Education in focus: Significant improvements in student learning and satisfaction with ophthalmology teaching delivered using a blended learning approach.

PURPOSE: This study aimed to measure student satisfaction with a revised ophthalmology delivery format, which due to the pandemic had previously relied on a remote online flipped classroom (OFC) format compared to a blended learning format. This educational strategy combined online learning with in-person seminars and practical patient centred sessions. Our previous investigations demonstrated a significant lack of student satisfaction with a curriculum solely reliant on a remote OFC, as such we hypothesised that a blended learning approach would result in improved levels of student satisfaction and knowledge gain. METHODS: Non-randomised intervention study of two groups; group 1 = OFC group and group 2 = BL group, compared perspectives of 4th year ophthalmology students using a validated course evaluation questionnaire (CEQ). RESULTS: A total of 59 students from the BL group (n = 257; response rate = 23.0%) and 28 from the OFC group agreed to participate in the study (n = 114; response rate = 24.6%). Participants in the BL group felt it was easier to determine the standard of work that was expected (77.42% v 60.71%) and demonstrated significantly increased satisfaction with staff motivation of students (95.16% v 64.29%, p <0.001) and provision of feedback (74.19% v 46.43%, p = 0.004), compared to the OFC group. Furthermore, students in the BL group also felt the course significantly improved their analytical skills (64.52% v 42.85%, p = 0.023) and ability to work as part of team (69.36% v 25%, p <0.001) as well as reporting reduced dissatisfaction with the level of choice afforded in terms of how they would learn (33.88% v 60.71%, p = 0.31) and the how they were assessed (59.68% v 89.28%, p = 0.004). No evidence of a statistical difference in exam score was observed. CONCLUSIONS: The COVID-19 pandemic necessitated an unavoidable pivot to online and distance learning, to meet the challenges presented by government mandates and social distancing requirements. Since many of these directives have been reversed, it is important to evaluate the effectiveness and learner perceptions' of the online and distance learning interventions. In this study we demonstrated a significant student preference for BL compared to the OFC approach, with comparable student performances determined by MCQ examinations. Our findings suggest a preference for reintroducing in-person and patient engagement activities in post-pandemic health professions education.

Neodymium-doped yttrium aluminum garnet (Nd: YAG) laser treatment in ophthalmology: a review of the most common procedures Capsulotomy and Iridotomy.

Nowadays, lasers are used in various medical fields. Ophthalmology was the first medical specialty to utilize lasers in patient treatment and still remains the leading medical field that uses laser energy for both therapeutic and diagnostic purposes. The neodymium: yttrium-aluminum-garnet (Nd: YAG) laser is one of the most common lasers used in ophthalmology. It is a solid-state laser with a wavelength of 1064 nm that works on the principle of photodisruption. Since its introduction in ophthalmology over 40 years ago, it has found various applications, mainly for procedures where cutting or disruption of ocular tissue is required. Compared to surgical alternatives, the use of Nd: YAG lasers on ocular tissue is minimally invasive. In this review, we focus on the two most common ophthalmic applications of Nd: YAG laser - laser peripheral iridotomy and posterior capsulotomy. The history of the techniques, current trends, potential complications, and the prognosis for future use is discussed.

The Versatile Teaching Eye: an affordable, 3D-printed model eye for simulating ophthalmic examination.

Purpose: To describe the Versatile Teaching Eye (VT Eye), a 3D-printed model eye designed to provide an affordable examination simulator, and to report the results of a pilot program introducing the VT Eye and an ophthalmic training curriculum at a teaching hospital in Ghana. Methods: TinkerCAD was used to design the VT Eye, which was printed with ABS plastic. The design features an adapter that permits use of a smartphone as a digital fundus. We developed a set of digital flashcards allowing for an interactive review of a range of retinal pathologies. An analog fundus was developed for practicing traditional slit lamp and indirect examinations as well as retinal laser practice. The model was used for a period of 2 weeks by ophthalmic trainees at Komfo Anokye Teaching Hospital, Kumasi, Ghana, to practice indirect ophthalmoscopy, slit lamp biomicroscopy, smartphone funduscopy, and retinal image drawing. Results were assessed at by means of a pre-/post-training survey of 6 residents. Results: The VT Eye accommodates diverse fundus examination techniques. Its 3D-printed design ensures cost-effective, high-quality replication. When paired with a 20 D practice examination lens, the digital fundus provides a comprehensive, interactive training environment for <$30.00 (USD). This device allows for indirect examination practice without requiring an indirect headset, which may increase the amount of available practice for trainees early in their careers. In the Ghana pilot program, the model's use in indirect examination training sessions significantly boosted residents' confidence in various examination techniques. Comparing pre- and post-session ratings, average reported confidence levels rose by 30% for acquiring clear views of the posterior pole, 42% for visualizing the periphery, and 141% for capturing important pathology using personal smartphones combined with a 20 D lens (all P < 0.05). Conclusions: The VT Eye is readily reproducible and can be easily integrated into ophthalmic training curricula, even in regions with limited resources. It offers an effective and affordable training solution, underscoring its potential for global adoption and the benefits of incorporating innovative technologies in medical education.

Tutorial on Biostatistics: Sample Size and Power Calculation for Ophthalmic Studies With Correlated Binary Eye Outcomes.

Purpose: To describe and demonstrate sample size and power calculation for ophthalmic studies with a binary outcome from one or both eyes. Methods: We describe sample size and power calculation for four commonly used eye designs: (1) one-eye design or person-design: one eye per subject or outcome is at person-level; (2) paired design: two eyes per subject and two eyes are in different treatment groups; (3) two-eye design: two eyes per subject and both eyes are in the same treatment group; and (4) mixture design: mixture of one eye and two eyes per subject. For each design, we demonstrate sample size and power calculations in real ophthalmic studies. Results: Using formulas and commercial or free statistical packages including SAS, STATA, R, and PS, we calculated sample size and power. We demonstrated that different statistical packages require different parameters and provide similar, yet not identical, results. We emphasize that studies using data from two eyes of a subject need to account for the intereye correlation for appropriate sample size and power calculations. We demonstrate the gain in efficiency in designs that include two eyes of a subject compared to one-eye designs. Conclusions: Ophthalmic studies use different eye designs that include one or both eyes in the same or different treatment groups. Appropriate sample size and power calculations depend on the eye design and should account for intereye correlation when two eyes from some or all subjects are included in a study. Calculations can be executed using formulas and commercial or free statistical packages.

How much can we learn from each other? Polish and Hungarian good practices in financing ophthalmology care as a proposal for implementation in Ukraine.

OBJECTIVES: The article aims to compare payment schemes for cataract, glaucoma, vitrectomy, cornea transplantations, DME, and AMD across Hungary, Poland, and Ukraine, and to identify implementable practices in Ukraine within the context of ongoing healthcare reforms. METHODS: Researchers used mixed-method research-with legal documents and data analysis on utilisation of ophthalmology services between 2010 and 2019 and in-depth semi structured interviews with fifteen health experts from Hungary, Poland, and Ukraine. Interviewees, five from each country, were representatives from healthcare providers and payers with at least 10 years' experience in ophthalmology care and knowledge about financing schemes in each country of residence. RESULTS: We identified significant differences in healthcare delivery and financing of ophthalmology services between Hungary and Poland, despite both countries rely on Diagnosis-Related Group (DRG) based systems for hospital care. Good practices for financing specific eye treatments like cataract, glaucoma, age-related macular degeneration (AMD), diabetic macular edema (DME), cornea transplantations, and vitrectomy are identified. The financing scheme, including financial products and incentives, can influence the volume of treatments. Access to ophthalmic care is a key concern, with differences in treatment schemes between Hungary (ambulatory care) and Poland (hospital care), leading to higher costs and the need for centralization of complex procedures like cornea transplantations. CONCLUSIONS: The article highlights the importance of incentivizing quality improvements and removing financial barriers in Poland, while Hungary should focus on continuous monitoring of treatment methods and flexibility in reimbursement. For Ukraine, the research findings are significant due to ongoing healthcare reform, and the country seeks optimal practices while considering the experiences of other countries.

Update and projections for New Zealand's ophthalmology workforce.

AIM: The aim of this study was to update and project the growth of ophthalmologists in New Zealand. This will help decision makers better understand the current ophthalmologist workforce and make appropriate resource allocations. METHOD: Supply and demographics of ophthalmologists in New Zealand were obtained from the Medical Council of New Zealand, Health Workforce New Zealand and Health New Zealand - Te Whatu Ora. Ophthalmology trainee numbers were extracted from the annual reports of the Royal Australian and New Zealand College of Ophthalmologists (RANZCO). New Zealand population statistics were extracted from the Stats NZ database. A simulation model was developed to project the growth of ophthalmologists from 2024 to 2050. RESULTS: In March 2023, there were 175 practising ophthalmologists in New Zealand. Overall, there were 34.0 ophthalmologists per million population, with 201.4 ophthalmologists per million for those aged ≥65 years. To maintain the current ratio, an additional 20 practising ophthalmologists are needed by 2050. CONCLUSION: The ratio of ophthalmologists per million population aged ≥65 years is projected to drop by 1.5% annually. To meet the demand of an increasing and ageing population, and RANZCO's goal of 40 ophthalmologists per million population, there needs to be an increase in ophthalmologist training positions from the current 5-year average of 6.6 to 11 new trainees annually, and a more effective distribution of the ophthalmologist workforce.

Eyesi direct ophthalmoscope simulator: an effective training tool for medical undergraduates.

INTRODUCTION: Non-ophthalmologists often lack sufficient operational training to use a direct ophthalmoscope proficiently, resulting in a global deficit of basic ophthalmological skills among general practitioners. This deficiency hampers the timely diagnosis, referral, and intervention of patients. Consequently, the optimization of teaching tools and methods to enhance teaching efficiency is imperative. This study explores the effectiveness of the Eyesi Direct Ophthalmoscope Simulator (Eyesi) as an innovative tool for fundus examination training. METHODS: Medical undergraduates were randomly assigned to Group A or B (n = 168). All participants completed a pre-training questionnaire. Group A received Eyesi training, while Group B underwent traditional direct ophthalmoscope (TDO) training. Subsequently, participants answered questionnaires relevant to their respective training methods. Both groups exchanged training tools and completed a summary questionnaire. RESULTS: After training, 54.17% of participants believed that images presented by the Eyesi were consistent with the real fundus. Group A scored significantly higher than Group B in fundus structure recognition and self-confidence in examination. The degree of mastery over fundus theory score increased from 6.10 ± 0.13 to 7.74 ± 0.16 (P < 0.001) in Group A, but Group B did not demonstrate a significant difference. We also compared undergraduates' tendencies for different learning purposes, 75.59% of participants preferred the Eyesi to TDO as a training tool, and 88.41% of participants were receptive to introducing the Eyesi in training. CONCLUSION: According to subjective participant feedback, Eyesi outperformed TDO in fundus observation, operational practice, and theoretical learning. It effectively equips undergraduates with fundus examination skills, potentially promoting the use of direct ophthalmoscopes in primary medical institutions.

Tissue Engineering and Ophthalmology.

Tissue engineering (TE) is a field of science that combines biological, engineering, and medical sciences and allows the development of disease models, drug development and gene therapy studies, and even cellular or tissue-based treatments developed by engineering methods. The eye is an organ that is easily accessible and amenable to engineering applications, paving the way for TE in ophthalmology. TE studies are being conducted on a wide range of topics, including the tear film, eyelids, cornea, optic nerve, glaucoma, and retinal diseases. With the rapid scientific advances in the field, it seems that TE is radically modifying the management of ocular disorders.

Fairer AI in ophthalmology via implicit fairness learning for mitigating sexism and ageism.

The transformative role of artificial intelligence (AI) in various fields highlights the need for it to be both accurate and fair. Biased medical AI systems pose significant potential risks to achieving fair and equitable healthcare. Here, we show an implicit fairness learning approach to build a fairer ophthalmology AI (called FairerOPTH) that mitigates sex (biological attribute) and age biases in AI diagnosis of eye diseases. Specifically, FairerOPTH incorporates the causal relationship between fundus features and eye diseases, which is relatively independent of sensitive attributes such as race, sex, and age. We demonstrate on a large and diverse collected dataset that FairerOPTH significantly outperforms several state-of-the-art approaches in terms of diagnostic accuracy and fairness for 38 eye diseases in ultra-widefield imaging and 16 eye diseases in narrow-angle imaging. This work demonstrates the significant potential of implicit fairness learning in promoting equitable treatment for patients regardless of their sex or age.

Automated detection of steps in videos of strabismus surgery using deep learning.

BACKGROUND: Learning to perform strabismus surgery is an essential aspect of ophthalmologists' surgical training. Automated classification strategy for surgical steps can improve the effectiveness of training curricula and the efficient evaluation of residents' performance. To this end, we aimed to develop and validate a deep learning (DL) model for automated detecting strabismus surgery steps in the videos. METHODS: In this study, we gathered 479 strabismus surgery videos from Shanghai Children's Hospital, affiliated to Shanghai Jiao Tong University School of Medicine, spanning July 2017 to October 2021. The videos were manually cut into 3345 clips of the eight strabismus surgical steps based on the International Council of Ophthalmology's Ophthalmology Surgical Competency Assessment Rubrics (ICO-OSCAR: strabismus). The videos dataset was randomly split by eye-level into a training (60%), validation (20%) and testing dataset (20%). We evaluated two hybrid DL algorithms: a Recurrent Neural Network (RNN) based and a Transformer-based model. The evaluation metrics included: accuracy, area under the receiver operating characteristic curve, precision, recall and F1-score. RESULTS: DL models identified the steps in video clips of strabismus surgery achieved macro-average AUC of 1.00 (95% CI 1.00-1.00) with Transformer-based model and 0.98 (95% CI 0.97-1.00) with RNN-based model, respectively. The Transformer-based model yielded a higher accuracy compared with RNN-based models (0.96 vs. 0.83, p < 0.001). In detecting different steps of strabismus surgery, the predictive ability of the Transformer-based model was better than that of the RNN. Precision ranged between 0.90 and 1 for the Transformer-based model and 0.75 to 0.94 for the RNN-based model. The f1-score ranged between 0.93 and 1 for the Transformer-based model and 0.78 to 0.92 for the RNN-based model. CONCLUSION: The DL models can automate identify video steps of strabismus surgery with high accuracy and Transformer-based algorithms show excellent performance when modeling spatiotemporal features of video frames.

Awareness of glaucoma among adult patients attending hawassa university comprehensive specialized hospital ophthalmic outpatient department, Sidama, Ethiopia, August 2022.

BACKGROUND: Due to the asymptomatic nature of the disease and lack of awareness, most glaucoma patients present for eye examination late, after significant damage of optic nerve occur. Being aware of glaucoma is important for timely diagnosis of the disease and preventing blindness from it. OBJECTIVE: The aim of this study was to assess glaucoma awareness and associated factors among adult patients aged 35 and over attending the eye outpatient department. METHODS: Hospital based cross-sectional study was conducted on 284 adult patients aged 35 and over attending ophthalmic outpatient department from July to August 2022 using systematic random sampling. An interviewer-administered questionnaire was used to collect data. The data were checked for completeness, and then entered to SPSS version 22 software. Descriptive and binary logistic regression analyses were performed. Independent variables with p-value ≤ 0.05 in multivariate logistic regression were considered as statistically significant. RESULTS: About 284 study participants, of whom 57.75% were male, participated in this study with a response rate of 94.1%. The mean age of the study participants was 53.58 years. Only 39.09%(95% CI: 36.53-41.65) were aware of glaucoma. Age groups 46-50 [AOR; 1.83: 1.18, 2.56] and 51-64 [AOR; 3.21: 2.03, 4.39], having college education or above [AOR; 3.1: 2.20, 6.64], family member with glaucoma [AOR; 5.86:3.25, 8.0], income 6500 ETB [AOR; 2.9: 1.97, 5.00] and previous eye examination [AOR; 2.15: 1.46, 4.05] were factors significantly associated with awareness of glaucoma. The main sources of information were news media, family members with glaucoma and health workers. CONCLUSION: More than half (60.91%) of adult ophthalmic patients attending HUCSH were unaware of glaucoma and need eye health education concerning glaucoma.

Hotspots and trends of artificial intelligence in the field of cataracts: a bibliometric analysis.

PURPOSE: To analyze the hotspots and trends in artificial intelligence (AI) research in the field of cataracts. METHODS: The Science Citation Index Expanded of the Web of Science Core Collection was used to collect the research literature related to AI in the field of cataracts, which was analyzed for valuable information such as years, countries/regions, journals, institutions, citations, and keywords. Visualized co-occurrence network graphs were generated through the library online analysis platform, VOSviewer, and CiteSpace tools. RESULTS: A total of 222 relevant research articles from 41 countries were selected. Since 2019, the number of related articles has increased significantly every year. China (n = 82, 24.92%), the United States (n = 55, 16.72%) and India (n = 26, 7.90%) were the three countries with the most publications, accounting for 49.54% of the total. The Journal of Cataract and Refractive Surgery (n = 13, 5.86%) and Translational Vision Science & Technology (n = 10, 4.50%) had the most publications. Sun Yat-sen University (n = 25, 11.26%), the Chinese Academy of Sciences (n = 17, 7.66%), and Capital Medical University (n = 16, 7.21%) are the three institutions with the highest number of publications. We discovered through keyword analysis that cataract, diagnosis, imaging, classification, intraocular lens, and formula are the main topics of current study. CONCLUSIONS: This study revealed the hot spots and potential trends of AI in terms of cataract diagnosis and intraocular lens power calculation. AI will become more prevalent in the field of ophthalmology in the future.

A Clinician's Guide to Sharing Data for AI in Ophthalmology.

Data is the cornerstone of using AI models, because their performance directly depends on the diversity, quantity, and quality of the data used for training. Using AI presents unique potential, particularly in medical applications that involve rich data such as ophthalmology, encompassing a variety of imaging methods, medical records, and eye-tracking data. However, sharing medical data comes with challenges because of regulatory issues and privacy concerns. This review explores traditional and nontraditional data sharing methods in medicine, focusing on previous works in ophthalmology. Traditional methods involve direct data transfer, whereas newer approaches prioritize security and privacy by sharing derived datasets, creating secure research environments, or using model-to-data strategies. We examine each method's mechanisms, variations, recent applications in ophthalmology, and their respective advantages and disadvantages. By empowering medical researchers with insights into data sharing methods and considerations, this review aims to assist informed decision-making while upholding ethical standards and patient privacy in medical AI development.