Ophthalmology science最新文献

{"title":"Mean Arteriolar Diameter Measured from Wide-Field Swept-Source OCT Angiography: A Highly Sensitive Indicator for Mean Arterial Pressure","authors":"Yuchen Li ,&nbsp;Yuyao Qu ,&nbsp;Hanze Zhang ,&nbsp;Yue Bian ,&nbsp;Langxuan Yuan ,&nbsp;Jingbo Hu ,&nbsp;Shengrui Xu ,&nbsp;Xiayu Xu PhD ,&nbsp;Jianqin Lei MD","doi":"10.1016/j.xops.2024.100679","DOIUrl":"10.1016/j.xops.2024.100679","url":null,"abstract":"<div><h3>Purpose</h3><div>To investigate the metrics of retinal arterioles and venules based on the en face images of the superficial retina using wide-field swept-source OCT angiography (SSOCTA) and assess their relationships with specific systemic parameters in a normal sample.</div></div><div><h3>Design</h3><div>Cross-sectional study.</div></div><div><h3>Participants</h3><div>Normal volunteers with no history of hypertension, diabetes, or significant eye diseases were recruited at the outpatient clinic of the First Affiliated Hospital of Xi’an Jiaotong University. Eligible eyes had a corrected visual acuity of 20/25 or better and an intraocular pressure (IOP) not exceeding 21 mmHg.</div></div><div><h3>Methods</h3><div>The SSOCTA scans of 24 × 20 mm in size were acquired from both eyes of each participant, along with measurements of axial length (AL), IOP, real-time mean arterial pressure (MAP), and body mass index (BMI). Retinal arterioles and venules were manually segmented and distinguished from the disc margin to their capillary junctions in the superficial retinal layer. Image processing and calculations were performed using ImageJ.</div></div><div><h3>Main Outcome Measurements</h3><div>The averaged caliber and fractal dimension of arteries and veins, as well as the arteriovenous ratio, were calculated from binarized vascular images. Generalized linear mixed models were used to test correlations between systemic parameters and these metrics.</div></div><div><h3>Results</h3><div>A total of 54 eyes from 44 participants were included after screening. Univariate analysis revealed a significant negative impact of MAP on averaged caliber of retinal artery (B = −0.003, <em>P</em> = 0.004) and averaged caliber of retinal vein (ACvein) (B = −0.002, <em>P</em> = 0.045), whereas age, sex, BMI, AL, and IOP showed no significant impact on these metrics. Variations among graders significantly influenced most metrics; however, real-time MAP continued to predict retinal arteriolar caliber after adjusting for graders (B = −0.002, <em>P</em> = 0.03).</div></div><div><h3>Conclusions</h3><div>The mean arteriolar diameter measured using wide-field SSOCTA could serve as a highly sensitive indicator of systemic vascular changes.</div></div><div><h3>Financial Disclosure(s)</h3><div>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</div></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"5 3","pages":"Article 100679"},"PeriodicalIF":3.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal Links between Gut Microbiota, Blood Metabolites, Immune Cells, Inflammatory Proteins, and Myopia: A Mendelian Randomization Study","authors":"Huibin Lv MD ,&nbsp;Zhenyu Wang MD ,&nbsp;Chen Huang MD ,&nbsp;Xiaotong Yu MS ,&nbsp;Xuemin Li MD ,&nbsp;Xudong Song MD","doi":"10.1016/j.xops.2024.100684","DOIUrl":"10.1016/j.xops.2024.100684","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Purpose&lt;/h3&gt;&lt;div&gt;This study aimed to investigate causal relationships between gut microbiota, blood metabolites, immune cell traits, circulating inflammatory proteins, and myopia through Mendelian randomization (MR) analysis.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Design&lt;/h3&gt;&lt;div&gt;Mendelian randomization study.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Subjects&lt;/h3&gt;&lt;div&gt;Genome-wide association study (GWAS) data of 412 gut microbiota, 1400 blood metabolites/metabolite ratios, 731 immune cell traits, and 91 circulating inflammatory proteins from the public GWAS database. Genome-wide association study data of myopia from the public GWAS database and FinnGen consortium.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;Two-sample MR analysis and meta-analysis were employed using 4 methods, with inverse-variance weighted as the primary approach, to investigate potential causal links. Metabolic pathway analysis was conducted to explore metabolic pathways. The Cochran Q-test, MR-Egger intercept test, and MR-PRESSO were used for sensitivity analyses. Mediation and reverse MR analyses were also carried out to identify potential mediation relationships and modification effects of myopia.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Main Outcome Measures&lt;/h3&gt;&lt;div&gt;Causal relationships between gut microbiota, blood metabolites, immune cell traits, circulating inflammatory proteins, and myopia.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;We identified causal effects of 34 and 22 gut microbiota/bacterial pathways, 131 and 98 blood metabolites/metabolite ratios, 60 and 37 immune cell traits, and 5 and 2 circulating inflammatory proteins on myopia (ukb-b-6353 and R10_H7_MYOPIA, respectively). Overlapping causal relationships were found for 1 gut bacterial pathway, 10 blood metabolites/metabolite ratios, and 2 immune cell traits across both outcomes; however, none of these overlaps reached significance after meta-analysis. The Small Molecule Pathway Database and Kyoto Encyclopedia of Genes and Genomes database enriched 14 significant pathways. Flavin adenine dinucleotide was involved in 8 pathways in both databases. Furthermore, the causal effect of glycochenodeoxycholate glucuronide on myopia was mediated by acetyl-CoA fermentation to butanoate lI, with mediation proportion of 19.03% (ukb-b-6353) and 19.48% (R10_H7_MYOPIA). Reverse MR analysis identified modification effects of myopia (ukb-b-6353) on gut microbiota, blood metabolites, and circulating inflammatory proteins.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;These findings demonstrated significant causal relationships between gut microbiota, blood metabolites, immune cell traits, circulating inflammatory proteins, and myopia. Gut microbiota pathway may mediate the causal effects of blood metabolite on myopia. This may provide researchers with a new perspective in exploring the biological mechanisms of myopia and may lead to the exploration of earlier treatment strategies.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Financial Disclosure(s)&lt;/h3&gt;&lt;div&gt;The author(s) have no proprietary or commercial interest in a","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"5 4","pages":"Article 100684"},"PeriodicalIF":3.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning on Prediagnostic Metabolite Data Identifies Etiologic Endotypes of Exfoliation Glaucoma in United States Health Professionals","authors":"Akiko Hanyuda MD, MPH ,&nbsp;Oana A. Zeleznik PhD ,&nbsp;Yoshihiko Raita MD, MPH ,&nbsp;Kazuno Negishi MD, PhD ,&nbsp;Louis R. Pasquale MD ,&nbsp;Jessica Lasky-Su ScD ,&nbsp;Janey L. Wiggs MD, PhD ,&nbsp;Jae H. Kang ScD","doi":"10.1016/j.xops.2024.100678","DOIUrl":"10.1016/j.xops.2024.100678","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Purpose&lt;/h3&gt;&lt;div&gt;Exfoliation glaucoma (XFG) etiology is poorly understood. Metabolomics-based etiologic endotypes of XFG may provide novel etiologic insights. We aimed to use unsupervised machine learning on prediagnostic plasma metabolites to characterize etiologic XFG endotypes.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Design&lt;/h3&gt;&lt;div&gt;Prospective case-only analysis.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Participants&lt;/h3&gt;&lt;div&gt;Among Nurses' Health Study and Health Professionals Follow-up Study participants, 205 (174 female and 31 male) incident XFG cases diagnosed with an average of 11.8 years following blood collection (1989–1995) were included.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;We identified and confirmed incident cases of XFG or XFG suspect (collectively called “XFG” henceforth) through 2016 with medical record review. Liquid chromatography-mass spectrometry was used to profile 341 plasma metabolites. After preprocessing prediagnostic metabolites with adjustment for season, time of blood draw, and fasting status, we computed a distance matrix using Pearson distance and computed gap statistics to identify distinct endotypes.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Main Outcome Measures&lt;/h3&gt;&lt;div&gt;Metabolomics-based XFG etiologic endotypes. Metabolomic profiles were compared across endotypes; false discovery rate (FDR) was used to account for multiple comparisons in Metabolite Set Enrichment Analyses. Exfoliation glaucoma environmental risk factors (e.g., lifetime ultraviolet (UV) exposure, folate consumption), a genetic risk score incorporating 8 major single nucleotide polymorphisms for exfoliation syndrome, and clinical presentations were compared across endotypes.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;We identified 3 distinct XFG metabolomic endotypes. Compared with the most common endotype 2 (reference group [n = 90; 43.9%]), endotype 1 (n = 56; 27.3%) tended to include more male southern US residents with greater UV exposure and were the least likely to have cardiovascular disease; among women, a higher percentage were postmenopausal. Endotype 3 (n = 59; 28.8%) was associated with being a male northern US resident; a higher prevalence of cardiovascular disease and risk factors such as higher body mass index, diabetes, hypertension, and dyslipidemia; and the lowest genetic susceptibility score. There were no differences in ophthalmic characteristics (e.g., maximum intraocular pressure, bilaterality, age at diagnosis) across endotypes (&lt;em&gt;P&lt;/em&gt; ≥ 0.6). In metabolite class analyses, compared with endotype 2, organic acids and carnitines were positively associated with endotype 1, whereas diacylglycerols and triacylglycerols were positively associated with endotype 3 (FDR &lt;0.05).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;Integrated metabolomic profiling can identify distinct XFG etiologic endotypes, suggesting different pathobiological mechanisms.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Financial Disclosure(s)&lt;/h3&gt;&lt;div&gt;Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the ","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"5 3","pages":"Article 100678"},"PeriodicalIF":3.2,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patients with Age-related Macular Degeneration Have Increased Susceptibility to Valvular Heart Disease","authors":"Natan Lishinsky-Fischer,&nbsp;Itay Chowers MD, PhD,&nbsp;Yahel Shwartz MSc,&nbsp;Jaime Levy MD","doi":"10.1016/j.xops.2024.100677","DOIUrl":"10.1016/j.xops.2024.100677","url":null,"abstract":"<div><h3>Purpose</h3><div>Valvular heart disease (VHD) contributes significantly to cardiovascular-related morbidity worldwide. Aortic valve stenosis is the third most common cardiovascular disease in the Western world, after hypertension and coronary artery disease. Recent studies have reported an association between VHD and the presence of subretinal drusenoid deposits (SDDs), a distinct manifestation of age-related macular degeneration (AMD). However, these findings were based on self-reported questionnaires and relatively modest cohort sizes. Our goal was therefore to investigate the putative associations between AMD and VHD and between the presence of SDDs and VHD.</div></div><div><h3>Design</h3><div>Retrospective case-control study.</div></div><div><h3>Subjects</h3><div>A total of 945 with AMD and 8275 control patients without AMD from a single tertiary center.</div></div><div><h3>Methods</h3><div>All patients with AMD underwent spectral-domain OCT (SD-OCT). The SD-OCT scans were annotated by 2 experienced graders. Among the patients with AMD, 547 had drusen and SDDs, and 398 had drusen only with no SDDs. We also extracted data from all 9220 patients’ electronic medical records, including demographics and previous heart valve procedures based on International Classification of Diseases, ninth revision codes.</div></div><div><h3>Main Outcome Measures</h3><div>Heart valve-related diagnoses and procedures performed in both patient groups.</div></div><div><h3>Results</h3><div>Patients with AMD had a higher prevalence of various VHDs compared with the control group, including increased rates of aortic stenosis (odds ratio [OR], 2.00; 95% confidence interval (CI), 1.40–2.86; <em>P</em> &lt; 0.001), aortic regurgitation (OR, 2.41; 95% CI, 1.49–3.91; <em>P</em> &lt; 0.001), and mitral valve regurgitation (OR, 1.51; 95% CI, 1.13–2.01; <em>P</em> = 0.004). Heart valve procedures were also more prevalent among AMD patients including aortic valve replacement (OR, 1.70; 95% CI, 1.08–2.66; <em>P</em> = 0.019) and tricuspid valve replacement (OR, 3.99; 95% CI, 1.03–15.46; <em>P =</em> 0.03). Moreover, a supervised machine learning model successfully detected the presence of AMD based solemnly on the patient’s history of VHD. In the AMD cohort, we found no significant difference in VHD prevalence between patients with nonneovascular AMD and patients with neovascular AMD, or between patients with SDDs and patients without SDDs.</div></div><div><h3>Conclusions</h3><div>Patients with AMD have a higher prevalence of VHD and are more likely to undergo a heart valve-related procedure compared with patients without AMD, with no difference between patients with SDDs and patients without SDDs in the AMD cohort.</div></div><div><h3>Financial Disclosure(s)</h3><div>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</div></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"5 2","pages":"Article 100677"},"PeriodicalIF":3.2,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11787562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143082441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Cone Photoreceptor Detection in Adaptive Optics Flood Illumination Ophthalmoscopy","authors":"Sander Wooning ,&nbsp;Pam A.T. Heutinck MD ,&nbsp;Kubra Liman ,&nbsp;Sem Hennekam ,&nbsp;Manon van Haute ,&nbsp;Filip van den Broeck MD ,&nbsp;Bart Leroy PhD, MD ,&nbsp;Danuta M. Sampson PhD ,&nbsp;Danial Roshandel MD, PhD ,&nbsp;Fred K. Chen MD, PhD ,&nbsp;Daniel M. Pelt PhD ,&nbsp;L. Ingeborgh van den Born MD, PhD ,&nbsp;Virginie J.M. Verhoeven MD, PhD ,&nbsp;Caroline C.W. Klaver MD, PhD ,&nbsp;Alberta A.H.J. Thiadens MD, PhD ,&nbsp;Marine Durand ,&nbsp;Nicolas Chateau ,&nbsp;Theo van Walsum PhD ,&nbsp;Danilo Andrade De Jesus PhD ,&nbsp;Luisa Sanchez Brea PhD","doi":"10.1016/j.xops.2024.100675","DOIUrl":"10.1016/j.xops.2024.100675","url":null,"abstract":"<div><h3>Purpose</h3><div>To develop and validate a deep learning–based model for detecting cone photoreceptor cells in adaptive optics flood illumination ophthalmoscopy (AO-FIO).</div></div><div><h3>Design</h3><div>Healthy volunteer study.</div></div><div><h3>Participants</h3><div>A total of 36 healthy participants were included.</div></div><div><h3>Methods</h3><div>The imaging protocol consisted of 21 AO-FIO images per eye acquired with the rtx1 adaptive optics retinal camera (Imagine Eyes), 4° × 4° each with 2° overlap, imaging a retinal patch 4° nasal (N) to 12° temporal (T) and −5° inferior to 5° superior relative to the fovea. Each image was divided into patches of 128 × 128 pixels, with a 20-pixel overlap. A training set (625 patches) from 18 subjects (32 ± 12 years, 6 males and 12 females) was annotated by a single center, whereas the test set (54 patches) from 18 subjects (40 ± 16 years, 11 males and 7 females) was annotated by graders from 3 different institutions. The deep learning model, based on the U-Net architecture, underwent a parameter search using the tree-structured Parzen estimator.</div></div><div><h3>Main Outcome Measures</h3><div>The F1 score was used to determine both intragrader and intergrader agreements and to evaluate the model’s performance compared with the automated detection by the manufacturer’s software (AOdetect Mosaic).</div></div><div><h3>Results</h3><div>The average intragrader agreement was 0.85 ± 0.06 between 2°N and 2°T, followed by 0.83 ± 0.09 between 3 and 6°T, and 0.80 ± 0.10 between 7 and 10°T. The average intergrader agreement for the 3 centers was 0.84 ± 0.05, 0.79 ± 0.05, and 0.76 ± 0.06 at 2°N–2°T, 3–6°T, and 7–10°T, respectively. The best combination of hyperparameters based on the tree-structured Parzen estimator algorithm achieved an F1 score of 0.89 ± 0.04. The average agreement between the model and the graders was 0.87 ± 0.04, 0.85 ± 0.03, and 0.81 ± 0.03 at 2°N–2°T, 3°–6°T, and 7°–10°T, respectively. These values were higher than those between AOdetect’s auto detection without manual correction and the graders (0.84 ± 0.05, 0.79 ± 0.03, and 0.68 ± 0.04, respectively). A reduction in cone density was noted at greater eccentricities, in line with previous research findings, and the model indicated variations in estimating cell density for individuals aged 18 to 30 compared with those aged ≥50 years.</div></div><div><h3>Conclusions</h3><div>The performance of the developed deep learning–based model, AO-FIO ConeDetect, was comparable to that of graders from 3 medical centers. It outperformed the manufacturers’ software auto-detection, particularly at higher eccentricities (7°–10°T). Hence, the model could reduce the need for manual correction and enable faster cone mosaic analyses.</div></div><div><h3>Financial Disclosure(s)</h3><div>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</div></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"5 3","pages":"Article 100675"},"PeriodicalIF":3.2,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OCT Angiography Analysis of Retinal and Choroidal Flow after Proton Beam Therapy for Choroidal Melanoma","authors":"Su-Kyung Jung MD ,&nbsp;Edward H. Lee MD ,&nbsp;Kavita K. Mishra MD ,&nbsp;Inder K. Daftari PhD ,&nbsp;Susanna S. Park MD, PhD","doi":"10.1016/j.xops.2024.100674","DOIUrl":"10.1016/j.xops.2024.100674","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the macular and peripapillary retinal and choroidal flow changes in eyes with choroidal melanoma (CM) treated with proton beam radiation therapy (PBRT) using OCT angiography (OCTA).</div></div><div><h3>Design</h3><div>A prospective, cross-sectional, single-center study.</div></div><div><h3>Participants</h3><div>All patients seen at the study center between 2019 and 2024 who received PBRT for CM in 1 eye ≥1 year before enrollment with best-corrected visual acuity (BCVA) <u>&gt;</u>20/200, unremarkable contralateral eye, and agreed to participate.</div></div><div><h3>Methods</h3><div>After a comprehensive eye examination, including BCVA, Optovue AngioVue was used to obtain the 4.5-mm optic disc and 6.0-mm macular OCT/OCT angiography (OCTA) images of both eyes. All vascular density (VD) measurements were obtained automatically using the OCTA software, except choriocapillaris VD, which was quantitated using ImageJ. The Wilcoxon signed-rank test was used to analyze differences in OCT/OCTA parameters between the treated and the contralateral eyes. Spearman’s ρ was used to identify OCTA parameters associated with BCVA or radiation dose. A <em>P</em> value of &lt;0.05 was considered statistically significant.</div></div><div><h3>Main Outcome Measures</h3><div>Foveal avascular zone (FAZ) area and perimeter, choriocapillaris and retinal (superficial and deep) capillary VD in the macula and radial peripapillary capillary (RPC) VD on OCTA; macular and retinal nerve fiber layer thickness on OCT, tumor location, laterality and size at baseline, BCVA of both eyes, PBRT dose, and duration of follow-up at enrollment.</div></div><div><h3>Results</h3><div>Among 24 participants, OCT/OCTA parameters were significantly different in the treated eyes when compared with the contralateral eyes, including increased FAZ area and perimeter, decreased peripapillary retinal nerve fiber layer thickness and RPC VD, and decreased macular choriocapillaris VD and parafoveal and perifoveal superficial retinal plexus VD (<em>P</em> &lt; 0.05). Best-corrected visual acuity in the treated eyes correlated significantly with FAZ area and perimeter, parafoveal and perifoveal deep retinal plexus VD, and radiation dose to fovea but not radiation dose to the optic disc.</div></div><div><h3>Conclusions</h3><div>Although PBRT can affect both retinal and choroidal vascular flow in the macular and peripapillary region in eyes with CM, BCVA after PBRT seems to correlate best with the retinal vascular flow changes in the macula on OCTA and radiation dose to the fovea.</div></div><div><h3>Financial Disclosure(s)</h3><div>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</div></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"5 3","pages":"Article 100674"},"PeriodicalIF":3.2,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optical Strategy Utilizing Contrast Modulation to Slow Myopia","authors":"James S. Wolffsohn MBA, PhD ,&nbsp;Kate L. Gifford BAppSc (Optom), PhD","doi":"10.1016/j.xops.2024.100672","DOIUrl":"10.1016/j.xops.2024.100672","url":null,"abstract":"<div><div>A new method to slow myopia progression utilizes Diffusion Optics Technology (DOT) spectacle lenses. The proposed mechanism of action for the DOT lenses is to modulate contrast across the photoreceptor cells, leading to an altered activity of the ON and OFF pathways and slowing the progression of axial elongation. This approach is different from the current optical approaches that utilize optical defocus to reduce hyperopic defocus at the peripheral retina although central vision is fully corrected to slow myopia. Initial clinical studies with the DOT lenses have demonstrated promising results with a reduction in progression of myopia. This overview summarizes the current knowledge on myopia risk factors, the evidence for involvement of contrast signaling pathways in refractive error development, and the theories and mechanisms behind DOT lens technology. It also considers the role of contrast and the paradoxical observations given the established paradigm of form deprivation in animal models.</div></div><div><h3>Financial Disclosure(s)</h3><div>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</div></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"5 4","pages":"Article 100672"},"PeriodicalIF":3.2,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AlphaMissense Predictions and ClinVar Annotations: A Deep Learning Approach to Uveal Melanoma","authors":"David J. Taylor Gonzalez MD ,&nbsp;Mak B. Djulbegovic MD, MSc ,&nbsp;Meghan Sharma MD, MPH ,&nbsp;Michael Antonietti BS ,&nbsp;Colin K. Kim BS ,&nbsp;Vladimir N. Uversky PhD, DSc ,&nbsp;Carol L. Karp MD ,&nbsp;Carol L. Shields MD ,&nbsp;Matthew W. Wilson MD","doi":"10.1016/j.xops.2024.100673","DOIUrl":"10.1016/j.xops.2024.100673","url":null,"abstract":"<div><h3>Objective</h3><div>Uveal melanoma (UM) poses significant diagnostic and prognostic challenges due to its variable genetic landscape. We explore the use of a novel deep learning tool to assess the functional impact of genetic mutations in UM.</div></div><div><h3>Design</h3><div>A cross-sectional bioinformatics exploratory data analysis of genetic mutations from UM cases.</div></div><div><h3>Subjects</h3><div>Genetic data from patients diagnosed with UM were analyzed, explicitly focusing on missense mutations sourced from the Catalogue of Somatic Mutations in Cancer (COSMIC) database.</div></div><div><h3>Methods</h3><div>We identified missense mutations frequently observed in UM using the COSMIC database, assessed their potential pathogenicity using AlphaMissense, and visualized mutations using AlphaFold. Clinical significance was cross-validated with entries in the ClinVar database.</div></div><div><h3>Main Outcome Measures</h3><div>The primary outcomes measured were the agreement rates between AlphaMissense predictions and ClinVar annotations regarding the pathogenicity of mutations in critical genes associated with UM, such as <em>GNAQ, GNA11, SF3B1, EIF1AX</em>, and <em>BAP1</em>.</div></div><div><h3>Results</h3><div>Missense substitutions comprised 91.35% (n = 1310) of mutations in UM found on COSMIC. Of the 151 unique missense mutations analyzed in the most frequently mutated genes, only 40.4% (n = 61) had corresponding data in ClinVar. Notably, AlphaMissense provided definitive classifications for 27.2% (n = 41) of the mutations, which were labeled as “unknown significance” in ClinVar, underscoring its potential to offer more clarity in ambiguous cases. When excluding these mutations of uncertain significance, AlphaMissense showed perfect agreement (100%) with ClinVar across all analyzed genes, demonstrating no discrepancies where a mutation predicted as “pathogenic” was classified as “benign” or vice versa.</div></div><div><h3>Conclusions</h3><div>Integrating deep learning through AlphaMissense offers a promising approach to understanding the mutational landscape of UM. Our methodology holds the potential to improve genomic diagnostics and inform the development of personalized treatment strategies for UM.</div></div><div><h3>Financial Disclosure(s)</h3><div>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</div></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"5 3","pages":"Article 100673"},"PeriodicalIF":3.2,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Models to Identify Patients with High Probability of Glaucoma Using Electronic Health Records","authors":"Rohith Ravindranath MS,&nbsp;Sophia Y. Wang MD, MS","doi":"10.1016/j.xops.2024.100671","DOIUrl":"10.1016/j.xops.2024.100671","url":null,"abstract":"<div><h3>Purpose</h3><div>Early detection of glaucoma allows for timely treatment to prevent severe vision loss, but screening requires resource-intensive examinations and imaging, which are challenging for large-scale implementation and evaluation. The purpose of this study was to develop artificial intelligence models that can utilize the wealth of data stored in electronic health records (EHRs) to identify patients who have high probability of developing glaucoma, without the use of any dedicated ophthalmic imaging or clinical data.</div></div><div><h3>Design</h3><div>Cohort study.</div></div><div><h3>Participants</h3><div>A total of 64 735 participants who were ≥18 years of age and had ≥2 separate encounters with eye-related diagnoses recorded in their EHR records in the All of Us Research Program, a national multicenter cohort of patients contributing EHR and survey data, and who were enrolled from May 1, 2018, to July 1, 2022.</div></div><div><h3>Methods</h3><div>We developed models to predict which patients had a diagnosis of glaucoma, using the following machine learning approaches: (1) penalized logistic regression, (2) XGBoost, and (3) a deep learning architecture that included a 1-dimensional convolutional neural network (1D-CNN) and stacked autoencoders. Model input features included demographics and only the nonophthalmic lab results, measurements, medications, and diagnoses available from structured EHR data.</div></div><div><h3>Main Outcome Measures</h3><div>Evaluation metrics included area under the receiver operating characteristic curve (AUROC).</div></div><div><h3>Results</h3><div>Of 64 735 patients, 7268 (11.22%) had a glaucoma diagnosis. Overall, AUROC ranged from 0.796 to 0.863. The 1D-CNN model achieved the highest performance with an AUROC score of 0.863 (95% confidence interval [CI], 0.862–0.864). Investigation of 1D-CNN model performance stratified by race/ethnicity showed that AUROC ranged from 0.825 to 0.869 by subpopulation, with the highest performance of 0.869 (95% CI, 0.868–0.870) among the non-Hispanic White subpopulation.</div></div><div><h3>Conclusions</h3><div>Machine and deep learning models were able to use the extensive systematic data within EHR to identify individuals with glaucoma, without the need for ophthalmic imaging or clinical data. These models could potentially automate identifying high-risk glaucoma patients in EHRs, aiding targeted screening referrals. Additional research is needed to investigate the impact of protected class characteristics such as race/ethnicity on model performance and fairness.</div></div><div><h3>Financial Disclosure(s)</h3><div>The author(s) have no proprietary or commercial interest in any materials discussed in this article.</div></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"5 3","pages":"Article 100671"},"PeriodicalIF":3.2,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometrical Features of Subbasal Corneal Whorl-like Nerve Patterns in Dry Eye Disease","authors":"Ziqing Feng MD,&nbsp;Kang Yu MD,&nbsp;Yupei Chen MS,&nbsp;Gengyuan Wang MS,&nbsp;Yuqing Deng MD,&nbsp;Wei Wang MD,&nbsp;Ruiwen Xu MD,&nbsp;Yimin Zhang MD,&nbsp;Peng Xiao PhD,&nbsp;Jin Yuan MD, PhD","doi":"10.1016/j.xops.2024.100669","DOIUrl":"10.1016/j.xops.2024.100669","url":null,"abstract":"<div><h3>Purpose</h3><div>To investigate the geometrical feature of the whorl-like corneal nerve in dry eye disease (DED) across different severity levels and subtypes and preliminarily explore its diagnostic ability.</div></div><div><h3>Design</h3><div>Cross-sectional study.</div></div><div><h3>Participants</h3><div>The study included 29 healthy subjects (51 eyes) and 62 DED patients (95 eyes).</div></div><div><h3>Methods</h3><div>All subjects underwent comprehensive ophthalmic examinations, dry eye tests, and in vivo confocal microscopy to visualize the whorl-like corneal nerve at the inferior whorl (IW) region and the straight nerve at the central cornea. The structure of the corneal nerve was extracted and characterized using the fractal dimension (CND_f), multifractal dimension (CND₀), tortuosity (CNTor), fiber length (CNFL), and numbers of branching points.</div></div><div><h3>Main Outcome Measures</h3><div>The characteristics of quantified whorl-like corneal nerve metrics in different groups of severity and subtype defined by symptoms and signs of DED.</div></div><div><h3>Results</h3><div>Compared with the healthy controls, the CND_f, CND₀, and CNFL of the IW decreased significantly as early as grade 1 DED (<em>P</em> &lt; 0.05), whereas CNTor increased (<em>P</em> &lt; 0.05). These parameters did not change significantly in the straight nerve. As the DED severity increased, CND_f and CNFL in the whorl-like nerve further decreased in grade 3 DED compared with grade 1. Significant nerve fiber loss was observed in aqueous-deficient DED compared with evaporative DED (<em>P</em> &lt; 0.05). Whorl-like nerve metrics correlated with ocular discomfort, tear film break-up time, tear secretion, and corneal fluorescein staining, respectively (<em>P</em> &lt; 0.05). Furthermore, merging parameters of whorl-like and linear nerve showed an area under the curve value of 0.910 in diagnosing DED.</div></div><div><h3>Conclusions</h3><div>Geometrical parameters of IW could potentially allow optimization of the staging of DED. Reliable and objective measurements for the whorl-like cornea nerve might facilitate patient stratification and diagnosis of DED.</div></div><div><h3>Financial Disclosure(s)</h3><div>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</div></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"5 2","pages":"Article 100669"},"PeriodicalIF":3.2,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11787521/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143082487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}