Cataract Prevalence Varies Substantially with Assessment Systems: Comparison of Clinical and Photographic Grading in a Population-Based Study

NCME-Net: Nuclear cataract mask encoder network for intelligent grading using self-supervised learning from anterior segment photographs

Cataracts are a leading cause of blindness worldwide, making accurate diagnosis and effective surgical planning critical. However, grading the severity of the lens nucleus is challenging because deep learning (DL) models pretrained using ImageNet perform poorly when applied directly to medical data due to the limited availability of labeled medical images and high interclass similarity. Self-supervised pretraining offers a solution by circumventing the need for cost-intensive data annotations and bridging domain disparities. In this study, to address the challenges of intelligent grading, we proposed a hybrid model called nuclear cataract mask encoder network (NCME-Net), which utilizes self-supervised pretraining for the four-class analysis of nuclear cataract severity. A total of 792 images of nuclear cataracts were categorized into the training set (533 images), the validation set (139 images), and the test set (100 images). NCME-Net achieved a diagnostic accuracy of 91.0 % on the test set, a 5.0 % improvement over the best-performing DL model (ResNet50). Experimental results demonstrate NCME-Net's ability to distinguish between cataract severities, particularly in scenarios with limited samples, making it a valuable tool for intelligently diagnosing cataracts. In addition, the effect of different self-supervised tasks on the model's ability to capture the intrinsic structure of the data was studied. Findings indicate that image restoration tasks significantly enhance semantic information extraction.

A deep network DeepOpacityNet for detection of cataracts from color fundus photographs

BACKGROUND

Cataract diagnosis typically requires in-person evaluation by an ophthalmologist. However, color fundus photography (CFP) is widely performed outside ophthalmology clinics, which could be exploited to increase the accessibility of cataract screening by automated detection.

METHODS

DeepOpacityNet was developed to detect cataracts from CFP and highlight the most relevant CFP features associated with cataracts. We used 17,514 CFPs from 2573 AREDS2 participants curated from the Age-Related Eye Diseases Study 2 (AREDS2) dataset, of which 8681 CFPs were labeled with cataracts. The ground truth labels were transferred from slit-lamp examination of nuclear cataracts and reading center grading of anterior segment photographs for cortical and posterior subcapsular cataracts. DeepOpacityNet was internally validated on an independent test set (20%), compared to three ophthalmologists on a subset of the test set (100 CFPs), externally validated on three datasets obtained from the Singapore Epidemiology of Eye Diseases study (SEED), and visualized to highlight important features.

RESULTS

Internally, DeepOpacityNet achieved a superior accuracy of 0.66 (95% confidence interval (CI): 0.64-0.68) and an area under the curve (AUC) of 0.72 (95% CI: 0.70-0.74), compared to that of other state-of-the-art methods. DeepOpacityNet achieved an accuracy of 0.75, compared to an accuracy of 0.67 for the ophthalmologist with the highest performance. Externally, DeepOpacityNet achieved AUC scores of 0.86, 0.88, and 0.89 on SEED datasets, demonstrating the generalizability of our proposed method. Visualizations show that the visibility of blood vessels could be characteristic of cataract absence while blurred regions could be characteristic of cataract presence.

CONCLUSIONS

DeepOpacityNet could detect cataracts from CFPs in AREDS2 with performance superior to that of ophthalmologists and generate interpretable results. The code and models are available at https://github.com/ncbi/DeepOpacityNet ( https://doi.org/10.5281/zenodo.10127002 ).

A role of point-of-care ultrasound in the emergency department diagnosis of vision loss due to traumatic cataract

BACKGROUND

Ocular complaints, including acute or subacute vision loss, are commonly encountered in emergency departments (ED). These potentially time-sensitive complaints are difficult to diagnose and evaluate without adequate, specialized equipment and expertise. Additionally, a thorough evaluation often requires a more extensive and specialized physical exam, imaging, and ophthalmologic consultation, all of which may not be readily available in the acute setting.

CASE PRESENTATION

This case report presented a patient in the emergency department with the chief complaint of vision loss. Point-of-care ultrasound (POCUS) using the 10-MHz-linear-array probe, in the ocular setting, demonstrated calcification of the lens, a finding consistent with cataract in the right eye.

CONCLUSIONS

The use of POCUS can expedite the accurate identification of vision threatening pathology, such as cataracts, and streamline ED disposition and plan of care.

Prevalence of cataract among Australian commercial airline pilots

Because little is known about cataract in pilots, we estimated prevalence by anonymously ascertaining all commercial airline pilots diagnosed with cataract 2011-2016 using the electronic Medical Records System of the Australian Civil Aviation Safety Authority. Of 14,163 Australian male commercial pilots licensed in 2011, 1286 aged ≥60 had biennial eye examinations showing a cataract prevalence of 11.6%. Among 12,877 pilots aged <60, based on compulsory eye examinations only when first licensed, prevalence was 0.5%. There was no significant difference by ambient ultraviolet (UV) radiation levels in state of residence though lowest prevalence was seen in the low-UV state of Victoria. Most cataract in pilots ≥60 years was bilateral and of mild severity, while cataract in pilots <60 were more likely to be unilateral and of greater severity.

Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity

PURPOSE

To develop and evaluate deep learning models to perform automated diagnosis and quantitative classification of age-related cataract, including all three anatomical types, from anterior segment photographs.

DESIGN

Application of deep learning models to Age-Related Eye Disease Study (AREDS) dataset.

PARTICIPANTS

18,999 photographs (6,333 triplets) from longitudinal follow-up of 1,137 eyes (576 AREDS participants).

METHODS

Deep learning models were trained to detect and quantify nuclear cataract (NS; scale 0.9-7.1) from 45-degree slit-lamp photographs and cortical (CLO; scale 0-100%) and posterior subcapsular (PSC; scale 0-100%) cataract from retroillumination photographs. Model performance was compared with that of 14 ophthalmologists and 24 medical students. The ground truth labels were from reading center grading.

MAIN OUTCOME MEASURES

Mean squared error (MSE).

RESULTS

On the full test set, mean MSE values for the deep learning models were: 0.23 (SD 0.01) for NS, 13.1 (SD 1.6) for CLO, and 16.6 (SD 2.4) for PSC. On a subset of the test set (substantially enriched for positive cases of CLO and PSC), for NS, mean MSE for the models was 0.23 (SD 0.02), compared to 0.98 (SD 0.23; p=0.000001) for the ophthalmologists, and 1.24 (SD 0.33; p=0.000005) for the medical students. For CLO, mean MSE values were 53.5 (SD 14.8), compared to 134.9 (SD 89.9; p=0.003) and 422.0 (SD 944.4; p=0.0007), respectively. For PSC, mean MSE values were 171.9 (SD 38.9), compared to 176.8 (SD 98.0; p=0.67) and 395.2 (SD 632.5; p=0.18), respectively. In external validation on the Singapore Malay Eye Study (sampled to reflect the distribution of cataract severity in AREDS), MSE was 1.27 for NS and 25.5 for PSC.

CONCLUSIONS

A deep learning framework was able to perform automated and quantitative classification of cataract severity for all three types of age-related cataract. For the two most common types (NS and CLO), the accuracy was significantly superior to that of ophthalmologists; for the least common type (PSC), the accuracy was similar. The framework may have wide potential applications in both clinical and research domains. In the future, such approaches may increase the accessibility of cataract assessment globally. The code and models are publicly available at https://XXX.

GLA-Net: A global-local attention network for automatic cataract classification

Cataracts are the most crucial cause of blindness among all ophthalmic diseases. Convenient and cost-effective early cataract screening is urgently needed to reduce the risks of visual loss. To date, many studies have investigated automatic cataract classification based on fundus images. However, existing methods mainly rely on global image information while ignoring various local and subtle features. Notably, these local features are highly helpful for the identification of cataracts with different severities. To avoid this disadvantage, we introduce a deep learning technique to learn multilevel feature representations of the fundus image simultaneously. Specifically, a global-local attention network (GLA-Net) is proposed to handle the cataract classification task, which consists of two levels of subnets: the global-level attention subnet pays attention to the global structure information of the fundus image, while the local-level attention subnet focuses on the local discriminative features of the specific regions. These two types of subnets extract retinal features at different attention levels, which are then combined for final cataract classification. Our GLA-Net achieves the best performance in all metrics (90.65% detection accuracy, 83.47% grading accuracy, and 81.11% classification accuracy of grades 1 and 2). The experimental results on a real clinical dataset show that the combination of global-level and local-level attention models is effective for cataract screening and provides significant potential for other medical tasks.

In-vivo anterior segment OCT imaging provides unique insight into cerulean blue-dot opacities and cataracts in Down syndrome

Down syndrome (DS) is frequently associated with cataract, but there remains scant information about DS cataract morphology. Supra-nuclear cataracts in DS have been proposed as indicative of beta-amyloid (Aβ) aggregation and thus potential biomarkers for Alzheimer’s (AD). This study employed anterior segment OCT (AS-OCT) and slit-lamp (SL) photography to image the crystalline lens in DS, compared with adult controls. Lens images were obtained post-dilation. Using MATLAB, AS-OCT images were analysed and lens opacities calculated as pixel intensity and area ratios. SL images were classified using LOCS III. Subjects were n = 28 DS (mean ± SD 24.1 ± 14.3years), and n = 36 controls (54.0 ± 3.4years). For the DS group, AS-OCT imaging revealed the frequent presence of small dot opacities (27 eyes, 50%) in the cortex and nucleus of the lens, covering an area ranging from 0.2–14%. There was no relation with age or visual acuity and these dot opacities (p > 0.5) and they were not present in any control lenses. However, their location and morphology does not coincide with previous reports linking these opacities with Aβ accumulation and AD. Four participants (14%) in the DS group had clinically significant age-related cataracts, but there was no evidence of early onset of age-related cataracts in DS.

Analysis of factors associated with vision after cataract surgery in chronic renal failure patients on dialysis

Background

To analyze the related factors of visual acuity after phacoemulsification and intraocular lens implantation in chronic renal failure (CRF) patients.

Methods

We retrospectively analyzed 42 patients (51 eyes) with CRF (failure, uremia) on hemodialysis or peritoneal dialysis and 40 patients (50 eyes) without CRF as a control group. Each individual underwent physical and laboratory examinations including best corrected visual acuity (BCVA), slit lamp examination, intraocular pressure, corneal endothelial cell count, fundus examination and optical coherence tomography (OCT) for macular examination. The patients with abnormal platelet, liver and kidney function, coagulation function received treatment accordingly to reduce the perioperative risk. All patients underwent phacoemulsification with IOL implantation. Follow-up examinations were performed at 1 week, 1 month and 3 months after surgery and included BCVA, slit lamp examination, noncontact IOP, dilated fundus examination and OCT of the macula.

Results

In control group the preoperative RBC, HB, Cr, and urea values were not associated with the pre- or postoperative BCVA. The RBC, HB, Cr, urea, SBP, DBP, preoperative BCVA and postoperative BCVA values were all significantly different between CRF and control group(P < 0.05).

Conclusion

In CRF patients, the RBC, HB, Cr and Urea indexes should be monitored before the cataract operation for guarded visual outcome. The pre-existing ocular comorbidities could significantly compromise the vision. The CRF patients could achieve relatively good visual outcome after cataract surgery when the underlaying diseases are effectively managed.

Exploiting ensemble learning for automatic cataract detection and grading

Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is one of the most common causes of visual impairment worldwide. Early diagnosis demands the expertise of trained healthcare professionals, which may present a barrier to early intervention due to underlying costs. To date, studies reported in the literature utilize a single learning model for retinal image classification in grading cataract severity. We present an ensemble learning based approach as a means to improving diagnostic accuracy. Three independent feature sets, i.e., wavelet-, sketch-, and texture-based features, are extracted from each fundus image. For each feature set, two base learning models, i.e., Support Vector Machine and Back Propagation Neural Network, are built. Then, the ensemble methods, majority voting and stacking, are investigated to combine the multiple base learning models for final fundus image classification. Empirical experiments are conducted for cataract detection (two-class task, i.e., cataract or non-cataractous) and cataract grading (four-class task, i.e., non-cataractous, mild, moderate or severe) tasks. The best performance of the ensemble classifier is 93.2% and 84.5% in terms of the correct classification rates for cataract detection and grading tasks, respectively. The results demonstrate that the ensemble classifier outperforms the single learning model significantly, which also illustrates the effectiveness of the proposed approach.

Straylight and Visual Quality on Early Nuclear and Posterior Subcapsular Cataracts

PURPOSE

To measure log(s) and OSI parameters, both related to forward light scattering in the eye, in subjects with different kinds of early cataracts-nuclear or posterior subcapsular-and corrected visual acuity (CVA).

METHODS

34 eyes of 19 patients ranged between 50 and 75 years old with diagnosed nuclear (14 eyes) or posterior subcapsular cataract (20 eyes) were recruited. Only NO1, NO2, P1, and P2 opacity scores according to LOCS III were included. Observer examination included visual acuity, contrast threshold (Ct), and measurements performed by straylightmeter (straylight parameter log(s)) and double-pass instrument (objective scatter index (OSI)).

RESULTS

OSI and log(s) were correlated with LOCSIII in nuclear opacities (p = 0.015 and 0.004, respectively) and in the whole data (p = 0.027 and 0.019, respectively) but did not for posterior subcapsular opacities alone. OSI was strongly correlated with log(s) in nuclear (r = 0.885 and p < 0.001) but not in posterior subcapsular cases (r = 0.382 and p = 0.097). Ct was correlated with log(s) for both cataract types (p = 0.043 for nuclear and p= 0.005 for posterior subcapsular cataract) but not with OSI (p = 0.093 for nuclear and p = 0.064 for posterior subcapsular cataract).

CONCLUSIONS

OSI and log(s) discriminate early stages of nuclear cataracts when taking LOCS III as reference, so these opacities could be graded by any of those parameters. LOCSIII does not represent the visual condition for posterior subcapsular cataract. Straylightmeter measurements express the loss in contrast sensitivity caused by nuclear and posterior subcapsular opacities. Studies of lens opacities must be separated according to the type of opacity present in eyes.

Results of cataract surgery in renal transplantation and hemodialysis patients

AIM

To compare the effect of cataract surgery in renal transplantation and hemodialysis patients.

METHODS

We evaluated 51 eyes of 31 renal transplantation patients, 41 eyes of 29 hemodialysis patients and 45 eyes of 32 normal control patients who received phacoemulsification and intraocular lens (IOL) implantation from January, 2000 to August, 2014 in the Beijing Friendship Hospital. Each individual underwent a blood routine and a kidney function examination. Routine ophthalmologic examination included best-corrected visual acuity (BCVA), a slit-lamp examination to detect cataract type, determination of intraocular pressure, a corneal endothelial count, and fundus examination. All patients received phacoemulsification and an IOL implantation.

RESULTS

For the types of cataract in the three groups, transplantation group was significantly different from normal control group (P=0.04), the most kind is posterior subcapsular cataract (PSC) in transplantation group 33 (64.7%), hemodialysis group had no significantly difference from normal control group (P=0.43), and the difference between transplantation group and hemodialysis group also had significantly difference (P=0.02). For postoperative BCVA in the three groups, transplantation group had significantly difference from normal control group (P=0.03), hemodialysis group was significantly different from normal control group (P=0.00), and the difference between transplantation group and hemodialysis group also had significantly difference (P=0.00). The multiple linear regression equation is Y=0.007 hemoglobin (Hb)-0.000233 serum creatinine (Cr), R(2)=0.898. Postoperative fundus examination showed that hemorrhage, exudation, and macular degeneration were greater in the hemodialysis group.

CONCLUSION

This study showed that the PSC was more in the renal transplantation patients. BCVA was better and fundus lesions were less frequent in the renal transplantation group than in the hemodialysis group after cataract surgery. The multiple linear regression was showed that the Hb was positively correlated with postoperative BCVA, while Cr was negatively correlated with postoperative BCVA. These results may act as indicators in predicting visual acuity for the renal transplantation and hemodialysis patients.

Ancestry, Socioeconomic Status, and Age-Related Cataract in Asians: The Singapore Epidemiology of Eye Diseases Study

PURPOSE

To determine the prevalence of age-related cataract and its ancestral and socioeconomic risk factors in a multi-ethnic Asian population.

DESIGN

Population-based, cross-sectional study.

PARTICIPANTS

A total of 10 033 adults (3353 Chinese, 3280 Malays, and 3400 Indians) aged >40 years in the Singapore Epidemiology of Eye Diseases Study.

METHODS

Study participants were invited for a structured interview and received a standardized comprehensive eye examination. Digital lens photographs were taken from eyes of each participant and graded for nuclear, cortical, and posterior subcapsular (PSC) cataract, following the Wisconsin Cataract Grading System. Prevalence data were compared with the Blue Mountains Eye Study (BMES) in Australia. Information on medical and lifestyle factors was collected using questionnaires and blood samples. To increase the precision of racial definition, genetic ancestry was derived from genome-wide single nucleotide polymorphism markers using principal component analysis. Regression models were used to investigate the association of cataract with socioeconomic factors (education and income) and genetic ancestry.

MAIN OUTCOME MEASURES

Age-related cataract.

RESULTS

A total of 8750 participants (94.0%) had gradable lens photographs. The age-standardized prevalence of cataract surgery in Chinese (16.0%), Malays (10.6%), and Indians (20.2%) was higher than in white subjects (4.1%). We found the age-standardized cataract prevalence in Chinese (30.4%), Malays (37.8%), and Indians (33.1%) was higher than in whites (18.5%). Cataract was 1.5 to 2 times more common in Asians and began 10 years earlier than in white subjects. Malays had significantly higher age-standardized prevalence of nuclear, cortical, and PSC cataract than Chinese (P<0.001). The severity of nuclear, cortical, and PSC cataract was significantly correlated with genetic ancestry in our South East Asian population. Less education and lower income were associated with cataract for Chinese and Indians but not Malays. The presence of visual impairment associated with cataract was higher in people aged ≥60 years and Malays.

CONCLUSIONS

We showed that people of different Asian ethnicities had a higher prevalence and earlier age of onset of cataract than Europeans. People of Malay ancestry have a greater severity for all cataract subtypes than people of Chinese ancestry. Education and income were associated with cataract for certain Asian subgroups.

Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning

GOAL

Cataracts are a clouding of the lens and the leading cause of blindness worldwide. Assessing the presence and severity of cataracts is essential for diagnosis and progression monitoring, as well as to facilitate clinical research and management of the disease.

METHODS

Existing automatic methods for cataract grading utilize a predefined set of image features that may provide an incomplete, redundant, or even noisy representation. In this study, we propose a system to automatically learn features for grading the severity of nuclear cataracts from slit-lamp images. Local filters are first acquired through clustering of image patches from lenses within the same grading class. The learned filters are fed into a convolutional neural network, followed by a set of recursive neural networks, to further extract higher order features. With these features, support vector regression is applied to determine the cataract grade.

RESULTS

The proposed system is validated on a large population-based dataset of [Formula: see text] images, where it outperforms the state of the art by yielding with respect to clinical grading a mean absolute error ( ε) of 0.304, a 70.7% exact integral agreement ratio ( R0), an 88.4% decimal grading error ≤ 0.5 ( Re0.5 ), and a 99.0% decimal grading error ≤ 1.0 ( Re1.0 ).

SIGNIFICANCE

The proposed method is useful for assisting and improving clinical management of the disease in the context of large-population screening and has the potential to be applied to other eye diseases.

Longitudinal increase in anisometropia in older adults

PURPOSE

Anisometropia shows an exponential increase in prevalence with increasing age based on cross-sectional studies. The purpose of this study was to evaluate longitudinal changes in anisometropia in all refractive components in older observers and to assess the influence of early cataract development.

METHODS

Refractive error was assessed at two time points separated by approximately 12 years in 118 older observers (aged 67.1 and 79.3 years at the two test times). Anisometropia defined as greater than or equal to 1.00 D was calculated for all refractive components. The subjects had intact ocular lenses in both eyes throughout the study. Lens evaluations were performed at the second test using the Lens Opacities Classification System III.

RESULTS

All refractive components approximately doubled in prevalence of anisometropia. Spherical equivalent anisometropia changed from 16.1 to 32.2%. Similar changes were found for spherical error (17 to 38.1%), primary astigmatism (7.6 to 17.8%), and oblique astigmatism (14.4 to 29.7%). Many who did not have anisometropia at the first visit subsequently developed anisometropia (e.g., 26.3% for spherical error and 22.9% for oblique cylinder). The onset of anisometropia occurred at all ages within the studied age range, with no particular preference for any one age. A small number lost anisometropia over time. Individual comparisons of refractive error changes in the two eyes in combination with nuclear lens changes showed that early changes in nuclear sclerosis in the two eyes could account for a large proportion of anisometropia (~40%), but unequal hyperopic shift in the spherical component in the two eyes was the primary cause of the anisometropia.

CONCLUSIONS

Anisometropia is at least 10 times more common in the elderly than in children, and anisometropia develops in all refractive components in the oldest observers. Clinicians need to be aware of this common condition that could lead to binocular vision problems and potentially cause falls in the elderly.

Female reproductive factors and major eye diseases in Asian women -the Singapore Malay Eye Study

PURPOSE

To examine the association of reproductive factors and major eye diseases, including glaucoma, age-related macular degeneration (AMD), diabetic retinopathy and cataract, in Asian women.

METHODS

The Singapore Malay Eye Study is a population-based cross-sectional epidemiological study which examined 3280 persons (78.7% response) of Malay ethnicity aged 40-80 years; 1704 were female. Information on reproductive factors and use of hormone replacement therapy (HRT) was collected using an interviewer-administered questionnaire. Glaucoma was defined according to the International Society for Geographical and Epidemiological Ophthalmology criteria. Retinal photographs were graded for AMD following the Wisconsin grading system, and diabetic retinopathy according to the modified Airlie House classification system. Cataract was graded according to the Lens Opacity Classification System III.

RESULTS

A total of 1176 women reported having experienced menopause by the time of the study with 1073 (91%) having a natural menopause, 88 (7.5%) a hysterectomy and 9 (0.8%) due to other reasons; HRT was used by 70 (6%) women. Women whose age at menopause was ≤52 years were 3.5 times more likely to have glaucoma (95% confidence interval, CI, 1.23-9.98, p value = 0.02) than those whose age at menopause was ≥53 years. Age of menopause was not associated with AMD (age-adjusted odds ratio, OR, 1.22, 95% CI 0.65-2.31), diabetic retinopathy (age-adjusted OR 1.01, 95% CI 0.66-1.54) or cataract (age-adjusted OR 1.38, 95% CI 0.95-2.00). Use of HRT was not associated with any of these eye diseases.

CONCLUSION

Women who had menopause at a younger age were more likely to have glaucoma. This association needs to be confirmed in other studies.

Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression

Cataracts, which result from lens opacification, are the leading cause of blindness worldwide. Current methods for determining the severity of cataracts are based on manual assessments that may be weakened by subjectivity. In this work, we propose a system to automatically grade the severity of nuclear cataracts from slit-lamp images. We introduce a new feature for cataract grading together with a group sparsity-based constraint for linear regression, which performs feature selection, parameter selection and regression model training simultaneously. In experiments on a large database of 5378 images, our system outperforms the state-of-the-art by yielding with respect to clinical grading a mean absolute error (epsilon) of 0.336, a 69.0% exact integral agreement ratio (R0), a 85.2% decimal grading error < or = 0.5 (Re0.5), and a 98.9% decimal grading error < or = 1.0 (Re1.0). Through a more objective grading of cataracts using our proposed system, there is potential for better clinical management of the disease.

Retinal vascular fractal dimension and its relationship with cardiovascular and ocular risk factors

PURPOSE

To examine the influence of a range of cardiovascular risk factors and ocular conditions on retinal vascular fractal dimension in the Singapore Malay Eye Study.

DESIGN

Population-based cross-sectional study.

METHODS

Fractal analysis of the retinal vessels is a method to quantify the global geometric complexity of the retinal vasculature. Retinal vascular fractal dimension (D(f)) and caliber were measured from retinal photographs using a computer-assisted program. D(f) and arteriolar caliber were combined to form a retinal vascular optimality score (ranging from 0 to 3). Data on cardiovascular and ocular factors were collected from all participants based on a standardized protocol.

RESULTS

Two thousand nine hundred thirteen (88.8% of 3280 participants) persons had retinal photographs of sufficient quality for the measurement. The mean D(f) was 1.405 (standard deviation, 0.046; interquartile range, 1.243 to 1.542). In the multiple linear regression analysis, after controlling for gender, serum glucose, intraocular pressure, anterior chamber depth, and retinal vascular caliber, smaller D(f) was associated independently with older age (standardized regression coefficient [sβ] = -0.311; P < .001), higher mean arterial blood pressure (sβ = -0.085; P < .001), a more myopic spherical equivalent (sβ = 0.152; P < .001), and presence of cataract (sβ = -0.107; P < .001). Retinal vascular optimality score was associated significantly with higher mean arterial blood pressure (P > .001 for trend).

CONCLUSIONS

Age, blood pressure, refractive error, and lens opacity had significant influence on retinal vascular fractal measurements. A new score of retinal vascular optimality combining fractals and caliber showed strong association with blood pressure. Quantitative analysis of retinal vasculature therefore may provide additional information on microvascular architecture and optimality.

Interventional cardiologists and risk of radiation-induced cataract: results of a French multicenter observational study

BACKGROUND

Interventional cardiologists (ICs) are exposed to X-rays and may be at risk to develop cataract earlier than common senile cataract. Excess risk of posterior subcapsular cataract, known as radiation-induced, was previously observed in samples of ICs from Malaysia, and Latin America. The O'CLOC study (Occupational Cataracts and Lens Opacities in interventional Cardiology) was performed to quantify the risk at the scale of France.

METHODS

This cross-sectional multicenter study included an exposed group of ICs from different French centers and an unexposed control group of non-medical workers. Individual information was collected about cataract risk factors and past and present workload in catheterization laboratory. All participants had a clinical eye examination to classify the lens opacities (nuclear, cortical, or posterior subcapsular) with the international standard classification LOCS III.

RESULTS

The study included 106 ICs (mean age = 51 ± 7 years) and 99 unexposed control subjects (mean age = 50 ± 7 years). The groups did not differ significantly in the prevalence of either nuclear or cortical lens opacities (61% vs. 69% and 23% vs. 29%, respectively). However, posterior subcapsular lens opacities, were significantly more frequent among ICs (17% vs. 5%, p=0.006), for an OR=3.9 [1.3-11.4]. The risk increased with duration of activity but no clear relationship with workload was observed. However, the risk appeared lower for regular users of protective lead glasses (OR=2.2 [0.4-12.8]).

CONCLUSIONS

ICs, in France as elsewhere, are at high risk of posterior subcapsular cataracts. Use of protective equipment against X-rays, in particular lead glasses, is strongly recommended to limit this risk.