Ophthalmology science最新文献

{"title":"Predicting Choroidal Nevus Transformation to Melanoma Using Machine Learning","authors":"Prashant D. Tailor MD ,&nbsp;Piotr K. Kopinski MD, PhD ,&nbsp;Haley S. D’Souza MD ,&nbsp;David A. Leske MS ,&nbsp;Timothy W. Olsen MD ,&nbsp;Carol L. Shields MD ,&nbsp;Jerry A. Shields MD ,&nbsp;Lauren A. Dalvin MD","doi":"10.1016/j.xops.2024.100584","DOIUrl":"10.1016/j.xops.2024.100584","url":null,"abstract":"<div><h3>Purpose</h3><p>To develop and validate machine learning (ML) models to predict choroidal nevus transformation to melanoma based on multimodal imaging at initial presentation.</p></div><div><h3>Design</h3><p>Retrospective multicenter study.</p></div><div><h3>Participants</h3><p>Patients diagnosed with choroidal nevus on the Ocular Oncology Service at Wills Eye Hospital (2007–2017) or Mayo Clinic Rochester (2015–2023).</p></div><div><h3>Methods</h3><p>Multimodal imaging was obtained, including fundus photography, fundus autofluorescence, spectral domain OCT, and B-scan ultrasonography. Machine learning models were created (XGBoost, LGBM, Random Forest, Extra Tree) and optimized for area under receiver operating characteristic curve (AUROC). The Wills Eye Hospital cohort was used for training and testing (80% training–20% testing) with fivefold cross validation. The Mayo Clinic cohort provided external validation. Model performance was characterized by AUROC and area under precision–recall curve (AUPRC). Models were interrogated using SHapley Additive exPlanations (SHAP) to identify the features most predictive of conversion from nevus to melanoma. Differences in AUROC and AUPRC between models were tested using 10 000 bootstrap samples with replacement and results.</p></div><div><h3>Main Outcome Measures</h3><p>Area under receiver operating curve and AUPRC for each ML model.</p></div><div><h3>Results</h3><p>There were 2870 nevi included in the study, with conversion to melanoma confirmed in 128 cases. Simple AI Nevus Transformation System (SAINTS; XGBoost) was the top-performing model in the test cohort [pooled AUROC 0.864 (95% confidence interval (CI): 0.864–0.865), pooled AUPRC 0.244 (95% CI: 0.243–0.246)] and in the external validation cohort [pooled AUROC 0.931 (95% CI: 0.930–0.931), pooled AUPRC 0.533 (95% CI: 0.531–0.535)]. Other models also had good discriminative performance: LGBM (test set pooled AUROC 0.831, validation set pooled AUROC 0.815), Random Forest (test set pooled AUROC 0.812, validation set pooled AUROC 0.866), and Extra Tree (test set pooled AUROC 0.826, validation set pooled AUROC 0.915). A model including only nevi with at least 5 years of follow-up demonstrated the best performance in AUPRC (test: pooled 0.592 (95% CI: 0.590–0.594); validation: pooled 0.656 [95% CI: 0.655–0.657]). The top 5 features in SAINTS by SHAP values were: tumor thickness, largest tumor basal diameter, tumor shape, distance to optic nerve, and subretinal fluid extent.</p></div><div><h3>Conclusions</h3><p>We demonstrate accuracy and generalizability of a ML model for predicting choroidal nevus transformation to melanoma based on multimodal imaging.</p></div><div><h3>Financial Disclosures</h3><p>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</p></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"5 1","pages":"Article 100584"},"PeriodicalIF":3.2,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524001209/pdfft?md5=611e184a6e4ec4c46ad3dd1688c15182&pid=1-s2.0-S2666914524001209-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141844736","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":"Reference Database for a Novel Binocular Visual Function Perimeter: A Randomized Clinical Trial","authors":"Vincent Michael Patella OD ,&nbsp;Nevin W. El-Nimri OD, PhD ,&nbsp;John G. Flanagan PhD ,&nbsp;Mary K. Durbin PhD ,&nbsp;Timothy Bossie OD ,&nbsp;Derek Y. Ho MD, PhD ,&nbsp;Mayra Tafreshi MBA ,&nbsp;Michael A. Chaglasian OD ,&nbsp;David Kasanoff OD ,&nbsp;Satoshi Inoue MSc ,&nbsp;Sasan Moghimi MD ,&nbsp;Takashi Nishida MD, PhD ,&nbsp;Murray Fingeret OD ,&nbsp;Robert N. Weinreb MD","doi":"10.1016/j.xops.2024.100583","DOIUrl":"10.1016/j.xops.2024.100583","url":null,"abstract":"<div><h3>Purpose</h3><p>To construct a comprehensive reference database (RDB) for a novel binocular automated perimeter.</p></div><div><h3>Design</h3><p>A four-site prospective randomized clinical trial.</p></div><div><h3>Subjects and Controls</h3><p>Three hundred fifty-six healthy subjects without ocular conditions that might affect visual function were categorized into 7 age groups.</p></div><div><h3>Methods</h3><p>Subjects underwent comprehensive ocular examination of both eyes before enrollment. Using the TEMPO/IMOvifa automated perimeter (Topcon Healthcare/CREWT Medical Systems), each subject completed 4 binocular threshold visual field (VF) tests during a single visit: First, practice 24-2 and 10-2 tests were obtained from both eyes. Next, study 24-2 and 10-2 tests were obtained from both eyes. Test order of each sequence was randomized, and the tests were conducted under standard automated perimetry testing conditions: Goldmann stimulus size III, 3183 cd/m² maximum stimulus intensity, and background intensity of 10 cd/m², using AIZE-Rapid test strategy. Standard VF reliability indices were assessed. For each subject, 24-2 and 10-2 test results from 1 randomly selected eye were analyzed.</p></div><div><h3>Main Outcome Measures</h3><p>Perimetric threshold sensitivity and reference limits for each test analysis parameter.</p></div><div><h3>Results</h3><p>The ages of the study cohort were widely distributed, with a mean age (standard deviation [SD]) of 52.3 (18.5) years. Sex assignment was 44.0% male and 56.0% female. The majority of subjects self-identified as White (67.4%), followed by Black or African American (13.5%) and Asian (8.7%), with 14.6% self-identified as Hispanic or Latino ethnicity. Mean sensitivity (SD) was 29.1 (1.3) decibels (dB) for the 24-2 and 32.4 (1.0) dB for the 10-2 test. For the 24-2 and 10-2, mean sensitivity (SD) age-related changes averaged −0.06 (0.01) dB and −0.05 (0.01) dB per year, respectively. The normal range of pointwise threshold sensitivity increased with eccentricity and showed asymmetry around the mean, particularly notable in the 24-2 test. Mean (SD) binocular test duration was 3.18 (0.38) minutes (1 minute 35 seconds per eye) for the 24-2 test and 3.58 (0.43) minutes (1 minute 47 seconds per eye) for the 10-2 test.</p></div><div><h3>Conclusions</h3><p>An RDB for the TEMPO/IMOvifa perimeter was established, highlighting the significance of considering both age and stimulus eccentricity in interpreting threshold VF test results.</p></div><div><h3>Financial Disclosure(s)</h3><p>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</p></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"4 6","pages":"Article 100583"},"PeriodicalIF":3.2,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524001192/pdfft?md5=ec0b7377fc17660ad95c7721e62a1e84&pid=1-s2.0-S2666914524001192-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141838949","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":"Censoring the Floor Effect in Long-Term Stargardt Disease Microperimetry Data Produces a Faster Rate of Decline","authors":"Jason Charng PhD ,&nbsp;Jennifer A. Thompson PhD ,&nbsp;Rachael C. Heath Jeffery MD ,&nbsp;Amy Kalantary MD ,&nbsp;Tina M. Lamey PhD ,&nbsp;Terri L. McLaren BSc ,&nbsp;Fred K. Chen PhD","doi":"10.1016/j.xops.2024.100581","DOIUrl":"10.1016/j.xops.2024.100581","url":null,"abstract":"<div><h3>Purpose</h3><p>To evaluate progression rate estimation in long-term Stargardt disease microperimetry data by accounting for floor effect.</p></div><div><h3>Design</h3><p>Cohort study.</p></div><div><h3>Subjects</h3><p>Thirty-seven subjects (23 females, 14 males) with biallelic ABCA4 pathogenic or likely pathogenic variants and more than &gt;2 years of longitudinal microperimetry data.</p></div><div><h3>Methods</h3><p>Cross-sectional and longitudinal microperimetry data (Grid A: 18° diameter, Grid B: 6° diameter; Macular Integrity Assessment microperimeter, dynamic range 0–36 decibels [dB]) was extracted from patients with biallelic mutation in the adenosine triphosphate-binding cassette subfamily A member 4 (<em>ABCA4</em>) gene. For each eye, mean sensitivity (MS) and responding point sensitivity (RPS) rates were extracted. Floor censored sensitivity (FCS) progression rate, which accounts for the floor effect at each locus by terminating calculation when scotoma was observed in 2 consecutive visits, was also calculated. In a subset of eyes with ≥1 scotomatous locus at baseline (Grid A), sensitivity progression of loci around the scotoma (edge of scotoma sensitivity [ESS]) was examined against other progression parameters. Paired <em>t</em> test compared progression rate parameters across the same eyes.</p></div><div><h3>Main Outcome Measures</h3><p>Microperimetry grid parameters at baseline and progression rates.</p></div><div><h3>Results</h3><p>A total of 37 subjects with biallelic <em>ABCA4</em> mutations and &gt;2 years of longitudinal microperimetry data were included in the study. In Grid A, at baseline, the average MS and RPS were 16.5 ± 7.9 and 19.1 ± 5.7 dB, respectively. Similar MS (18.4 ± 7.6 dB) and RPS (20.0 ± 5.5 dB) values were found at baseline for Grid B. In Grid A, overall, MS, RPS, and FCS progression rates were −0.57 ± 1.05, −0.74 ± 1.24, and −1.26 ± 1.65 (all dB/year), respectively. Floor censored sensitivity progression rate was significantly greater than the MS or RPS progression rates. Similar findings were observed in Grid B (MS −1.22 ± 1.42, RPS −1.44 ± 1.44, FCS −2.16 ± 2.24, all dB/year), with paired <em>t</em> test again demonstrated that FCS had a significantly faster rate of decline than MS or RPS. In patients with progression data in both grids, MS, RPS, and FCS progression rates were significantly faster in the smaller Grid B. In 24 eyes with scotoma at baseline, fastest rate of decline was ESS combined with FCS compared with other progression parameters.</p></div><div><h3>Conclusions</h3><p>Incorporation of FCS can reduce confound of floor effect in perimetry analysis and can in turn detect a faster rate of decline.</p></div><div><h3>Financial Disclosure(s)</h3><p>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</p></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"4 6","pages":"Article 100581"},"PeriodicalIF":3.2,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524001179/pdfft?md5=5a10ead90090ebe64a026d61cb8212f2&pid=1-s2.0-S2666914524001179-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141840548","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":"Quantitative Volumetric Analysis of Retinal Ischemia with an Oxygen Diffusion Model and OCT Angiography","authors":"Pengxiao Zang PhD ,&nbsp;Tristan T. Hormel PhD ,&nbsp;Thomas S. Hwang MD ,&nbsp;Yali Jia PhD","doi":"10.1016/j.xops.2024.100579","DOIUrl":"10.1016/j.xops.2024.100579","url":null,"abstract":"<div><h3>Purpose</h3><p>Retinal ischemia is a major feature of diabetic retinopathy (DR). Traditional nonperfused areas measured by OCT angiography (OCTA) measure blood supply but not ischemia. We propose a novel 3-dimensional (3D) quantitative method to derive ischemia measurements from OCTA data.</p></div><div><h3>Design</h3><p>Cross-sectional study.</p></div><div><h3>Participants</h3><p>We acquired 223 macular OCTA volumes from 33 healthy eyes, 33 diabetic eyes without retinopathy, 7 eyes with nonreferable DR, 17 eyes with referable but nonvision-threatening DR, and 133 eyes with vision-threatening DR.</p></div><div><h3>Methods</h3><p>Each eye was scanned using a spectral-domain OCTA system (Avanti RTVue-XR, Visionix/Optovue, Inc) with 1.6-mm scan depth in a 3 × 3-mm region (640 × 304 × 304 voxels) centered on the fovea. For each scanned OCTA volume, a custom algorithm removed flow projection artifacts. We then enhanced, binarized, and skeletonized the vasculature in each OCTA volume and generated a 3D oxygen tension map using a zero-order kinetics oxygen diffusion model. Each volume was scaled to the average retina thickness in healthy controls after foveal registration and flattening of the Bruch's membrane. Finally, we extracted 3D ischemia maps by comparison with a reference map established from scans of healthy eyes using the same processing. To assess the ability of the ischemia maps to grade DR severity, we constructed receiver operating characteristic curves for diagnosing diabetes, referable DR, and vision-threatening DR.</p></div><div><h3>Main Outcome Measures</h3><p>Spearman correlation coefficient and area under receiver operating characteristic curve (AUC) were used to quantify the ability of the ischemia maps to DR.</p></div><div><h3>Results</h3><p>The ischemia maps showed that the ischemic tissues were at or near pathologically nonperfused areas, but not the normally nonvascular tissue, such as the foveal avascular zone. We found multiple novel metrics, including inferred 3D-oxygen tension, ischemia index, and ischemic volume ratio, were strongly correlated with DR severity. The AUCs of ischemia index measured were 0.94 for diabetes, 0.89 for DR, 0.88 for referable DR, and 0.85 for vision-threatening DR.</p></div><div><h3>Conclusions</h3><p>A quantitative method to infer 3D oxygen tension and ischemia using OCTA in diabetic eyes can identify ischemic tissue that are more specific to pathologic changes in DR.</p></div><div><h3>Financial Disclosures</h3><p>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</p></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"4 6","pages":"Article 100579"},"PeriodicalIF":3.2,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524001155/pdfft?md5=b7ecfcf304d2f9f069a370d571d1ba6c&pid=1-s2.0-S2666914524001155-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141845803","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":"The Association between the Pulsatile Choroidal Volume Change and Ocular Rigidity","authors":"Diane N. Sayah OD, PhD ,&nbsp;Denise Descovich MD ,&nbsp;Santiago Costantino PhD ,&nbsp;Mark R. Lesk MD, MSc","doi":"10.1016/j.xops.2024.100576","DOIUrl":"10.1016/j.xops.2024.100576","url":null,"abstract":"<div><h3>Purpose</h3><p>To assess the relationship between the pulsatile choroidal volume change (ΔV) and ocular rigidity (OR), an important biomechanical property of the eye.</p></div><div><h3>Design</h3><p>This is a prospective cross-sectional study.</p></div><div><h3>Subjects</h3><p>Two hundred seventeen participants (235 eyes) were included in this study. Of those, 18 eyes (18 participants) had exudative retinal disease, and 217 eyes (199 participants) had open-angle glaucoma (39.2%), suspect discs (12.4%), ocular hypertension (14.3%), or healthy eyes (34.1%).</p></div><div><h3>Methods</h3><p>Pulsatile choroidal volume change was measured using dynamic OCT, which detects the change in choroidal thickness during the cardiac cycle. Ocular rigidity was measured using an invasive procedure as well as using a validated optical method. Correlations between ΔV and OR were assessed in subjects with healthy eyes, eyes with glaucoma, or eyes with exudative retinal disease.</p></div><div><h3>Main Outcome Measures</h3><p>Ocular rigidity and pulsatile ocular volume change.</p></div><div><h3>Results</h3><p>In 18 eyes where OR was obtained invasively and ΔV was obtained noninvasively, a significant correlation was found between ΔV and OR (r_s = −0.664, <em>P</em> = 0.003). Similarly, a strong inverse correlation was found between the noninvasive measurements of both ΔV and OR (r_s = −0.748, <em>P</em> &lt; 0.001) in a large cohort and maintained its significance across diagnostic groups (a more compliant eye is associated with greater ΔV). No correlation was found between ΔV and age, blood pressure, intraocular pressure, axial length, or diagnosis (<em>P</em> ≥ 0.05). Mean ΔV was 7.3 ± 3.4 μL for all groups combined with a range of 3.0 to 20.8 μL.</p></div><div><h3>Conclusions</h3><p>These results suggest an association between the biomechanics of the corneoscleral shell and pulsatile ocular blood flow, which may indicate that a more rigid eye exerts more resistance to pulsatile choroidal expansion. This highlights the dynamic nature of both blood flow and biomechanics in the eye, as well as how they may interact, leading to a greater understanding of the pathophysiology of ocular disease.</p></div><div><h3>Financial Disclosures</h3><p>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</p></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"4 6","pages":"Article 100576"},"PeriodicalIF":3.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266691452400112X/pdfft?md5=ddd401d8b394fedad1dc8122bed5d1a8&pid=1-s2.0-S266691452400112X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142020826","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":"Improving the Identification of Diabetic Retinopathy and Related Conditions in the Electronic Health Record Using Natural Language Processing Methods","authors":"Keith Harrigian MS ,&nbsp;Diep Tran MSc ,&nbsp;Tina Tang MD ,&nbsp;Anthony Gonzales OD ,&nbsp;Paul Nagy PhD ,&nbsp;Hadi Kharrazi MD, PhD ,&nbsp;Mark Dredze PhD ,&nbsp;Cindy X. Cai MD, MS","doi":"10.1016/j.xops.2024.100578","DOIUrl":"10.1016/j.xops.2024.100578","url":null,"abstract":"<div><h3>Purpose</h3><p>To compare the performance of 3 phenotyping methods in identifying diabetic retinopathy (DR) and related clinical conditions.</p></div><div><h3>Design</h3><p>Three phenotyping methods were used to identify clinical conditions including unspecified DR, nonproliferative DR (NPDR) (mild, moderate, severe), consolidated NPDR (unspecified DR or any NPDR), proliferative DR, diabetic macular edema (DME), vitreous hemorrhage, retinal detachment (RD) (tractional RD or combined tractional and rhegmatogenous RD), and neovascular glaucoma (NVG). The first method used only International Classification of Diseases, 10th Revision (ICD-10) diagnosis codes (<em>ICD-10 Lookup System</em>). The next 2 methods used a Bidirectional Encoder Representations from Transformers with a dense Multilayer Perceptron output layer natural language processing (NLP) framework. The NLP framework was applied either to free-text of provider notes (<em>Text-Only NLP System</em>) or both free-text and ICD-10 diagnosis codes (<em>Text-and-International Classification of Diseases</em> [<em>ICD</em>] <em>NLP System</em>).</p></div><div><h3>Subjects</h3><p>Adults ≥18 years with diabetes mellitus seen at the Wilmer Eye Institute.</p></div><div><h3>Methods</h3><p>We compared the performance of the 3 phenotyping methods in identifying the DR related conditions with gold standard chart review. We also compared the estimated disease prevalence using each method.</p></div><div><h3>Main Outcome Measures</h3><p>Performance of each method was reported as the macro F1 score. The agreement between the methods was calculated using the kappa statistic. Prevalence estimates were also calculated for each method.</p></div><div><h3>Results</h3><p>A total of 91 097 patients and 692 486 office visits were included in the study. Compared with the gold standard, the <em>Text-and-ICD NLP System</em> had the highest F1 score for most clinical conditions (range 0.39–0.64). The agreement between the <em>ICD-10 Lookup System</em> and <em>Text-Only NLP System</em> varied (kappa of 0.21–0.81). The prevalence of DR and related conditions ranged from 1.1% for NVG to 17.9% for DME (using the <em>Text-and-ICD NLP System</em>).</p></div><div><h3>Conclusions</h3><p>The prevalence of DR and related conditions varied significantly depending on the methodology of identifying cases. The best performing phenotyping method was the <em>Text-and-ICD NLP System</em> that used information in both diagnosis codes as well as free-text notes.</p></div><div><h3>Financial Disclosures</h3><p>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</p></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"4 6","pages":"Article 100578"},"PeriodicalIF":3.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524001143/pdfft?md5=aee0aca9014224fef1aa919db24f5c88&pid=1-s2.0-S2666914524001143-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141844268","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 Inflammation-Related Biomarkers Detected with OCT in Patients with Diabetic Macular Edema","authors":"Xinyi Chen MD ,&nbsp;Wendy Yang BS ,&nbsp;Ashley Fong MS ,&nbsp;Noor Chahal BS ,&nbsp;Abu T. Taha BS ,&nbsp;Jeremy D. Keenan MD, MPH ,&nbsp;Jay M. Stewart MD","doi":"10.1016/j.xops.2024.100580","DOIUrl":"10.1016/j.xops.2024.100580","url":null,"abstract":"<div><h3>Purpose</h3><p>To investigate sex-based differences in inflammation-related biomarkers on spectral-domain OCT.</p></div><div><h3>Design</h3><p>Cross-sectional study.</p></div><div><h3>Participants</h3><p>Patients with diabetic macular edema (DME) between February 1, 2019, and March 31, 2023, without intravitreal anti-VEGF injection within the previous 6 months.</p></div><div><h3>Methods</h3><p>We reviewed each patient’s medical record for age, biological sex, race and ethnicity, most recent glycated hemoglobin A1c (HbA1c) level, visual acuity (VA), and central macular thickness (CMT). OCT biomarkers that have been found in literature to be associated with inflammation, including disorganization of retinal inner layers (DRIL), retinal hyperreflective retinal foci (HRFs), hyperreflective choroidal foci (HCFs), subfoveal neuroretinal detachment (SND), and perturbation in retinal nerve fiber layer thickness, ganglion cell layer thickness, and inner nuclear layer (INL) thickness were evaluated by graders masked to the clinical characteristics of the patients. We performed multivariable regression analyses with the OCT biomarkers as the outcome variables and sex, age, HbA1c, and CMT as independent variables.</p></div><div><h3>Main Outcome Measures</h3><p>OCT inflammation-related biomarkers, as listed above.</p></div><div><h3>Results</h3><p>Female patients were, on average, 2 years older than male patients (<em>P</em> = 0.041). There were no significant differences in race and ethnicity, HbA1c, VA, or CMT between male and female patients. After controlling for age, HbA1c, and CMT, we found male sex to be associated with more HRF (incidence rate ratio [IRR] = 1.19; 95% confidence interval [CI] = 1.10–1.29), more HCF (odds ratio = 2.01; 95% CI = 1.12–3.64), and thicker INL (7 μm thicker in males; 95% CI = 2–12). Sex was not a significant predictor for either DRIL or SND in the multivariable regression models. Patients with higher HbA1c were more likely to have more HRF (IRR = 1.02 per 1 point increase; 95% CI = 1.00–1.04) after controlling for other factors.</p></div><div><h3>Conclusions</h3><p>Male sex was correlated with more inflammation-related biomarkers on OCT including more HRF, more HCF, and thicker INL, after accounting for age, glycemic control, and amount of DME. Further studies are needed to evaluate the potential implications of these sex-based differences for individualized treatment.</p></div><div><h3>Financial Disclosures</h3><p>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</p></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"4 6","pages":"Article 100580"},"PeriodicalIF":3.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524001167/pdfft?md5=fd1a7303565f3c755d6452f95c65ef8d&pid=1-s2.0-S2666914524001167-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848952","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":"Posterior Eye Shape in Myopia","authors":"Jost B. Jonas MD ,&nbsp;Songhomitra Panda-Jonas MD ,&nbsp;Zhe Pan MD ,&nbsp;Jie Xu MD ,&nbsp;Ya Xing Wang MD","doi":"10.1016/j.xops.2024.100575","DOIUrl":"10.1016/j.xops.2024.100575","url":null,"abstract":"<div><h3>Purpose</h3><p>To explore prevalence and associated factors of abnormalities of the posterior eye shape in dependence of axial length.</p></div><div><h3>Design</h3><p>Population-based study.</p></div><div><h3>Participants</h3><p>Of the participants (n = 3468) of the Beijing Eye Study, we included all eyes with an axial length of ≥25 mm, and a randomized sample of eyes with an axial length of &lt;25 mm.</p></div><div><h3>Methods</h3><p>Using 30°-wide, serial horizontal, and fovea-centered radial, OCT images, we examined location and depth of the most posterior point of the retinal pigment epithelium/Bruch’s membrane line (PP-RPE/BML).</p></div><div><h3>Main Outcome Measures</h3><p>Prevalence and depth of an extrafoveal PP-RPE/BML.</p></div><div><h3>Results</h3><p>The study included 366 eyes (314 individuals). On the radial OCT scans, the PP-RPE/BML was located in the foveola in 190 (51.9%) eyes, in 121 (33.1%) eyes in the 6 o’clock part of the vertical meridian (distance to foveola: 1.73 ± 0.70 mm), and in 54 (14.8%) eyes in the 12 o’clock part of the vertical meridian (fovea distance: 2.01 ± 0.66 mm). On the horizontal OCT scans, the PP-RPE/BML was located in the foveola in 304 (83.1%) eyes, between foveola and optic disc in 36 (9.8%) eyes (fovea distance: 1.59 ± 0.76 mm), and temporal to the foveola in 26 (7.1%) eyes (fovea distance: 1.20 ± 0.60 mm). Higher prevalence of an extrafoveal PP-RPE/BML correlated with longer axial length (odds ratio [OR]: 1.55; 95% confidence interval [CI]: 1.28, 1.89), higher corneal astigmatism (OR: 1.78; 95% CI: 1.14, 2.79), and female sex (OR: 2.74; 95% CI: 1.30, 5.77). The curvature of the RPE/BML at the posterior pole was similar to the RPE/BML curvature outside of the posterior pole in 309 (84.4%) eyes, and it was steeper (i.e., smaller curvature radius) in 57 (15.6%) eyes. In these eyes, axial length was longer (24.41 ± 1.78 mm versus 27.74 ± 1.88 mm; <em>P</em> &lt; 0.001).</p></div><div><h3>Conclusions</h3><p>With longer axial length, the foveola is more often located outside of the geometrical posterior pole. It may be of importance for biometric axial length measurements. An extrafoveal location of the PP-RPE/BML may be due to an axial elongation-associated, meridionally asymmetric enlargement of Bruch’s membrane in the fundus midperiphery.</p></div><div><h3>Financial Disclosure(s)</h3><p>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</p></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"4 6","pages":"Article 100575"},"PeriodicalIF":3.2,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524001118/pdfft?md5=42f31daebc61edd63d9f5d3810c4a00e&pid=1-s2.0-S2666914524001118-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141715410","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":"Quantifying Changes on OCT in Eyes Receiving Treatment for Neovascular Age-Related Macular Degeneration","authors":"Gabriella Moraes MD, MSc ,&nbsp;Robbert Struyven MD ,&nbsp;Siegfried K. Wagner BMBCh, FRCOphth ,&nbsp;Timing Liu BA ,&nbsp;David Chong MBBChir ,&nbsp;Abdallah Abbas iBSc, MBBS ,&nbsp;Reena Chopra BSc ,&nbsp;Praveen J. Patel MD, FRCOphth ,&nbsp;Konstantinos Balaskas MD ,&nbsp;Tiarnan D.L. Keenan BM BCh, PhD ,&nbsp;Pearse A. Keane MD, FRCOphth","doi":"10.1016/j.xops.2024.100570","DOIUrl":"10.1016/j.xops.2024.100570","url":null,"abstract":"<div><h3>Purpose</h3><p>Application of artificial intelligence (AI) to macular OCT scans to segment and quantify volumetric change in anatomical and pathological features during intravitreal treatment for neovascular age-related macular degeneration (AMD).</p></div><div><h3>Design</h3><p>Retrospective analysis of OCT images from the Moorfields Eye Hospital AMD Database.</p></div><div><h3>Participants</h3><p>A total of 2115 eyes from 1801 patients starting anti-VEGF treatment between June 1, 2012, and June 30, 2017.</p></div><div><h3>Methods</h3><p>The Moorfields Eye Hospital neovascular AMD database was queried for first and second eyes receiving anti-VEGF treatment and had an OCT scan at baseline and 12 months. Follow-up scans were input into the AI system and volumes of OCT variables were studied at different time points and compared with baseline volume groups. Cross-sectional comparisons between time points were conducted using Mann–Whitney <em>U</em> test.</p></div><div><h3>Main Outcome Measures</h3><p>Volume outputs of the following variables were studied: intraretinal fluid, subretinal fluid, pigment epithelial detachment (PED), subretinal hyperreflective material (SHRM), hyperreflective foci, neurosensory retina, and retinal pigment epithelium.</p></div><div><h3>Results</h3><p>Mean volumes of analyzed features decreased significantly from baseline to both 4 and 12 months, in both first-treated and second-treated eyes. Pathological features that reflect exudation, including pure fluid components (intraretinal fluid and subretinal fluid) and those with fluid and fibrovascular tissue (PED and SHRM), displayed similar responses to treatment over 12 months. Mean PED and SHRM volumes showed less pronounced but also substantial decreases over the first 2 months, reaching a plateau postloading phase, and minimal change to 12 months. Both neurosensory retina and retinal pigment epithelium volumes showed gradual reductions over time, and were not as substantial as exudative features.</p></div><div><h3>Conclusions</h3><p>We report the results of a quantitative analysis of change in retinal segmented features over time, enabled by an AI segmentation system. Cross-sectional analysis at multiple time points demonstrated significant associations between baseline OCT-derived segmented features and the volume of biomarkers at follow-up. Demonstrating how certain OCT biomarkers progress with treatment and the impact of pretreatment retinal morphology on different structural volumes may provide novel insights into disease mechanisms and aid the personalization of care. Data will be made public for future studies.</p></div><div><h3>Financial Disclosure(s)</h3><p>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</p></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"4 6","pages":"Article 100570"},"PeriodicalIF":3.2,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524001064/pdfft?md5=ba7935cea71518e47f805f746ad59e08&pid=1-s2.0-S2666914524001064-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985193","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":"Advancing Glaucoma Diagnosis: Employing Confidence-Calibrated Label Smoothing Loss for Model Calibration","authors":"Midhula Vijayan PhD,&nbsp;Deepthi Keshav Prasad PhD,&nbsp;Venkatakrishnan Srinivasan MTech","doi":"10.1016/j.xops.2024.100555","DOIUrl":"10.1016/j.xops.2024.100555","url":null,"abstract":"<div><h3>Objective</h3><p>The aim of our research is to enhance the calibration of machine learning models for glaucoma classification through a specialized loss function named Confidence-Calibrated Label Smoothing (CC-LS) loss. This approach is specifically designed to refine model calibration without compromising accuracy by integrating label smoothing and confidence penalty techniques, tailored to the specifics of glaucoma detection.</p></div><div><h3>Design</h3><p>This study focuses on the development and evaluation of a calibrated deep learning model.</p></div><div><h3>Participants</h3><p>The study employs fundus images from both external datasets—the Online Retinal Fundus Image Database for Glaucoma Analysis and Research (482 normal, 168 glaucoma) and the Retinal Fundus Glaucoma Challenge (720 normal, 80 glaucoma)—and an extensive internal dataset (4639 images per category), aiming to bolster the model's generalizability. The model's clinical performance is validated using a comprehensive test set (47 913 normal, 1629 glaucoma) from the internal dataset.</p></div><div><h3>Methods</h3><p>The CC-LS loss function seamlessly integrates label smoothing, which tempers extreme predictions to avoid overfitting, with confidence-based penalties. These penalties deter the model from expressing undue confidence in incorrect classifications. Our study aims at training models using the CC-LS and comparing their performance with those trained using conventional loss functions.</p></div><div><h3>Main Outcome Measures</h3><p>The model's precision is evaluated using metrics like the Brier score, sensitivity, specificity, and the false positive rate, alongside qualitative heatmap analyses for a holistic accuracy assessment.</p></div><div><h3>Results</h3><p>Preliminary findings reveal that models employing the CC-LS mechanism exhibit superior calibration metrics, as evidenced by a Brier score of 0.098, along with notable accuracy measures: sensitivity of 81%, specificity of 80%, and weighted accuracy of 80%. Importantly, these enhancements in calibration are achieved without sacrificing classification accuracy.</p></div><div><h3>Conclusions</h3><p>The CC-LS loss function presents a significant advancement in the pursuit of deploying machine learning models for glaucoma diagnosis. By improving calibration, the CC-LS ensures that clinicians can interpret and trust the predictive probabilities, making artificial intelligence-driven diagnostic tools more clinically viable. From a clinical standpoint, this heightened trust and interpretability can potentially lead to more timely and appropriate interventions, thereby optimizing patient outcomes and safety.</p></div><div><h3>Financial Disclosure(s)</h3><p>Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</p></div>","PeriodicalId":74363,"journal":{"name":"Ophthalmology science","volume":"4 6","pages":"Article 100555"},"PeriodicalIF":3.2,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524000915/pdfft?md5=427dfb03f669c0bff9531a39938549c2&pid=1-s2.0-S2666914524000915-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006395","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}