Epidemiology of eye disease in the UK: the Bridlington Eye Assessment Project

Purpose; To determine the prevalence of and impact of eye disease in an elderly population and the diagnostic accuracy of a novel artificial intelligence algorithm for the detection of glaucoma Design; population based, cross sectional study Participants; 3549 Caucasian individuals over the age of 65 years Methods: A directed general and ophthalmic history was obtained from all subjects. Slit lamp eye examination including applanation tonometry and dilated examination of the fundus was performed by one of four specially trained optometrists and supplemented with fundus photography, visual field testing and Heidelberg Retinal Tomography (HRT). Those with reduced vision, raised intraocular pressure, visual field defects or other abnormalities were referred for further assessment by a consultant ophthalmologist and followed longitudinally until a definitive diagnosis was made. All diagnoses of glaucoma were made retrospectively using at least 5 years of longitudinal data to determine status at incident examination. All fundus photographs were reviewed by a single ophthalmologist for signs of age related macula degeneration (AMD) and other retinal disease. HRT outputs were analysed using the device’s proprietary software which produced results for normative based Moorfield’s regression analysis (MRA) and the shape analysis tool Glaucoma Probability Score (GPS). We used a bespoke Matlab based machine learning classifier to providing two further measures based on shape analysis which were termed shape abnormality score (SAS) and abnormal disc score (ADS). Statistical Analysis: Outcomes and associations were explored using t-tests, chi- squared tests and Mantal Haenzel methods. Linear and logistic regression was sed for multivariate analysis. Agreement was measured using kappa, intraclass correlation coefficient and concordance correlation coefficient and plotted using Bland Altman plots. Covariate effects on diagnostic performance were examined using a combination of maximum likelihood probit models and bootstrap analysis. All data analysis was performed using Stata v14 Results; Cataract; Significant lens opacities were present in 45% of individuals of whom 12% went on to have cataract surgery. Women were 29% more likely to have significant cataract than men. 9.5% of eyes showed signs of previous cataract surgery of which 17% either required or had received treatment for subsequent posterior capsular opacification. In the absence of thresholds for surgery 18 cataract operations per thousand ( 95% CI 14 – 23 ) were required for those aged 65-75 years old. For those over 75 years, 76 cataract operations per thousand ( 95% CI 66 – 86 ) were required AMD; Geographic atrophy (grade 4a) occurred in 2.5%, and neovascular AMD (grade 4b) in 1.8% of eyes. Prevalence increased with age with grade 4 (advanced) AMD in 2.2% of those aged 65–69 years, 15.8% for those aged 85– 90 years, and 21.2% for over 90 years. There was significant asymmetry between eyes of individuals with advanced AMD (P<0.001). After correction for age and co-pathology, those with advanced AMD in the better eye were 4 times more likely to be disattisified with their vision. Glaucoma; For tests with a specificity of > 90% for new OAG, intraocular pressure was the least sensitive (48%), while clinical CDR ≥ 0.7 was the most sensitive (76%) test. Optometric impression showed the best specificity (98%) with acceptable sensitivity (51%) but may have been subject to verification bias since final diagnosis was based on clinical impression albeit with the reference to longitudinal results. Because of the low relative prevalence of new glaucoma, the test specificity of 98% still resulted in referral of nearly twice as many false positives as new patients with glaucoma. There was moderate agreement between individual optometrists and the HRT in measuring CDR but wide limits of agreement precluding effective comparison between approaches. Of the disc based measures, SAS and optometric assessment were found to be the most specific but MRA showed the best overall performance. In our subgroup analysis, we found a drop in sensitivity for detection of new disease by HRT using automated shape analysis and by optometrist using Jonas criteria. MRA performed well across all groups and showed similar sensitivity in detection of new and previously diagnosed glaucoma

Epidemiology of eye disease in the UK: the Bridlington Eye Assessment Project

Purpose; To determine the prevalence of and impact of eye disease in an elderly population and the diagnostic accuracy of a novel artificial intelligence algorithm for the detection of glaucoma Design; population based, cross sectional study Participants; 3549 Caucasian individuals over the age of 65 years Methods: A directed general and ophthalmic history was obtained from all subjects. Slit lamp eye examination including applanation tonometry and dilated examination of the fundus was performed by one of four specially trained optometrists and supplemented with fundus photography, visual field testing and Heidelberg Retinal Tomography (HRT). Those with reduced vision, raised intraocular pressure, visual field defects or other abnormalities were referred for further assessment by a consultant ophthalmologist and followed longitudinally until a definitive diagnosis was made. All diagnoses of glaucoma were made retrospectively using at least 5 years of longitudinal data to determine status at incident examination. All fundus photographs were reviewed by a single ophthalmologist for signs of age related macula degeneration (AMD) and other retinal disease. HRT outputs were analysed using the device’s proprietary software which produced results for normative based Moorfield’s regression analysis (MRA) and the shape analysis tool Glaucoma Probability Score (GPS). We used a bespoke Matlab based machine learning classifier to providing two further measures based on shape analysis which were termed shape abnormality score (SAS) and abnormal disc score (ADS). Statistical Analysis: Outcomes and associations were explored using t-tests, chi- squared tests and Mantal Haenzel methods. Linear and logistic regression was sed for multivariate analysis. Agreement was measured using kappa, intraclass correlation coefficient and concordance correlation coefficient and plotted using Bland Altman plots. Covariate effects on diagnostic performance were examined using a combination of maximum likelihood probit models and bootstrap analysis. All data analysis was performed using Stata v14 Results; Cataract; Significant lens opacities were present in 45% of individuals of whom 12% went on to have cataract surgery. Women were 29% more likely to have significant cataract than men. 9.5% of eyes showed signs of previous cataract surgery of which 17% either required or had received treatment for subsequent posterior capsular opacification. In the absence of thresholds for surgery 18 cataract operations per thousand ( 95% CI 14 – 23 ) were required for those aged 65-75 years old. For those over 75 years, 76 cataract operations per thousand ( 95% CI 66 – 86 ) were required AMD; Geographic atrophy (grade 4a) occurred in 2.5%, and neovascular AMD (grade 4b) in 1.8% of eyes. Prevalence increased with age with grade 4 (advanced) AMD in 2.2% of those aged 65–69 years, 15.8% for those aged 85– 90 years, and 21.2% for over 90 years. There was significant asymmetry between eyes of individuals with advanced AMD (P<0.001). After correction for age and co-pathology, those with advanced AMD in the better eye were 4 times more likely to be disattisified with their vision. Glaucoma; For tests with a specificity of > 90% for new OAG, intraocular pressure was the least sensitive (48%), while clinical CDR ≥ 0.7 was the most sensitive (76%) test. Optometric impression showed the best specificity (98%) with acceptable sensitivity (51%) but may have been subject to verification bias since final diagnosis was based on clinical impression albeit with the reference to longitudinal results. Because of the low relative prevalence of new glaucoma, the test specificity of 98% still resulted in referral of nearly twice as many false positives as new patients with glaucoma. There was moderate agreement between individual optometrists and the HRT in measuring CDR but wide limits of agreement precluding effective comparison between approaches. Of the disc based measures, SAS and optometric assessment were found to be the most specific but MRA showed the best overall performance. In our subgroup analysis, we found a drop in sensitivity for detection of new disease by HRT using automated shape analysis and by optometrist using Jonas criteria. MRA performed well across all groups and showed similar sensitivity in detection of new and previously diagnosed glaucoma

Clinical risk stratification in glaucoma

Glaucoma is the leading cause of preventable sight loss in the United Kingdom and the provision of timely glaucoma care has been highlighted as a significant challenge in recent years. Following a recent high-profile investigation, The Healthcare Safety Investigation Branch recommended the validation of risk stratification models to safeguard the vision-related quality of life of glaucoma patients. There continues to be no nationally agreed evidence-based risk stratification model for glaucoma care across the United Kingdom. Some models have used simple measures of disease staging such as visual field mean deviation as surrogates for risk, but more refined, individualised risk stratification models should include factors related to both visual impairment and visual disability. Candidate tools should also incorporate both ocular and systemic co-morbidities, rate of disease progression, visual needs and driving status and undergo clinical refinement and validation to justify implementation. The disruption to routine glaucoma care caused by the COVID-19 pandemic has only highlighted the importance of such risk stratification models and has accelerated their development, application and evaluation. This review aims to critically appraise the available evidence underpinning current approaches for glaucoma risk stratification and to discuss how these may be applied to contemporary glaucoma care within the United Kingdom. Further research will be essential to justify and validate the utility of glaucoma risk stratification models in everyday clinical practice

Prevalence of peripapillary choroidal neovascular membranes (PPCNV) in an elderly UK population—the Bridlington eye assessment project (BEAP): a cross-sectional study (2002–2006)

© 2018, The Royal College of Ophthalmologists. Purpose: There is paucity of data on the epidemiology of peripapillary choroidal neovascularisartion (PPCNV). Our aim was to determine prevalence of PPCNV in the elderly UK population of Bridlington residents aged ≥65 years. Methods: Eyes with PPCNV in the Bridlington eye assessment project (BEAP) database of 3475 participants were analysed. PPCNV outline was drawn, its area measured, and clock-hour involvement of disc circumference recorded. Location and shortest distance from the lesion edge to fovea were recorded. Masked grading for age-related maculopathy (ARM)/reticular pseudodrusen (RPD) within the ETDRS grid was assigned for each eye using a modified Rotterdam scale. Peripapillary retinal pigment epithelial (RPE) changes/drusen were recorded. Visual acuity (VA) and demographic details analysed separately were merged with grading data. Results: PPCNV were identified in ten subjects, and were bilateral in two (20%), a population prevalence of 0.29%, and 0.06% bilaterality. Gender-specific prevalence were 0.36% and 0.19% for females and males, respectively. Age ranged from 66 to 85 years [mean 76.3 (SD 6.4)]. PPCNV were located nasal to disc in 41.7%, measuring 0.46–7.93 mm 2 [mean 2.81 mm 2 (SD 2.82)]. All PPCNV eyes had peripapillary RPE changes. One subject had no ARM, 1 angioid streaks, and 30% RPD. No direct foveal involvement, or reduced VA attributable to PPCNV was observed. Conclusion: PPCNV were infrequent in this population, more common in females, and often located nasal to the disc, without foveal extension. Peripapillary degenerative changes were universal, and strong association with ARM was observed in eyes with PPCNV. Typically, PPCNV were asymptomatic with VA preservation

Prevalence of optic disc haemorrhages in an elderly UK Caucasian population and possible association with reticular pseudodrusen—the Bridlington Eye Assessment Project (BEAP): a cross-sectional study (2002–2006)

Aims: To determine disc haemorrhages (DH) prevalence in an elderly UK population-the Bridlington Eye Assessment Project (BEAP).Methods: Thirty-degree (30°) fundus photographs (3549 participants ≥65 years) were graded for DH/macula changes. Glaucoma evaluation included Goldmann tonometry, 26-point suprathreshold visual-fields and mydriatic slit-lamp assessment for glaucomatous optic neuropathy.Results: 3548 participants with photographs in at least one eye. DH were present in 53 subjects (1.49%), increasing from 1.17% (65-69-year age-group) to 2.19% (80-84-year age53 group), p=0.06. DH was found in 9/96 (9.38%) right eyes (RE) with open angle glaucoma (OAG). Two of twelve RE (16.67%) with normal tension glaucoma (NTG) had DH. Prevalence in eyes without glaucoma was lower (32/3452, [0.93%]). Reticular pseudodrusen (RPD) occurred in 170/3212 (5.29%) subjects without DH, and 8/131 subjects (6.11%) with OAG. Twenty (20) eyes had normal tension glaucoma (NTG), 2 of whom had RPD (10%) (p=0.264). Within a logistic regression model, DH was associated with glaucoma (OR 10.2, 95% CI 5.32 - 19.72) and increasing age (OR 1.05, 95% CI 1.00-1.10, p=0.03). DH was associated with RPD (p=0.05) with univariate analysis but this was not statistically significant in the final adjusted model. There was no significant association with gender, diabetes mellitus (DM), hypertension treatment or AMD grade.Conclusion: DH prevalence is 1.5% in those over 65 years old and significantly associated with glaucoma and increasing age. There appears to be increased RPD prevalence in eyes with DH and NTG with age acting as a confounding factor. Larger studies are required to fully assess the relationship and investigate a possible shared aetiology of choroidal ischaemia

Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images

Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite of female Anopheles mosquito. Microscopists commonly examine thick and thin blood smears to diagnose disease and compute parasitemia. However, their accuracy depends on smear quality and expertise in classifying and counting parasitized and uninfected cells. Such an examination could be arduous for large-scale diagnoses resulting in poor quality. State-of-the-art image-analysis based computer-aided diagnosis (CADx) methods using machine learning (ML) techniques, applied to microscopic images of the smears using hand-engineered features demand expertise in analyzing morphological, textural, and positional variations of the region of interest (ROI). In contrast, Convolutional Neural Networks (CNN), a class of deep learning (DL) models promise highly scalable and superior results with end-to-end feature extraction and classification. Automated malaria screening using DL techniques could, therefore, serve as an effective diagnostic aid. In this study, we evaluate the performance of pre-trained CNN based DL models as feature extractors toward classifying parasitized and uninfected cells to aid in improved disease screening. We experimentally determine the optimal model layers for feature extraction from the underlying data. Statistical validation of the results demonstrates the use of pre-trained CNNs as a promising tool for feature extraction for this purpose

Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy

Despite the remarkable progress that has been made to reduce global malaria mortality by 29% in the past 5 years, malaria is still a serious global health problem. Inadequate diagnostics is one of the major obstacles in fighting the disease. An automated system for malaria diagnosis can help to make malaria screening faster and more reliable. We present an automated system to detect and segment red blood cells (RBCs) and identify infected cells in Wright-Giemsa stained thin blood smears. Specifically, using image analysis and machine learning techniques, we process digital images of thin blood smears to determine the parasitemia in each smear. We use a cell extraction method to segment RBCs, in particular overlapping cells. We show that a combination of RGB color and texture features outperforms other features. We evaluate our method on microscopic blood smear images from human and mouse and show that it outperforms other techniques. For human cells, we measure an absolute error of 1.18% between the true and the automatic parasite counts. For mouse cells, our automatic counts correlate well with expert and flow cytometry counts. This makes our system the first one to work for both human and mouse

Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy

Despite the remarkable progress that has been made to reduce global malaria mortality by 29% in the past 5 years, malaria is still a serious global health problem. Inadequate diagnostics is one of the major obstacles in fighting the disease. An automated system for malaria diagnosis can help to make malaria screening faster and more reliable. We present an automated system to detect and segment red blood cells (RBCs) and identify infected cells in Wright-Giemsa stained thin blood smears. Specifically, using image analysis and machine learning techniques, we process digital images of thin blood smears to determine the parasitemia in each smear. We use a cell extraction method to segment RBCs, in particular overlapping cells. We show that a combination of RGB color and texture features outperforms other features. We evaluate our method on microscopic blood smear images from human and mouse and show that it outperforms other techniques. For human cells, we measure an absolute error of 1.18% between the true and the automatic parasite counts. For mouse cells, our automatic counts correlate well with expert and flow cytometry counts. This makes our system the first one to work for both human and mouse