Ophthalmology science最新文献

{"title":"Impact of Elevated Intraocular Pressure on Lamina Cribrosa Oxygenation: A Combined Experimental–Computational Study on Monkeys","authors":"Yuankai Lu PhD ,&nbsp;Yi Hua PhD ,&nbsp;Bingrui Wang PhD ,&nbsp;Qi Tian ,&nbsp;Fuqiang Zhong PhD ,&nbsp;Andrew Theophanous ,&nbsp;Shaharoz Tahir ,&nbsp;Po-Yi Lee PhD ,&nbsp;Ian A. Sigal PhD","doi":"10.1016/j.xops.2025.100725","DOIUrl":"10.1016/j.xops.2025.100725","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate how lamina cribrosa (LC) oxygenation is affected by tissue distortions resulting from elevated intraocular pressure (IOP).</div></div><div><h3>Design</h3><div>Experimental study on 4 monkeys, histology, and computational analysis.</div></div><div><h3>Subjects</h3><div>Four healthy monkey eyes with OCT scans at IOPs of 10 to 60 mmHg.</div></div><div><h3>Methods</h3><div>Intraocular pressure–induced LC tissue deformations of a healthy monkey were measured in vivo using OCT images and digital volume correlation analysis techniques. Three-dimensional eye-specific models of the LC vasculature of 4 healthy monkey eyes were reconstructed using histology. The models were then used to compute LC oxygenation, first as reconstructed (baseline), and then with the LC vessels distorted according to the OCT-derived deformations. Two biomechanics-based mapping techniques were evaluated: cross-sectional and isotropic. The hemodynamics and oxygenations of the 4 LC vessel networks were evaluated at IOPs up to 60 mmHg to quantify the effects of IOP on LC oxygen supply, assorting the extent of LC tissue mild and severe hypoxia.</div></div><div><h3>Main Outcome Measures</h3><div>Intraocular pressure–induced deformation, vasculature structure, blood supply, and LC oxygenation.</div></div><div><h3>Results</h3><div>Intraocular pressure–induced deformations reduced LC oxygenation significantly and substantially. More than 20% of LC tissue suffered from mild hypoxia when IOP reached 30 mmHg. Extreme IOP (&gt;50 mmHg) led to large severe hypoxia regions (&gt;30%) in the isotropic mapping cases.</div></div><div><h3>Conclusions</h3><div>Our calculations predicted that moderately elevated IOP can lead to mild hypoxia in a substantial part of the LC, which, if sustained chronically, may contribute to neural tissue damage. For extreme IOP elevations, severe hypoxia was predicted, which would likely cause more immediate damage. Our findings suggest that despite the remarkable LC vascular robustness, IOP-induced distortions can potentially contribute to glaucomatous neuropathy.</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 100725"},"PeriodicalIF":3.2,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592627","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":"Code-Free Deep Learning Glaucoma Detection on Color Fundus Images","authors":"Daniel Milad MD ,&nbsp;Fares Antaki MDCM ,&nbsp;David Mikhail MD (C), MSc (C) ,&nbsp;Andrew Farah MDCM (C) ,&nbsp;Jonathan El-Khoury MD ,&nbsp;Samir Touma MD ,&nbsp;Georges M. Durr MD ,&nbsp;Taylor Nayman MD ,&nbsp;Clément Playout PhD (C) ,&nbsp;Pearse A. Keane MD, FRCOphth ,&nbsp;Renaud Duval MD","doi":"10.1016/j.xops.2025.100721","DOIUrl":"10.1016/j.xops.2025.100721","url":null,"abstract":"<div><h3>Objective</h3><div>Code-free deep learning (CFDL) allows clinicians with no coding experience to build their own artificial intelligence models. This study assesses the performance of CFDL in glaucoma detection from fundus images in comparison to expert-designed models.</div></div><div><h3>Design</h3><div>Deep learning model development, testing, and validation.</div></div><div><h3>Subjects</h3><div>A total of 101 442 labeled fundus images from the Rotterdam EyePACS Artificial Intelligence for Robust Glaucoma Screening (AIROGS) dataset were included.</div></div><div><h3>Methods</h3><div>Ophthalmology trainees without coding experience designed a CFDL binary model using the Rotterdam EyePACS AIROGS dataset of fundus images (101 442 labeled images) to differentiate glaucoma from normal optic nerves. We compared our results with bespoke models from the literature. We then proceeded to externally validate our model using 2 datasets, the Retinal Fundus Glaucoma Challenge (REFUGE) and the Glaucoma grading from Multi-Modality imAges (GAMMA) at 0.1, 0.3, and 0.5 confidence thresholds.</div></div><div><h3>Main Outcome Measures</h3><div>Area under the precision-recall curve (AuPRC), sensitivity at 95% specificity (SE@95SP), accuracy, area under the receiver operating curve (AUC), and positive predictive value (PPV).</div></div><div><h3>Results</h3><div>The CFDL model showed high performance metrics that were comparable to the bespoke deep learning models. Our single-label classification model had an AuPRC of 0.988, an SE@95SP of 95%, and an accuracy of 91% (compared with 85% SE@95SP for the top bespoke models). Using the REFUGE dataset for external validation, our model had an SE@95SP, AUC, PPV, and accuracy of 83%, 0.960%, 73% to 94%, and 95% to 98%, respectively, at the 0.1, 0.3, and 0.5 confidence threshold cutoffs. Using the GAMMA dataset for external validation at the same confidence threshold cutoffs, our model had an SE@95SP, AUC, PPV, and accuracy of 98%, 0.994%, 94% to 96%, and 94% to 97%, respectively.</div></div><div><h3>Conclusion</h3><div>The capacity of CFDL models to perform glaucoma screening using fundus images presents a compelling proof of concept, empowering clinicians to explore innovative model designs for broad glaucoma screening in the near future.</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 100721"},"PeriodicalIF":3.2,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681911","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":"Independent Evaluation of RETFound Foundation Model's Performance on Optic Nerve Analysis Using Fundus Photography","authors":"Maggie S. Chen ,&nbsp;Rohith Ravindranath MS ,&nbsp;Robert Chang MD ,&nbsp;Yukun Zhou PhD ,&nbsp;Pearse A. Keane MD FRCOphth ,&nbsp;Sophia Y. Wang MD, MS","doi":"10.1016/j.xops.2025.100720","DOIUrl":"10.1016/j.xops.2025.100720","url":null,"abstract":"<div><h3>Purpose</h3><div>This study evaluates RETFound, a retinal image foundation model, as a feature extractor for predicting optic nerve metrics like cup-to-disc ratio (CDR) and retinal nerve fiber layer (RNFL) thickness using an independent clinical dataset.</div></div><div><h3>Design</h3><div>Retrospective observational study.</div></div><div><h3>Participants</h3><div>Patients who underwent fundus photography and RNFL OCT at the Byers Eye Institute, Stanford University.</div></div><div><h3>Methods</h3><div>Fundus images were paired with RNFL OCT results where study dates were within 6 months of each other. Latent features from full-sized raw fundus images were extracted from RETFound and used as inputs for several linear regression models (Ridge, Lasso, Elastic Net, and ordinary least squares). Baseline models using pretrained VGG16 and Vision Transformers (ViTs) as feature extractors were also developed. All models were trained to perform single-output tasks (predicting CDR or average RNFL thickness) and multioutput tasks (predicting RNFL thickness at quadrants and clock hours). Data were split 80:20 at the patient level for training and validation.</div></div><div><h3>Main Outcome Measures</h3><div>Model predictions were evaluated on a test set using the metrics of <em>R</em>^<em>2</em>, mean absolute error, and root mean square error.</div></div><div><h3>Results</h3><div>Among the 463 unique participants, contributing 776 fundus–OCT data pairs, the mean age was 63 years (±18 years), with 57.24% being female (N = 265). RETFound models demonstrated strong performance on single-output tasks, achieving <em>R</em>^<em>2</em> values between 0.706 and 0.898 for CDR prediction and between 0.855 and 0.961 for average RNFL thickness prediction. Performance on multioutput tasks was less robust, with a highest <em>R</em>^<em>2</em> of 0.583 for clock-hour RNFL thickness prediction and an <em>R</em>^<em>2</em> of 0.811 for quadrant RNFL thickness prediction. RETFound models outperformed VGG16 and ViT models, which achieved maximum <em>R</em>^<em>2</em> of 0.731 and 0.687 in predicting RNFL thickness and CDR.</div></div><div><h3>Conclusions</h3><div>Machine learning models leveraging the massively pretrained RETFound foundation model could accurately predict CDR and average RNFL thickness from fundus photos on an independent clinical dataset. Although RETFound was not trained or fine-tuned for these optic nerve evaluation tasks, nevertheless, RETFound overcomes small dataset limitations and excels in specialized applications.</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 100720"},"PeriodicalIF":3.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592628","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":"Sex Differences in Age-Related Changes in Retinal Arteriovenous Area Based on Deep Learning Segmentation Model","authors":"Mizuho Mitamura MD ,&nbsp;Michiyuki Saito MD, PhD ,&nbsp;Kanae Fukutsu MD, PhD ,&nbsp;Zhenyu Dong MD, PhD ,&nbsp;Ryo Ando MD, PhD ,&nbsp;Satoru Kase MD, PhD ,&nbsp;Kousuke Noda MD, PhD ,&nbsp;Ryosuke Shiba ,&nbsp;Naoki Isogai ,&nbsp;Mitsuru Dohke MD ,&nbsp;Manabu Kase MD, PhD ,&nbsp;Susumu Ishida MD, PhD","doi":"10.1016/j.xops.2025.100719","DOIUrl":"10.1016/j.xops.2025.100719","url":null,"abstract":"","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"5 3","pages":"Article 100719"},"PeriodicalIF":3.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519009","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":"Impact of the Coronavirus Disease 2019 Pandemic on Initiation Therapy for Noninfectious Uveitis","authors":"Evan M. Chen MD,&nbsp;D. Claire Miller MS,&nbsp;Yuwei Sun MS,&nbsp;Anika Kumar BA,&nbsp;Jason Richards MPH,&nbsp;Nisha R. Acharya MD","doi":"10.1016/j.xops.2025.100718","DOIUrl":"10.1016/j.xops.2025.100718","url":null,"abstract":"<div><h3>Purpose</h3><div>Initial studies during the coronavirus disease 2019 (COVID-19) pandemic demonstrated a possible increased risk of COVID-19 infection and severe outcomes with prior or concurrent immunomodulatory therapy (IMT). The purpose of this study was to determine the impact of the COVID-19 pandemic on treatment patterns for noninfectious uveitis (NIU).</div></div><div><h3>Design</h3><div>Retrospective interrupted time series (ITS) analysis using Optum Labs Data Warehouse, a national deidentified health care database in the United States with administrative claims and electronic health record data.</div></div><div><h3>Participants</h3><div>Individuals with a new diagnosis of NIU from December 1, 2017, to December 31, 2020, with continuous enrollment ≥1 year before this diagnosis.</div></div><div><h3>Methods</h3><div>This study was divided into 3 time periods: prepandemic (December 1, 2017–November 30, 2019), early pandemic (March 1, 2020–December 31, 2020), and postvaccine period (January 1, 2021–September 30, 2021) corresponding to time before the pandemic, during the pandemic when no COVID-19 vaccine was available, and after widespread utilization of the vaccine began. Normalized prescription rates of uveitis therapies were modeled as an ITS. In the time-to-treatment analysis, Cox proportional hazard models were used to determine differences in likelihood of different modalities between time periods.</div></div><div><h3>Main Outcome Measures</h3><div>Temporal trends in the initial therapeutic choice for NIU.</div></div><div><h3>Results</h3><div>This study included 22 444 patients with a new NIU diagnosis. The average age was 61.9 (standard deviation 17.5) years, and 59.3% were female. There were no significant temporal breaks in prescribing trends for topical, local, and systemic corticosteroids or immunosuppressive therapy (disease-modifying antirheumatic drugs and biologics) between pandemic periods (all <em>P</em> &gt; 0.05) in ITS analysis. Overall, topical steroids were more likely to be prescribed in the early versus prepandemic period (hazard ratio [HR] 1.10; 95% confidence interval [CI] 1.06–1.15; <em>P</em> &lt; 0.001). Intraocular steroids also saw greater relative use during the early (HR 1.29; 95% CI 1.13–1.46; <em>P</em> &lt; 0.001) and postvaccine (HR 1.29; 95% CI 1.14–1.46; <em>P</em> &lt; 0.001) period. Use of IMTs increased in the postvaccine period compared with that in the prepandemic period (HR 1.25; 95% CI 1.07–1.46; <em>P</em> &lt; 0.001).</div></div><div><h3>Conclusions</h3><div>No significant differences in prescribing patterns for NIU were observed between pandemic periods. However, utilization of topical and local steroids for NIU was, overall, increased in the early compared with the prepandemic period.</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 4","pages":"Article 100718"},"PeriodicalIF":3.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681913","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":"Enhanced Phenotype Identification of Common Ocular Diseases in Real-World Datasets","authors":"Joshua D. Stein MD, MS ,&nbsp;Hong Su An PhD ,&nbsp;Chris A. Andrews PhD ,&nbsp;Suzann Pershing MD ,&nbsp;Tushar Mungle PhD ,&nbsp;Amanda K. Bicket MD, MSE ,&nbsp;Julie M. Rosenthal MD, MS ,&nbsp;Amy D. Zhang MD ,&nbsp;Wen-Shin Lee MD ,&nbsp;Cassie Ludwig MD, MS ,&nbsp;Bethlehem Mekonnen MD ,&nbsp;Tina Hernandez-Boussard PhD","doi":"10.1016/j.xops.2025.100717","DOIUrl":"10.1016/j.xops.2025.100717","url":null,"abstract":"<div><h3>Objective</h3><div>For studies using real-world data, accurately identifying patients with phenotypes of interest is challenging. To identify cohorts of interest, most studies exclusively use the International Classification of Diseases (ICD) billing codes, which can be limiting. We developed a method to accurately identify the presence or absence of 3 common ocular diseases (diabetic retinopathy [DR], age-related macular degeneration [AMD], and glaucoma) using electronic health record (EHR) data.</div></div><div><h3>Design</h3><div>Database study.</div></div><div><h3>Participants</h3><div>Three thousand nine hundred fourteen eyes from 1957 patients at 2 Sight OUtcomes Research CollaborativE (SOURCE) Ophthalmology Data Repository sites.</div></div><div><h3>Methods</h3><div>We developed enhanced phenotype identification (EPI) algorithms that search EHR fields, including eye examination findings, orders, charges, medication prescriptions, and surgery data for evidence that a patient has glaucoma, DR, or AMD. We trained our EPI models using gold standard assessments of the EHR by ophthalmologists for the presence/absence of these conditions, compared the performance of our EPI models to models developed using ICD codes alone, and validated the performance of model using data from another SOURCE site.</div></div><div><h3>Main Outcome Measures</h3><div>Area under the receiver operating curve (AUC), area under the precision–recall curve (AUPRC), and model calibration.</div></div><div><h3>Results</h3><div>The AUCs of our EPI models were better than ICD-only models for glaucoma (0.97 vs. 0.90), DR (0.997 vs. 0.98), and AMD (0.99 vs. 0.95). The AUPRCs of our EPI models were also much better than ICD-only models for glaucoma (0.79 vs. 0.32), DR (0.96 vs. 0.84), and AMD (0.74 vs. 0.55). When testing on patients from a second SOURCE site, the AUC and AUPRC for glaucoma (0.93, 0.74), DR (0.98, 0.77), and AMD (0.96, 0.64) were slightly worse than the primary site but still quite high. However, for all 3 conditions, model calibration was worse at the second site.</div></div><div><h3>Conclusions</h3><div>Leveraging machine learning, we developed EPI models to accurately identify most patients with glaucoma, DR, and AMD in real-world datasets. The EPI models significantly outperform ICD-only models in identifying patients confirmed to have these conditions. These findings underscore the potential of using comprehensive EHR data combined with advanced machine learning techniques to improve the accuracy of patient phenotype identification, leading to better patient management and clinical outcomes.</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 100717"},"PeriodicalIF":3.2,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681440","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":"Radiomic Feature Extraction from OCT Angiography of Idiopathic Epiretinal Membranes and Correlation with Visual Acuity: A Pilot Study","authors":"Maria Cristina Savastano MD ,&nbsp;Marica Vagni MD ,&nbsp;Matteo Mario Carlà MD ,&nbsp;Huong Elena Tran MD ,&nbsp;Claudia Fossataro MD ,&nbsp;Valentina Cestrone AO ,&nbsp;Francesco Boselli MD ,&nbsp;Federico Giannuzzi MD ,&nbsp;Sofia Marcelli AO ,&nbsp;Ilaria Biagini AO ,&nbsp;Luca Boldrini MD ,&nbsp;Stanislao Rizzo MD","doi":"10.1016/j.xops.2025.100716","DOIUrl":"10.1016/j.xops.2025.100716","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Purpose&lt;/h3&gt;&lt;div&gt;To explore the correlation between radiomics features extracted from OCT angiography (OCTA) of epiretinal membranes (ERMs) and baseline best-corrected visual acuity (BCVA).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Design&lt;/h3&gt;&lt;div&gt;Retrospective observational monocentric study.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Participants&lt;/h3&gt;&lt;div&gt;Eighty-three eyes affected by idiopathic ERMs, categorized into low (≤70 letters) and high (70 letters) BCVA groups.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;The central 3 × 3 mm&lt;sup&gt;2&lt;/sup&gt; crop of structural and vascular en-face OCTA scans of superficial and deep retina slab, and choriocapillaris of each eye was selected. PyRadiomics was used to extract 86 features belonging to 2 different families: intensity-based statistical features describing the gray-level distribution, and textural features capturing the spatial arrangement of pixels. By employing a greedy strategy, 4 radiomic features were selected to build the final logistic regression model. The ability of the model to discriminate between low and high baseline BCVA was quantified in terms of area under the receiver operating characteristics curve (AUC).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Main Outcome Measures&lt;/h3&gt;&lt;div&gt;The 4 selected informative radiomic features were as follows: the difference average (glcm_DifferenceAverage), quantifying the average difference in gray-level between neighboring pixels; the informational measure of correlation (glcm_Imc1), giving information about the spatial correlation of pixel intensities inside the image; the long run low gray-level emphasis (glrlm_LongRunLowGrayLevelEmphasis), highlighting long segments of low gray-level values within the image; and the large area emphasis (glszm_LargeAreaEmphasis), which quantifies the tendency for larger zones of uniform intensity to occur.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;No features exhibited a statistically significant difference between low and high BCVA values for the superficial and deep retinal slabs. Conversely, in the choriocapillaris layer, the glcm_DifferenceAverage and glcm_Imc1 features were significantly higher in the high BCVA group (&lt;em&gt;P&lt;/em&gt; = 0.047), whereas higher values for the glrlm_LongRunLowGrayLevelEmphasis and glszm_LargeAreaEmphasis were associated with the low BCVA group (&lt;em&gt;P&lt;/em&gt; = 0.047). Overall, these radiomic features predicted BCVA with an AUC (95% confidence interval) of 0.74 (0.63–0.85) and sensitivity/specificity of 0.67/0.75. During the cross-validation, the metrics remained stable.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;Radiomics features of the choriocapillaris in idiopathic ERMs showed a correlation with BCVA, with lower structural complexity and higher homogeneity, together with the presence of homogeneous areas with low-intensity pixel values, reflecting flow voids due to reduced microvascular perfusion, and were correlated with lower visual acuity.&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 an","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"5 3","pages":"Article 100716"},"PeriodicalIF":3.2,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510870","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":"Mechanical Property Comparison of 23-, 25-, and 27-Gauge Trocars across Multiple Pressures and Vitrectomy Systems","authors":"James M. Lai BS ,&nbsp;Justin Chen BS ,&nbsp;Aaron J. Fils BS ,&nbsp;Landon J. Rohowetz MD ,&nbsp;William Herskowitz BA ,&nbsp;Heather Durkee PhD ,&nbsp;Cornelis Jan Rowaan MS ,&nbsp;Araliya N. Gunawardene MS ,&nbsp;Nimesh Patel MD ,&nbsp;Jean-Marie Parel PhD ,&nbsp;Nicolas A. Yannuzzi MD","doi":"10.1016/j.xops.2025.100714","DOIUrl":"10.1016/j.xops.2025.100714","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate the mechanical properties of 23-, 25-, and 27-gauge trocars when subjected to representative pressures of posterior segment surgery.</div></div><div><h3>Design</h3><div>Experimental study.</div></div><div><h3>Subjects</h3><div>There were no subjects included in this study.</div></div><div><h3>Methods</h3><div>Twenty-four trocars each from Alcon, Dutch Ophthalmic Research Center (DORC), and Bausch &amp; Lomb (B/L), distributed evenly across 23-, 25-, and 27-gauge sizes were included in this study. These trocars were tested at both 40 mmHg and 60 mmHg of pressure. Two failure end points were identified, and each trocar was tested until it reached both failure points.</div></div><div><h3>Main Outcome Measures</h3><div>The number of instrument exchanges required before trocar valve failure at 2 predesignated end points.</div></div><div><h3>Results</h3><div>A total of 72 measurements were made. Most notably between brands, 23-gauge Alcon trocars demonstrated significantly greater competence than their 23-gauge counterparts in B/L and DORC at both 40 mmHg and 60 mmHg pressures (<em>P &lt;</em> 0.01). However, at the 25- and 27-gauge sizes, there were no significant differences in competence between trocar brands (<em>P</em> &gt; 0.05).</div></div><div><h3>Conclusions</h3><div>Based on the findings of this study, 23-gauge valved trocars from B/L and DORC exhibited a significantly lower threshold for compromise compared to that of Alcon valved trocars, likely attributable to differences in valve placement design. Surgeon experience, vitrector preference, and the specific type of surgery performed are important considerations in the choice of vitrector gauge and brand.</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 100714"},"PeriodicalIF":3.2,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636767","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":"Prediction Models for Retinopathy of Prematurity Using Nonimaging Machine Learning Approaches: A Regional Multicenter Study","authors":"Yusuke Takeda MD, MPH ,&nbsp;Yutaka Kaneko MD, PhD ,&nbsp;Masahiko Sugimoto MD, PhD ,&nbsp;Hidetoshi Yamashita MD, PhD ,&nbsp;Ayako Sasaki MD, PhD ,&nbsp;Tetsuo Mitsui MD, PhD","doi":"10.1016/j.xops.2025.100715","DOIUrl":"10.1016/j.xops.2025.100715","url":null,"abstract":"<div><h3>Purpose</h3><div>To develop nonimaging machine learning models using clinical data from the first screening to predict the occurrence of retinopathy of prematurity (ROP).</div></div><div><h3>Design</h3><div>This multicenter regional study was conducted in Yamagata Prefecture, Japan.</div></div><div><h3>Participants</h3><div>We collected clinical data of neonates born between October 2016 and September 2018 and screened in 4 neonatal care units.</div></div><div><h3>Methods</h3><div>The 35 variables available at the first screening were used as possible predictors to develop a decision tree, a random forest, a gradient-boosted tree, a neural network, and a Naive Bayes model. Parameter tuning was performed using a 10-fold cross-validation. This process was repeated 200 times using different random seeds for data partitioning.</div></div><div><h3>Main Outcome Measures</h3><div>The target outcome was the final ROP outcome (i.e., the development of any stage of ROP during hospitalization).</div></div><div><h3>Results</h3><div>Of the 215 neonates screened, 43 (20.0%) developed ROP. The median gestational age was 31.4 (interquartile range: 28.1–33.4) weeks, and the median birth weight was 1502 (interquartile range: 967–1823) g. The mean 200-iteration area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and specificity of the random forest model were 0.93 (95% confidence interval [CI] 0.83–0.99), 90.1% (95% CI 84.1–95.2), 95.7% (95% CI 88.2–100), and 66.0% (95% CI 41.7–91.7), respectively. The mean 200-iteration AUC-ROC, accuracy, sensitivity, and specificity of the Naive Bayes model were 0.94 (95% CI 0.86–0.99), 90.6% (95% CI 84.1–96.8), 94.6% (95% CI 86.3–100), and 73.6% (95% CI 50.0–91.7), respectively.</div></div><div><h3>Conclusions</h3><div>Nonimaging machine learning methods have shown high performance in predicting the occurrence of ROP. These models can be beneficial when a fundus camera cannot capture images due to eye opacity and for hospitals that lack pediatric fundus cameras.</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 4","pages":"Article 100715"},"PeriodicalIF":3.2,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681441","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":"Patterns of Myopia Progression in European Adults","authors":"Michael Moore PhD, MSc ,&nbsp;Gareth Lingham PhD ,&nbsp;Daniel I. Flitcroft DPhil, MBBS ,&nbsp;James Loughman PhD","doi":"10.1016/j.xops.2025.100713","DOIUrl":"10.1016/j.xops.2025.100713","url":null,"abstract":"<div><h3>Objective</h3><div>Data regarding the progression of myopia and new-onset myopia in young adults are lacking. This study aims to describe the natural history of myopia development and progression in adults using anonymized electronic medical records from Irish optometric practices.</div></div><div><h3>Design</h3><div>Longitudinal study.</div></div><div><h3>Subjects</h3><div>Electronic medical record data were extracted from 40 Irish optometry practices with 18 620 (59.5% female) patients meeting the inclusion criteria.</div></div><div><h3>Methods</h3><div>Refractive error change was determined among patients with multiple eye examination visits during the period January 1, 2003 to December 31, 2022. Patients aged 18 to 39 years, inclusive, at baseline and attending &gt;1 eye examination with an interval of ≥11 months between visits and that were myopic at the final visit were included in the analysis. Annualized myopia progression in diopter (D)/year was assessed using linear mixed models with age, sex, baseline spherical equivalent refraction, and previous myopic progression as fixed effect covariates. The proportion of patients with unstable myopia (progression worse than −0.25 D/year) was determined.</div></div><div><h3>Main Outcome Measures</h3><div>Proportion of adults across the age range 18 to 39 years with significant myopic progression.</div></div><div><h3>Results</h3><div>Significant myopia progression (progression &lt;−0.25 D/year) was noted in 10.7% of all myopes. The proportion of myopes with significant progression was clearly related to age with 19.9% of myopes in the youngest age group experiencing progression compared with 6.8% in the oldest age group. Higher proportions of myopic progression were also observed in high myopes with 1 in 12 high myopes (8.0%) exhibiting persistent fast myopic progression as adults (worse than −0.50 D/year). Of patients with emmetropia or hyperopia at baseline in this clinic-based population, 28.5% and 0.8% became myopic during the follow-up period.</div></div><div><h3>Conclusions</h3><div>Although myopia has stabilized in most adults (&gt;18 years of age), a sizeable proportion of younger adults and high myopes (of all ages) do progress at a clinically significant rate. Almost 3 times as many adults in youngest age group (18–24 years) experienced myopic progression when compared with the oldest age group (40–44 years). Consideration should therefore be given to exploring the efficacy and benefit of myopia management in this cohort of patients.</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 100713"},"PeriodicalIF":3.2,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636768","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}