The prevalence of visually significant cataract in the Australian National Eye Health Survey

Evaluation of the Efficiency of a Digital Workflow for Cataract Planning in Patients with Astigmatism

Purpose

To evaluate the efficiency and associated costs of a digital cataract workflow system and manual cataract workflow system for patients, with astigmatism presenting for cataract surgery in Brisbane, Australia.

Patients and Methods

Sixty patients with bilateral cataract requiring toric intraocular lenses (IOL) were sequentially assigned to a manual cataract workflow (n = 30) or digital workflow (n = 30) using EQ Workplace (SW v1.7.0) running on FORUM (SW v.4.2.1.66) (Carl Zeiss Meditec, Jena, Germany). Each step of preoperative data acquisition and analysis was timed. Steps in each workflow were divided into presurgical planning time and total workflow time, the latter including the time required to input toric data into CALLISTO eye (Carl Zeiss Meditec). Secondary outcomes included staff costs within each workflow.

Results

Median presurgical planning time using a digital workflow process was 6.51 ± 0.65 minutes, and using a manual workflow process, 12.32 ± 0.56 minutes (p < 0.001). Similarly, median total workflow time using a digital workflow process was 6.93 ± 0.57 minutes and using a manual workflow process, 13.49 ± 0.47 minutes (p < 0.001). Evaluating the staff remuneration during presurgical planning and the operating costs associated with running EQ Workplace, there was a cost-reduction of 35% per patient when using the digital cataract workflow process.

Conclusion

Using a digital cataract workflow process is more efficient and provides staff cost-savings compared to a manual workflow process when planning for toric IOL implantation.

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 ).

The incidence of falls after first and second eye cataract surgery: a longitudinal cohort study

OBJECTIVE

To compare fall incidence, and visual acuity and refractive status, before surgery and after first and second eye cataract surgery.

DESIGN, SETTING

Prospective observational study in eight tertiary referral ophthalmology clinics in public hospitals in Sydney, Melbourne, and Perth.

PARTICIPANTS

People aged 65 years or more referred for bilateral age-related cataract surgery during 2013-16, followed for maximum of 24 months after study entry or until six months after second eye surgery, whichever was shorter.

MAIN OUTCOME MEASURES

Primary outcome: age- and sex-adjusted incidence of falls.

SECONDARY OUTCOMES

visual acuity and refractive error.

RESULTS

The mean age of the 409 included participants was 75.4 years (SD, 5.4 years); 220 were women (54%). Age- and sex-adjusted fall incidence prior to surgery was 1.17 (95% CI, 0.95-1.43) per year, 0.81 (95% CI, 0.63-1.04) per year after first eye surgery, and 0.41 (95% CI, 0.29-0.57) per year after second eye surgery. For the 118 participants who underwent second eye surgery and participated in all follow-up visits, age- and sex-adjusted incidence before (0.80 [95% CI, 0.55-1.15] falls per year) and after first eye surgery (0.81 [95% CI, 0.57-1.15] falls per year) was similar, but was lower after second eye surgery (0.32 [95% CI 0.21-0.50] falls per year). Mean habitual binocular visual acuity (logMAR) was 0.32 (SD, 0.21) before surgery, 0.15 (SD, 0.17) after first eye surgery, and 0.07 (SD, 0.15) after second eye surgery.

CONCLUSIONS

First eye surgery substantially improves vision in older people with cataract, but second eye surgery is required to minimise fall incidence. Timely cataract surgery for both eyes not only optimises vision in older people with cataract, but also reduces their risk of injury from falls.

Considerations for Training and Workforce Development to Enhance Rural and Remote Ophthalmology Practise in Australia: A Scoping Review

Australia has one of the lowest per capita numbers of ophthalmologists among OECD countries, and they predominantly practise in metropolitan centres of the country. Increasing the size and distribution of the ophthalmology workforce is of critical importance. The objective of this review was to investigate the context of rural ophthalmology training and practise in Australia and how they relate to future ophthalmology workforce development. This scoping review was informed by Arksey and O’Malley’s framework and the methodology described by Coloqhuon et al. The search yielded 428 articles, of which 261 were screened for eligibility. Following the screening, a total of 75 articles were included in the study. Themes identified relating to rural ophthalmology training and practise included: Indigenous eye health; access and utilisation of ophthalmology-related services; service delivery models for ophthalmic care; ophthalmology workforce demographics; and ophthalmology workforce education and training for rural and remote practise. With an anticipated undersupply and maldistribution of ophthalmologists in the coming decade, efforts to improve training must focus on how to build a sizeable, fit-for-purpose workforce to address eye health needs across Australia. More research focusing on ophthalmology workforce distribution is needed to help identify evidence-based solutions for workforce maldistribution. Several strategies to better prepare the future ophthalmology workforce for rural practise were identified, including incorporating telehealth into ophthalmology training settings; collaborating with other health workers, especially optometrists and specialist nurses in eyecare delivery; and exposing trainees to more patients of Indigenous background.

Visually significant cataract and associated factors among older people attending a community ophthalmic service in central Gondar Zone, Northwest Ethiopia: a cross-sectional study

Objective

In this study, we aimed to determine the prevalence of visually significant cataract and associated factors among older people attending community ophthalmic services in the central Gondar Zone, Northwest Ethiopia.

Methods

We conducted a community outreach-based cross-sectional study among adults aged ≥40 years in the central Gondar Zone from 30 May to 15 June 2021. We used systematic random sampling to select study participants. Interviewer-administered questionnaires and ocular examinations were performed and the data recorded. We applied binary logistic regression to identify factors associated with visually significant cataract.

Results

A total of 821 participants were included, with median age 57 years. The prevalence of visually significant cataract was 29.1% (95% CI: 26.1–32.0). Age ≥80 years (adjusted odds ratio [AOR] = 16.9; 95% CI: 7.5–38.4), rural residence (AOR = 1.7; 95% CI: 1.02–2.7), unmarried status (AOR = 1.9; 95% CI: 1.2–3.2), illiteracy (AOR = 2.9; 95% CI: 1.4–6.1), unemployed status (AOR = 1.7; 95% CI: 1.1–2.7), and sunlight exposure ≥5 hours per day (AOR = 1.6; 95% CI: 1.04–2.4) were significantly associated with visually significant cataract.

Conclusion

In this study, visually significant cataract was found to be high, which requires immediate public health intervention.

Detecting visually significant cataract using retinal photograph-based deep learning

Age-related cataracts are the leading cause of visual impairment among older adults. Many significant cases remain undiagnosed or neglected in communities, due to limited availability or accessibility to cataract screening. In the present study, we report the development and validation of a retinal photograph-based, deep-learning algorithm for automated detection of visually significant cataracts, using more than 25,000 images from population-based studies. In the internal test set, the area under the receiver operating characteristic curve (AUROC) was 96.6%. External testing performed across three studies showed AUROCs of 91.6-96.5%. In a separate test set of 186 eyes, we further compared the algorithm's performance with 4 ophthalmologists' evaluations. The algorithm performed comparably, if not being slightly more superior (sensitivity of 93.3% versus 51.7-96.6% by ophthalmologists and specificity of 99.0% versus 90.7-97.9% by ophthalmologists). Our findings show the potential of a retinal photograph-based screening tool for visually significant cataracts among older adults, providing more appropriate referrals to tertiary eye centers.

Prevalence rates of cataract and cataract surgery in elderly Chinese people living in suburban Shanghai: the Pujiang Cataract Cohort Study

PURPOSE

To estimate the prevalence rates of cataract and cataract surgery in suburban Shanghai, China.

METHODS

This population-based, cross-sectional study was carried out in Pujiang Town, Shanghai, China, using a random cluster sampling strategy. A total of 5846 participants (11,657 eyes) aged ≥65 years were enrolled. Detailed eye examinations included presenting visual acuity (VA), best-corrected VA, non-contact tonometry, measurement of ocular parameters using IOLMaster 700, slit-lamp assessment of lens opacities using the Lens Opacities Classification System III and dilated fundus evaluation.

RESULTS

Cataract was present in 57.0% of participants (53.8% of eyes). Cortical, nuclear and posterior subcapsular cataract (PSC) were found in 41.3%, 41.0% and 12.6% of participants. Among participants with any cataract, VA was <20/40 in 32.5%. According to Global Burden of Disease Study visual impairment (VI) criteria, 52.5% of participants with PSC had VI, 4.9% were considered blind (VA <20/400) and 31.9% had moderate VI (VA ≥20/400 to <20/63). This rate was significantly greater than that in participants with cortical (32.0%) or nuclear (38.0%) cataract (P<0.05). Cataract surgery was performed in 8.1% of eyes (men, 3.1%; women, 5.0%) or 10.9% of participants (men, 4.2%; women, 6.7%).

CONCLUSIONS

The Pujiang Cataract Cohort Study has revealed the prevalence rates of cataract and cataract surgery among elderly individuals in suburban Shanghai, China. Although the frequency of cataract surgery has increased in China in recent decades, the high prevalence of cataract-related VI among older people suggests further attention to primary eyecare and medical awareness is necessary.

Distribution and Location Stability of the Australian Ophthalmology Workforce: 2014-2019

OBJECTIVE

To investigate the ophthalmology workforce distribution and location stability using Modified Monash Model category of remoteness.

METHODS

Whole of ophthalmologist workforce analysis using Australian Health Practitioner Registration Agency (AHPRA) data. Modified Monash Model (MMM) category was mapped to postcode of primary work location over a six-year period (2014 to 2019). MMM stability was investigated using survival analysis and competing risks regression.

DESIGN

Retrospective cohort study.

SETTING

Australia.

PARTICIPANTS

Ophthalmologists registered with AHPRA.

MAIN OUTCOME MEASURES

Retention within MMM category of primary work location.

RESULTS

A total of 948 ophthalmologists were identified (767 males, 181 females). Survival estimates indicate 84% of ophthalmologists remained working in MMM1, while 79% of ophthalmologists working in MMM2-MMM7remained in these regions during the six-year period.

CONCLUSION

The Australian ophthalmology workforce shows a high level of location stability and is concentrated in metropolitan areas of Australia. Investment in policy initiatives designed to train, recruit and retain ophthalmologists in regional, rural and remote areas is needed to improve workforce distribution outside of metropolitan areas.

Artificial Intelligence for Cataract Detection and Management

The rising popularity of artificial intelligence (AI) in ophthalmology is fuelled by the ever-increasing clinical "big data" that can be used for algorithm development. Cataract is one of the leading causes of visual impairment worldwide. However, compared with other major age-related eye diseases, such as diabetic retinopathy, age-related macular degeneration, and glaucoma, AI development in the domain of cataract is still relatively underexplored. In this regard, several previous studies explored algorithms for automated cataract assessment using either slit lamp of color fundus photographs. However, several other study groups proposed or derived new AI-based calculation for pre-cataract surgery intraocular lens power. Along with advancements in digitization of clinical data, data curation for future cataract-related AI developmental work is bound to undergo significant improvements in the foreseeable future. Even though most of these previous studies reported early promising performances, limitations such as lack of robust, high-quality training data, and lack of external validations remain. In the next phase of work, apart from algorithm's performance, it will also be pertinent to evaluate deployment angles, feasibility, efficiency, and cost-effectiveness of these new cataract-related AI systems.