Topographic Measurement of the Subretinal Pigment Epithelium Space in Normal Aging and Age-Related Macular Degeneration Using High-Resolution OCT.

Purpose: A micrometer scale hyporeflective band within the retinal pigment epithelium basal lamina - Bruch's membrane complex (RPE-BL-BrM) was topographically measured in aging and age-related macular degeneration (AMD). Methods: In a prospective cross-sectional study, 90 normal eyes from 76 subjects (range = 23-90 years) and 53 dry AMD eyes from 47 subjects (range = 62-91 years) were enrolled. Isotropic volume raster scans over 6 mm × 6 mm (500 × 500 A-scans) were acquired using a high-resolution (2.7 µm axial resolution) spectral-domain optical coherence tomography (SD-OCT) prototype instrument. Six consecutive optical coherence tomography (OCT) volumes were computationally motion-corrected and fused to improve feature visibility. A boundary regression neural network was developed to measure hyporeflective band thickness. Topographic dependence was evaluated over a 6-mm-diameter Early Treatment Diabetic Retinopathy Study (ETDRS) grid. Results: The hyporeflective band thickness map (median of 4.3 µm and 7.8 µm in normal and AMD eyes, respectively) is thicker below and radially symmetric around the fovea. In normal eyes, age-associated differences occur within 0.7 to 2.3 mm from the foveal center (P < 0.05). In AMD eyes, the hyporeflective band is hypothesized to be basal laminar deposits (BLamDs) and is thicker within the 3-mm ETDRS circle (P < 0.0002) compared with normal eyes. The inner ring is the most sensitive location to detect age versus AMD-associated changes within the RPE-BL-BrM. AMD eyes with subretinal drusenoid deposits (SDDs) have a significantly thicker hyporeflective band (P < 0.001) than those without SDDs. Conclusions: The hyporeflective band is a quantifiable biomarker which differentiates AMD from aging. Longitudinal studies are warranted. The hyporeflective band may be a useful biomarker for risk stratification and disease progression.

Journey through diagnosis.

This reflective poem explores the profound human impact of receiving a serious medical diagnosis. The speaker grapples with the emotional upheaval of this sudden severing from one's presumed healthy future. There are attempts to cling to denial or bargain for a different outcome. But the truth of the diagnosis persists, sending ripples of change throughout the patient's life. Dreams slip away and plans evaporate in the crucible of illness. After a struggle, the mind makes peace and courageously leans into the difficulties ahead. The poem celebrates the human capacity to accept vulnerability, find gifts within trials, and walk the remaining road with wisdom. It reflects on how a diagnosis can heighten awareness that life is fleeting and precious. The accompanying digital artwork was generated using OpenAI's DALL·E 3 and modified using Adobe Firefly. It is a stark, black canvas, which can be seen as a metaphor for the profound and contemplative journey described patients go through. It symbolizes the inner darkness and uncertainty faced when confronting life-altering diagnoses, echoing the feelings of isolation, the search for meaning, and the gradual acceptance of a new reality as one navigates through the trials of illness.

Large Language Models in Ophthalmology: Potential and Pitfalls.

Large language models (LLMs) show great promise in assisting clinicians in general, and ophthalmology in particular, through knowledge synthesis, decision support, accelerating research, enhancing education, and improving patient interactions. Specifically, LLMs can rapidly summarize the latest literature to keep clinicians up-to-date. They can also analyze patient data to highlight crucial insights and recommend appropriate tests or referrals. LLMs can automate tedious research tasks like data cleaning and literature reviews. As AI tutors, LLMs can fill knowledge gaps and assess competency in trainees. As chatbots, they can provide empathetic, personalized responses to patient inquiries and improve satisfaction. The visual capabilities of LLMs like GPT-4 allow assisting the visually impaired by describing environments. However, there are significant ethical, technical, and legal challenges around the use of LLMs that should be addressed regarding privacy, fairness, robustness, attribution, and regulation. Ongoing oversight and refinement of models is critical to realize benefits while minimizing risks and upholding responsible AI principles. If carefully implemented, LLMs hold immense potential to push the boundaries of care, discovery, and quality of life for ophthalmology patients.

A scientometric analysis of fairness in health AI literature.

Artificial intelligence (AI) and machine learning are central components of today's medical environment. The fairness of AI, i.e. the ability of AI to be free from bias, has repeatedly come into question. This study investigates the diversity of members of academia whose scholarship poses questions about the fairness of AI. The articles that combine the topics of fairness, artificial intelligence, and medicine were selected from Pubmed, Google Scholar, and Embase using keywords. Eligibility and data extraction from the articles were done manually and cross-checked by another author for accuracy. Articles were selected for further analysis, cleaned, and organized in Microsoft Excel; spatial diagrams were generated using Public Tableau. Additional graphs were generated using Matplotlib and Seaborn. Linear and logistic regressions were conducted using Python to measure the relationship between funding status, number of citations, and the gender demographics of the authorship team. We identified 375 eligible publications, including research and review articles concerning AI and fairness in healthcare. Analysis of the bibliographic data revealed that there is an overrepresentation of authors that are white, male, and are from high-income countries, especially in the roles of first and last author. Additionally, analysis showed that papers whose authors are based in higher-income countries were more likely to be cited more often and published in higher impact journals. These findings highlight the lack of diversity among the authors in the AI fairness community whose work gains the largest readership, potentially compromising the very impartiality that the AI fairness community is working towards.

Familial retinoblastoma: variations in clinical presentation and management based on paternal versus maternal inheritance.

BACKGROUND: Several studies have demonstrated the effect of parent-of-origin on retinoblastoma penetrance. The purpose of the current study was to assess differences in clinical presentation of paternally versus maternally inherited retinoblastoma. METHODS: The clinical records of all children with familial retinoblastoma treated on a tertiary Ocular Oncology Service between December 1975 and May 2020 were reviewed retrospectively. RESULTS: A total of 179 patients with familial retinoblastoma were included. Paternal inheritance (PI) was identified in 109 (61%) patients and maternal inheritance (MI) in 70 patients (39%). A comparison (PI vs MI) revealed PI patients were older at presentation (57.2 vs 24.4 months [P = 0.002]) with no difference in patient sex (53% females vs 57% males [P = 0.606]) or number of family members affected (3.2 vs 3.0 family members [P = 0.255]). PI patients had more advanced classification according to the International Classification of Retinoblastoma (ICRB) (group E: 31% vs 8% [P = 0.012)] and greater largest tumor in basal diameter (9.0 vs 6.2 mm [P = 0.040]) and thickness (5.6 vs 4.0 mm [P = 0.038]); they were also less likely to be located in the macula (40% vs 60% [P = 0.004]). There was no difference in tumor laterality (69% vs 64% bilaterality [P = 0.530]). PI patients required enucleation more frequently (34% vs 14% [P = 0.007]). There was no difference in need for plaque radiotherapy (P = 0.86) or chemotherapy (P = 0.85). One PI patient developed metastatic retinoblastoma, and there were no retinoblastoma-related deaths. CONCLUSIONS: Patients with paternally inherited retinoblastoma presented at an older age, with larger, more peripheral tumors and more advanced ICRB group, and were more likely to require enucleation compared to those with maternally inherited retinoblastoma.

Improved Performance of ChatGPT-4 on the OKAP Examination: A Comparative Study with ChatGPT-3.5.

Introduction: This study aims to evaluate the performance of ChatGPT-4, an advanced artificial intelligence (AI) language model, on the Ophthalmology Knowledge Assessment Program (OKAP) examination compared to its predecessor, ChatGPT-3.5. Methods: Both models were tested on 180 OKAP practice questions covering various ophthalmology subject categories. Results: ChatGPT-4 significantly outperformed ChatGPT-3.5 (81% vs. 57%; p <0.001), indicating improvements in medical knowledge assessment. Discussion: The superior performance of ChatGPT-4 suggests potential applicability in ophthalmologic education and clinical decision support systems. Future research should focus on refining AI models, ensuring a balanced representation of fundamental and specialized knowledge, and determining the optimal method of integrating AI into medical education and practice.