A survey exploring ophthalmologists' attitudes and beliefs in performing immediate sequential bilateral cataract surgery in Singapore.

BACKGROUND: To evaluate current practice patterns of Immediate Sequential Bilateral Cataract Surgery (ISBCS) by ophthalmologists in Singapore and assess their attitudes towards performing ISBCS in future cataract care. METHODS: An anonymised electronic survey, modified to local context from a similar study conducted in the United Kingdom, was distributed to members of the College of Ophthalmologists, Academy of Medicine, Singapore, from 20 June to 1 September 2023. An initial screening question on prior experience with ISBCS directed the rest of the survey. Questions explored ophthalmologists' current ISBCS practice patterns and the importance of factors affecting their willingness to perform ISBCS. Results were descriptively analysed. RESULTS: Results collated 2 months upon survey dissemination saw a total of 58 respondents from 235 eligible members (24.7% response rate). Of these, 16 (27.6%) were currently performing ISBCS, 37 (63.8%) had never performed, and 5 (8.6%) had stopped performing. In considering ISBCS, patient convenience (n = 11, 68.8%) and reduced hospital visits (n = 8, 50.0%) were the most important factors nominated. The most important barriers to performing ISBCS were medico-legal issues (n = 31, 83.8%) and risk of endophthalmitis (n = 27, 73.0%), followed by perceived lack of evidence for its effectiveness (n = 19, 51.4%). CONCLUSION: This is one of the first studies evaluating ophthalmologists' sentiments towards performing ISBCS in an Asian country. It highlights some of the most pertinent barriers and concerns that ophthalmologists face in performing and offering ISBCS. This study provides a better understanding of the potential role and prospects of ISBCS in future cataract care in Singapore.

Ocular image-based deep learning for predicting refractive error: A systematic review.

Background: Uncorrected refractive error is a major cause of vision impairment worldwide and its increasing prevalent necessitates effective screening and management strategies. Meanwhile, deep learning, a subset of Artificial Intelligence, has significantly advanced ophthalmological diagnostics by automating tasks that required extensive clinical expertise. Although recent studies have investigated the use of deep learning models for refractive power detection through various imaging techniques, a comprehensive systematic review on this topic is has yet be done. This review aims to summarise and evaluate the performance of ocular image-based deep learning models in predicting refractive errors. Main text: We search on three databases (PubMed, Scopus, Web of Science) up till June 2023, focusing on deep learning applications in detecting refractive error from ocular images. We included studies that had reported refractive error outcomes, regardless of publication years. We systematically extracted and evaluated the continuous outcomes (sphere, SE, cylinder) and categorical outcomes (myopia), ground truth measurements, ocular imaging modalities, deep learning models, and performance metrics, adhering to PRISMA guidelines. Nine studies were identified and categorised into three groups: retinal photo-based (n â€‹= â€‹5), OCT-based (n â€‹= â€‹1), and external ocular photo-based (n â€‹= â€‹3).For high myopia prediction, retinal photo-based models achieved AUC between 0.91 and 0.98, sensitivity levels between 85.10% and 97.80%, and specificity levels between 76.40% and 94.50%. For continuous prediction, retinal photo-based models reported MAE ranging from 0.31D to 2.19D, and R 2 between 0.05 and 0.96. The OCT-based model achieved an AUC of 0.79-0.81, sensitivity of 82.30% and 87.20% and specificity of 61.70%-68.90%. For external ocular photo-based models, the AUC ranged from 0.91 to 0.99, sensitivity of 81.13%-84.00% and specificity of 74.00%-86.42%, MAE ranges from 0.07D to 0.18D and accuracy ranges from 81.60% to 96.70%. The reported papers collectively showed promising performances, in particular the retinal photo-based and external eye photo -based DL models. Conclusions: The integration of deep learning model and ocular imaging for refractive error detection appear promising. However, their real-world clinical utility in current screening workflow have yet been evaluated and would require thoughtful consideration in design and implementation.

Popular large language model chatbots' accuracy, comprehensiveness, and self-awareness in answering ocular symptom queries.

In light of growing interest in using emerging large language models (LLMs) for self-diagnosis, we systematically assessed the performance of ChatGPT-3.5, ChatGPT-4.0, and Google Bard in delivering proficient responses to 37 common inquiries regarding ocular symptoms. Responses were masked, randomly shuffled, and then graded by three consultant-level ophthalmologists for accuracy (poor, borderline, good) and comprehensiveness. Additionally, we evaluated the self-awareness capabilities (ability to self-check and self-correct) of the LLM-Chatbots. 89.2% of ChatGPT-4.0 responses were 'good'-rated, outperforming ChatGPT-3.5 (59.5%) and Google Bard (40.5%) significantly (all p < 0.001). All three LLM-Chatbots showed optimal mean comprehensiveness scores as well (ranging from 4.6 to 4.7 out of 5). However, they exhibited subpar to moderate self-awareness capabilities. Our study underscores the potential of ChatGPT-4.0 in delivering accurate and comprehensive responses to ocular symptom inquiries. Future rigorous validation of their performance is crucial to ensure their reliability and appropriateness for actual clinical use.

Axial length elongation profiles from 3 to 6 years in an Asian paediatric population: the Growing Up in Singapore Towards Healthy Outcomes birth cohort study (GUSTO).

AIMS: To determine axial length (AL) elongation profiles in children aged 3-6 years in an Asian population. METHODS: Eligible subjects were recruited from the Growing Up in Singapore Towards Healthy Outcomes birth cohort. AL measurement was performed using IOLMaster (Carl Zeiss Meditec, Jena, Germany) at 3 and 6 years. Anthropometric measurements at birth, cycloplegic refraction at 3 and 6 years, questionnaires on the children's behavioural habits at 2 years and parental spherical equivalent refraction were performed. Multivariable linear regression model with generalised estimating equation was performed to determine factors associated with AL elongation. RESULTS: 273 eyes of 194 children were included. The mean AL increased from 21.72±0.59 mm at 3 years to 22.52±0.66 mm at 6 years (p<0.001). Myopic eyes at 6 years had greater AL elongation (1.02±0.34 mm) compared with emmetropic eyes (0.85±0.25 mm, p=0.008) and hyperopic eyes (0.74±0.16 mm, p<0.001). The 95th percentile limit of AL elongation was 1.59 mm in myopes, 1.34 mm in emmetropes and 1.00 mm in hyperopes. Greater birth weight (per 100 g, ß=0.010, p=0.02) was significantly associated with greater AL elongation from 3 to 6 years, while parental and other behavioural factors assessed at 2 years were not (all p≥0.08). CONCLUSION: In this preschool cohort, AL elongates at an average length of 0.80 mm from 3 to 6 years, with myopes demonstrating the greatest elongation. The differences in 95th percentile limits for AL elongation between myopes, emmetropes and hyperopes can be valuable information in identifying myopia development in preschool children.

Benchmarking large language models' performances for myopia care: a comparative analysis of ChatGPT-3.5, ChatGPT-4.0, and Google Bard.

BACKGROUND: Large language models (LLMs) are garnering wide interest due to their human-like and contextually relevant responses. However, LLMs' accuracy across specific medical domains has yet been thoroughly evaluated. Myopia is a frequent topic which patients and parents commonly seek information online. Our study evaluated the performance of three LLMs namely ChatGPT-3.5, ChatGPT-4.0, and Google Bard, in delivering accurate responses to common myopia-related queries. METHODS: We curated thirty-one commonly asked myopia care-related questions, which were categorised into six domains-pathogenesis, risk factors, clinical presentation, diagnosis, treatment and prevention, and prognosis. Each question was posed to the LLMs, and their responses were independently graded by three consultant-level paediatric ophthalmologists on a three-point accuracy scale (poor, borderline, good). A majority consensus approach was used to determine the final rating for each response. 'Good' rated responses were further evaluated for comprehensiveness on a five-point scale. Conversely, 'poor' rated responses were further prompted for self-correction and then re-evaluated for accuracy. FINDINGS: ChatGPT-4.0 demonstrated superior accuracy, with 80.6% of responses rated as 'good', compared to 61.3% in ChatGPT-3.5 and 54.8% in Google Bard (Pearson's chi-squared test, all p ≤ 0.009). All three LLM-Chatbots showed high mean comprehensiveness scores (Google Bard: 4.35; ChatGPT-4.0: 4.23; ChatGPT-3.5: 4.11, out of a maximum score of 5). All LLM-Chatbots also demonstrated substantial self-correction capabilities: 66.7% (2 in 3) of ChatGPT-4.0's, 40% (2 in 5) of ChatGPT-3.5's, and 60% (3 in 5) of Google Bard's responses improved after self-correction. The LLM-Chatbots performed consistently across domains, except for 'treatment and prevention'. However, ChatGPT-4.0 still performed superiorly in this domain, receiving 70% 'good' ratings, compared to 40% in ChatGPT-3.5 and 45% in Google Bard (Pearson's chi-squared test, all p ≤ 0.001). INTERPRETATION: Our findings underscore the potential of LLMs, particularly ChatGPT-4.0, for delivering accurate and comprehensive responses to myopia-related queries. Continuous strategies and evaluations to improve LLMs' accuracy remain crucial. FUNDING: Dr Yih-Chung Tham was supported by the National Medical Research Council of Singapore (NMRC/MOH/HCSAINV21nov-0001).

Areas and factors associated with patients' dissatisfaction with glaucoma care.

OBJECTIVE: The aim of this study was to evaluate patients' dissatisfaction with overall and specific aspects of a tertiary glaucoma service and to determine their independent factors, including intraocular pressure (IOP) and visual acuity (VA). METHODS: Patients, aged ≥21 years, from a specialist glaucoma service in a tertiary eye hospital in Singapore for at least 6 months, were recruited for this cross-sectional study between March and June 2014. All consenting patients completed a 7-area glaucoma-specific satisfaction questionnaire and one item related to satisfaction with overall glaucoma care. We determined the top three areas of dissatisfaction and overall dissatisfaction with the glaucoma service. We also explored the independent factors associated with overall and specific areas of patients' dissatisfaction with their glaucoma care, including VA and IOP by using logistic regression models. RESULTS: Of the 518 patients recruited, 438 (84.6%) patients completed the study. Patients' dissatisfaction with the overall glaucoma service was 7.5%. The three areas of glaucoma service with the highest dissatisfaction rates were as follows: 1) explanation of test results (24.8%); 2) explanation of glaucoma complications (23.7%); and 3) advice on managing glaucoma (23.5%). Patients who were dissatisfied with the overall service had a worse mean VA compared with satisfied patients (logarithm of the minimum angle of resolution =0.41±0.43 vs 0.27±0.49, p=0.005), whereas mean IOP remained well-controlled in both the groups (13.55±2.46 mmHg vs 14.82±2.86 mmHg, p=0.014). In adjusted models, factors associated with overall dissatisfaction with glaucoma care included a pre-university education and above (odds ratio [OR] =8.06, 95% CI =1.57-41.27) and lower IOP (OR =0.83, 95% CI =0.71-0.98). CONCLUSION: Although less than one tenth of glaucoma patients were dissatisfied with the overall glaucoma service, one in four patients were dissatisfied with three specific aspects of care. A lower IOP, ironically, and education level were associated with overall dissatisfaction. Improving patients' understanding of glaucoma test results, glaucoma complications, and disease management may increase patient satisfaction levels.