrospective, clinical cohort study. METHODS: Patients who underwent AGV implantation with at least 4 eligible postoperative VFs and 2 years of follow-up were included. Baseline, intraoperative, and postoperative data were collected. VF progression was explored with 3 methods: mean deviation (MD) rate; glaucoma rate index (GRI); and pointwise linear regression (PLR). For a subset of eyes with sufficient preoperative and postoperative VFs, rates were compared between the 2 periods. RESULTS: A total of 173 eyes were included. The intraocular pressure (IOP) and number of glaucoma medications were significantly reduced from a median (interquartile range [IQR]) of 23.5 (12.1) mm Hg at baseline to 12.8 (4.0) mm Hg at final follow-up, and from (mean ± SD) 3.3 ± 1.2 to 2.2 ± 1.4, respectively. A total of 38 eyes (22%) showed VF progression, and 101 eyes (58%) were stable as assessed by all 3 methods, which accounted for 80% of all eyes. The rate of VF decline by MD and GRI was a median (IQR) of -0.30 (0.8) dB/y and -2.30 (10.6) (of -100), respectively. When comparing progression before and after surgery, the reduction was not statistically significant with any of the methods. The peak IOP (after 3 postoperative months) was associated with VF deterioration, with a 7% increase in risk per each additional millimeter of mercury (mm Hg). CONCLUSIONS: To our knowledge, this is the largest published series reporting long-term VF outcomes after glaucoma drainage device implantation. There is a continued, significant rate of VF decline after AGV surgery.

A Neural Network for Automated Image Quality Assessment of Optic Disc Photographs.

This study describes the development of a convolutional neural network (CNN) for automated assessment of optic disc photograph quality. Using a code-free deep learning platform, a total of 2377 optic disc photographs were used to develop a deep CNN capable of determining optic disc photograph quality. Of these, 1002 were good-quality images, 609 were acceptable-quality, and 766 were poor-quality images. The dataset was split 80/10/10 into training, validation, and test sets and balanced for quality. A ternary classification model (good, acceptable, and poor quality) and a binary model (usable, unusable) were developed. In the ternary classification system, the model had an overall accuracy of 91% and an AUC of 0.98. The model had higher predictive accuracy for images of good (93%) and poor quality (96%) than for images of acceptable quality (91%). The binary model performed with an overall accuracy of 98% and an AUC of 0.99. When validated on 292 images not included in the original training/validation/test dataset, the model's accuracy was 85% on the three-class classification task and 97% on the binary classification task. The proposed system for automated image-quality assessment for optic disc photographs achieves high accuracy in both ternary and binary classification systems, and highlights the success achievable with a code-free platform. There is wide clinical and research potential for such a model, with potential applications ranging from integration into fundus camera software to provide immediate feedback to ophthalmic photographers, to prescreening large databases before their use in research.

Phenotypic expressions of the optic disc in primary open-angle glaucoma.

BACKGROUND: Which phenotypes are we able to recognize in the optic nerve of patients with primary open angle glaucoma? METHODS: Retrospective interventional case series. 885 eyes from 885 patients at an outpatient tertiary care centre who met specified criteria for POAG were included. Disc photographs were classified by three glaucoma specialists into the following phenotypes according to their predominant characteristics: (1) concentric rim thinning, (2) focal rim thinning, (3) acquired pit of the optic nerve (APON), (4) tilted, (5) extensive peripapillary atrophy (PPA), and (6) broad rim thinning. Demographic, medical, and ocular data were collected. Kruskal-Wallis was used as a non-parametric test and pairwise comparison was performed by using Wilcoxon rank sum test corrected. RESULTS: Phenotypic distribution was as follows: 398(45%) focal thinning, 153(18%) concentric thinning, 153(17%) broad thinning, 109(12%) tilted, 47(5%) extensive PPA and 25(3%) APON. Phenotypic traits of interest included a higher proportion of female patients with the focal thinning phenotype (p = 0.015); myopia (p = 0.000), Asian race (OR: 8.8, p = 0.000), and younger age (p = 0.000) were associated with the tilted phenotype; the concentric thinning patients had thicker RNFL (p = 0.000), higher MD (p = 0.008) and lower PSD (p = 0.043) than broad thinning, despite no difference in disc sizes (p = 0.849). The focal thinning group had a localized VF pattern with high PSD compared to concentric thinning (p = 0.005). CONCLUSION: We report six phenotypic classifications of POAG patients with demographic and ocular differences between phenotypes. Future refinement of phenotypes should allow enhanced identification of genetic associations and improved individualization of patient care.

RimNet: A Deep Neural Network Pipeline for Automated Identification of the Optic Disc Rim.

Purpose: Accurate neural rim measurement based on optic disc imaging is important to glaucoma severity grading and often performed by trained glaucoma specialists. We aim to improve upon existing automated tools by building a fully automated system (RimNet) for direct rim identification in glaucomatous eyes and measurement of the minimum rim-to-disc ratio (mRDR) in intact rims, the angle of absent rim width (ARW) in incomplete rims, and the rim-to-disc-area ratio (RDAR) with the goal of optic disc damage grading. Design: Retrospective cross sectional study. Participants: One thousand and twenty-eight optic disc photographs with evidence of glaucomatous optic nerve damage from 1021 eyes of 903 patients with any form of primary glaucoma were included. The mean age was 63.7 (± 14.9) yrs. The average mean deviation of visual fields was -8.03 (± 8.59). Methods: The images were required to be of adequate quality, have signs of glaucomatous damage, and be free of significant concurrent pathology as independently determined by glaucoma specialists. Rim and optic cup masks for each image were manually delineated by glaucoma specialists. The database was randomly split into 80/10/10 for training, validation, and testing, respectively. RimNet consists of a deep learning rim and cup segmentation model, a computer vision mRDR measurement tool for intact rims, and an ARW measurement tool for incomplete rims. The mRDR is calculated at the thinnest rim section while ARW is calculated in regions of total rim loss. The RDAR was also calculated. Evaluation on the Drishti-GS dataset provided external validation (Sivaswamy 2015). Main Outcome Measures: Median Absolute Error (MAE) between glaucoma specialists and RimNet for mRDR and ARW. Results: On the test set, RimNet achieved a mRDR MAE of 0.03 (0.05), ARW MAE of 31 (89)°, and an RDAR MAE of 0.09 (0.10). On the Drishti-GS dataset, an mRDR MAE of 0.03 (0.04) and an mRDAR MAE of 0.09 (0.10) was observed. Conclusions: RimNet demonstrated acceptably accurate rim segmentation and mRDR and ARW measurements. The fully automated algorithm presented here would be a valuable component in an automated mRDR-based glaucoma grading system. Further improvements could be made by improving identification and segmentation performance on incomplete rims and expanding the number and variety of glaucomatous training images.

DDLSNet: A Novel Deep Learning-Based System for Grading Funduscopic Images for Glaucomatous Damage.

Purpose: To report an image analysis pipeline, DDLSNet, consisting of a rim segmentation (RimNet) branch and a disc size classification (DiscNet) branch to automate estimation of the disc damage likelihood scale (DDLS). Design: Retrospective observational. Participants: RimNet and DiscNet were developed with 1208 and 11 536 optic disc photographs (ODPs), respectively. DDLSNet performance was evaluated on 120 ODPs from the RimNet test set, for which the DDLS scores were graded by clinicians. Reproducibility was evaluated on a group of 781 eyes, each with 2 ODPs taken within 4 years apart. Methods: Disc damage likelihood scale calculation requires estimation of optic disc size, provided by DiscNet (VGG19 network), and the minimum rim-to-disc ratio (mRDR) or absent rim width (ARW), provided by RimNet (InceptionV3/LinkNet segmentation model). To build RimNet's dataset, glaucoma specialists marked optic disc rim and cup boundaries on ODPs. The "ground truth" mRDR or ARW was calculated. For DiscNet's dataset, corresponding OCT images provided "ground truth" disc size. Optic disc photographs were split into 80/10/10 for training, validation, and testing, respectively, for RimNet and DiscNet. DDLSNet estimation was tested against manual grading of DDLS by clinicians with the average score used as "ground truth." Reproducibility of DDLSNet grading was evaluated by repeating DDLS estimation on a dataset of nonprogressing paired ODPs taken at separate times. Main Outcome Measures: The main outcome measure was a weighted kappa score between clinicians and the DDLSNet pipeline with agreement defined as ± 1 DDLS score difference. Results: RimNet achieved an mRDR mean absolute error (MAE) of 0.04 (± 0.03) and an ARW MAE of 48.9 (± 35.9) degrees when compared to clinician segmentations. DiscNet achieved 73% (95% confidence interval [CI]: 70%, 75%) classification accuracy. DDLSNet achieved an average weighted kappa agreement of 0.54 (95% CI: 0.40, 0.68) compared to clinicians. Average interclinician agreement was 0.52 (95% CI: 0.49, 0.56). Reproducibility testing demonstrated that 96% of ODP pairs had a difference of ≤ 1 DDLS score. Conclusions: DDLSNet achieved moderate agreement with clinicians for DDLS grading. This novel approach illustrates the feasibility of automated ODP grading for assessing glaucoma severity. Further improvements may be achieved by increasing the number of incomplete rims sample size, expanding the hyperparameter search, and increasing the agreement of clinicians grading ODPs.