Ophthalmology science最新文献_第3页

Visualization of Scleral Flap Patency in Glaucoma Filtering Blebs Using OCT 利用 OCT 观察青光眼滤过性裂孔中巩膜瓣的通畅性

IF 3.2 Q1 OPHTHALMOLOGY

Ophthalmology science

Pub Date : 2024-08-23 DOI: 10.1016/j.xops.2024.100604

Jeremy C.K. Tan MD, FRANZCO , Matthew Roney BSc(Hons) , Anshoo Choudhary FRCOphth, MD , Mark Batterbury FRCOphth , Neeru A. Vallabh FRCOphth, PhD

Purpose

To investigate the use of anterior-segment OCT (AS-OCT) to visualize the aqueous outflow pathway and patency of the scleral flap in glaucoma filtration surgery blebs.

Design

Cross-sectional study.

Subjects

Two hundred five filtering blebs of 112 patients with glaucoma who had undergone trabeculectomy (Trab, n = 97) or deep sclerectomy (DS, n = 108) surgery with/without mitomycin-C (MMC).

Methods

Swept-source AS-OCT raster slices were used to image the Trab and DS blebs in sagittal and coronal planes using a standardized protocol. Bleb appearances were classified into 4 categories based on the scleral flap and sclerostomy/trabeculo-descemet window (TDW) appearance: A—sclerostomy/TDW not visible; B—sclerostomy/TDW visible but scleral flap indiscriminate from sclera; C—scleral flap distinct but edges adherent to surrounding sclera; D—scleral flap edges non adherent to surrounding sclera.

Main Outcome Measures

Surgical outcomes were classified into complete success (CS) (intraocular pressure [IOP] ≤18 mmHg with no medications), qualified success (QS) (IOP ≤18 with medications), and failure (F) (IOP >18 mmHg).

Results

The proportions of CS, QS, and F in the Trab and DS cohorts were 45.0% and 29.6%, 33.0% and 31.5%, 22.0% and 38.9% respectively, with a median postoperative follow-up of 8.4 years (standard deviation 7.9; interquartile range 3.2–9.0). In QS and failed blebs, category C (Trab, 53.7%; DS, 52.5%) accounted for the majority of scleral flap appearances, followed by categories A and B. Category D (86.0%; 71.9%) accounted for the majority of appearances in Trab and DS blebs with CS. There was a significantly greater proportion of MMC use in categories C and D compared with categories A and B in both Trab (P < 0.0001) and DS (P = 0.02) cohorts, demonstrating the association of intraoperative MMC use with increased patency of the scleral flap.

Conclusions

Swept-source AS-OCT may be used to visualize the position and patency of the sclerostomy/TDW and scleral flap in relation to surrounding structures in both sagittal and coronal planes. Although free scleral flap edges are primarily correlated with MMC use, it may also correlate with surgical success. Anterior-segment OCT may be used to complement subjective bleb grading at the slit lamp in the assessment of filtering blebs.

Financial Disclosure(s)

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

目的研究用前节段OCT（AS-OCT）观察青光眼滤过手术出血点的水流出路径和巩膜瓣的通畅性。研究对象112名青光眼患者的25个滤过性出血点，这些患者接受了小梁切除术（Trab，97人）或深部巩膜切除术（DS，108人）手术，术中使用/未使用丝裂霉素-C（MMC）。方法采用标准化方案，使用扫源AS-OCT光栅切片对Trab和DS出血点进行矢状面和冠状面成像。根据巩膜瓣和巩膜造口/泪囊窗（TDW）的外观，将出血点外观分为 4 类：A-硬化剂/TDW不可见；B-硬化剂/TDW可见，但巩膜瓣与巩膜无差别；C-巩膜瓣明显，但边缘与周围巩膜粘连；D-巩膜瓣边缘与周围巩膜无粘连。主要结果测量手术结果分为完全成功（CS）（眼压[IOP]≤18 mmHg，无药物治疗）、合格成功（QS）（眼压≤18，有药物治疗）和失败（F）（眼压>18 mmHg）。结果 Trab 和 DS 组中 CS、QS 和 F 的比例分别为 45.0% 和 29.6%、33.0% 和 31.5%、22.0% 和 38.9%，术后随访中位数为 8.4 年（标准差为 7.9；四分位间范围为 3.2-9.0）。在QS和失败出血中，C类（Trab，53.7%；DS，52.5%）占巩膜瓣出现的绝大多数，其次是A类和B类；在有CS的Trab和DS出血中，D类（86.0%；71.9%）占出现的绝大多数。在 Trab（P < 0.0001）和 DS（P = 0.02）队列中，C 类和 D 类使用 MMC 的比例明显高于 A 类和 B 类，这表明术中使用 MMC 与巩膜瓣的通畅性增加有关。虽然游离巩膜瓣边缘主要与 MMC 的使用有关，但它也可能与手术成功与否有关。在评估滤过性出血时，前段 OCT 可用于补充裂隙灯下的主观出血分级。

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引用次数: 0

Retinal Vessel Plexus Differentiation Based on OCT Angiography Using Deep Learning 利用深度学习基于光学视网膜血管造影术区分视网膜血管丛

IF 3.2 Q1 OPHTHALMOLOGY

Ophthalmology science

Pub Date : 2024-08-23 DOI: 10.1016/j.xops.2024.100605

Jamie L. Shaffer MS , Luis De Sisternes PhD , Anand E. Rajesh BS , Marian S. Blazes MD , Yuka Kihara PhD , Cecilia S. Lee MD, MS , Warren H. Lewis MS , Roger A. Goldberg MD , Niranchana Manivannan PhD , Aaron Y. Lee MD, MSCI

Purpose

Although structural OCT is traditionally used to differentiate the vascular plexus layers in OCT angiography (OCTA), the vascular plexuses do not always obey the retinal laminations. We sought to segment the superficial, deep, and avascular plexuses from OCTA images using deep learning without structural OCT image input or segmentation boundaries.

Design

Cross-sectional study.

Subjects

The study included 235 OCTA cubes from 33 patients for training and testing of the model.

Methods

From each OCTA cube, 3 weakly labeled images representing the superficial, deep, and avascular plexuses were obtained for a total of 705 starting images. Images were augmented with standard intensity and geometric transforms, and regions from adjacent plexuses were programmatically combined to create synthetic 2-class images for each OCTA cube. Images were partitioned on a per patient basis into training, validation, and reserved test groups to train and evaluate a U-Net based machine learning model. To investigate the generalization of the model, we applied the model to multiclass thin slabs from OCTA volumes and qualitatively observed the resulting b-scans.

Main Outcome Measures

Plexus segmentation performance was assessed quantitatively using Dice scores on a held-out test set.

Results

After training on single-class plexus images, our model achieved good results (Dice scores > 0.82) and was further improved when using the synthetic 2-class images (Dice >0.95). Although not trained on more complex multiclass slabs, the model performed plexus labeling on slab data, which indicates that the use of only OCTA data shows promise for segmenting the superficial, deep, and avascular plexuses without requiring OCT layer segmentations, and the use of synthetic 2-class images makes a significant performance improvement.

Conclusions

This study presents the use of OCTA data alone to segment the superficial, deep, and avascular plexuses of the retina, confirming that use of structural OCT layer segmentations as boundaries is not required.

Financial Disclosure(s)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

目的虽然结构性 OCT 传统上用于区分 OCT 血管造影（OCTA）中的血管丛层，但血管丛并不总是服从视网膜层。我们试图在没有结构性 OCT 图像输入或分割边界的情况下，利用深度学习从 OCTA 图像中分割浅层、深层和无血管丛。方法从每个 OCTA 立方体中获取 3 个代表浅层、深层和无血管丛的弱标记图像，共 705 个起始图像。利用标准强度和几何变换对图像进行增强，并通过程序将相邻神经丛的区域组合起来，为每个 OCTA 立方体创建合成的 2 类图像。每个患者的图像被分为训练组、验证组和保留测试组，以训练和评估基于 U-Net 的机器学习模型。为了研究该模型的通用性，我们将该模型应用于来自 OCTA 容量的多类薄片，并定性地观察了所得到的 b-scan.Main Outcome Measures神经丛分割性能采用在保留测试集上的 Dice 分数进行定量评估.Results在单类神经丛图像上进行训练后，我们的模型取得了良好的结果（Dice 分数为 0.82），在使用合成 2 类图像时得到了进一步提高（Dice 分数为 0.95）。这表明，仅使用 OCTA 数据就能分割浅层、深层和血管丛，而无需进行 OCT 层分割，而且使用合成 2 类图像能显著提高性能。结论本研究介绍了仅使用 OCTA 数据分割视网膜浅层、深层和血管丛的方法，证实无需使用结构性 OCT 图层分割作为边界。

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引用次数: 0

Sources of Discrepancy between Retinal Nerve Fiber Layer and Bruch’s Membrane Opening-Minimum Rim Width Thickness in Eyes with Glaucoma 青光眼患者视网膜神经纤维层与布氏膜开口-最小边缘宽度厚度之间差异的来源

IF 3.2 Q1 OPHTHALMOLOGY

Ophthalmology science

Pub Date : 2024-08-22 DOI: 10.1016/j.xops.2024.100601

Iris Zhuang MD, Maryam Ashrafkhorasani MD, Vahid Mohammadzadeh MD, Kouros Nouri-Mahdavi MD, MS

Purpose

To compare the discrepancies between circumpapillary retinal nerve fiber layer (RNFL) and Bruch’s membrane opening-minimum rim width (BMO-MRW) thickness in glaucoma eyes.

Design

A cross-sectional observational study.

Subjects

One hundred eighty-six eyes (118 patients) with glaucoma.

Methods

OCT optic nerve head volume scans of patients enrolled in the Advanced Glaucoma Progression Study at the final available visit were exported. The RNFL and BMO-MRW measurements were averaged into corresponding 7.5° sectors, and the nasal sector data were excluded from analyses. A 2-stage screening process was used to identify true mismatches between the RNFL and BMO-MRW measurements, in which either the RNFL or BMO-MRW value was in the less than first percentile range while its counterpart was in the greater than first percentile range on the temporal-superior-nasal-inferior-temporal curve. The prevalence of these mismatches was mapped, and corresponding images were reviewed to determine the underlying cause of these discrepancies.

Main Outcome Measures

Proportion of mismatches between RNFL and BMO-MRW, location of mismatches between RNFL and BMO-MRW, anatomical causes of mismatches between RNFL and BMO-MRW.

Results

Mismatch analysis revealed true mismatches between RNFL and BMO-MRW in 7.7% of sectors. High BMO-MRW with low corresponding RNFL mismatches were most frequently located at the 45° and 322.5° sectors, whereas high RNFL with corresponding low BMO-MRW mismatches peaked at the 75° sector. Large blood vessels accounted for 90.9% of high RNFL with low BMO-MRW mismatches. Small to large blood vessels accounted for 62.9% of high BMO-MRW with low RNFL mismatches; the remaining mismatches could be attributed to retinoschisis or inclusion of outer retinal layers in BMO-MRW measurements.

Conclusions

Although overall agreement between RNFL and BMO-MRW measurements is good in areas with advanced damage, blood vessels and other anatomical factors can cause discrepancies between the 2 types of structural measurements and need to be considered when evaluating the utility of such measurements for detection of change.

Financial Disclosure(s)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

目的比较青光眼眼球毛细血管视网膜神经纤维层（RNFL）和布鲁氏膜开口-最小边缘宽度（BMO-MRW）厚度之间的差异。方法导出参加高级青光眼进展研究的患者在最后一次就诊时的OCT视神经头体积扫描数据。将 RNFL 和 BMO-MRW 测量值平均到相应的 7.5° 扇区中，鼻腔扇区数据不纳入分析。采用两阶段筛选过程来识别 RNFL 和 BMO-MRW 测量值之间的真正不匹配，即 RNFL 或 BMO-MRW 值小于第一百分位数范围，而其对应值在颞-上-鼻-下-颞曲线上大于第一百分位数范围。主要结果测量RNFL和BMO-MRW之间不匹配的比例、RNFL和BMO-MRW之间不匹配的位置、RNFL和BMO-MRW之间不匹配的解剖学原因。结果不匹配分析显示7.7%的区域存在RNFL和BMO-MRW之间的真正不匹配。高BMO-MRW与低RNFL错配最常见于45°和322.5°区域，而高RNFL与低BMO-MRW错配在75°区域达到峰值。在 BMO-MRW 低错配的高 RNFL 中，大血管占 90.9%。在 BMO-MRW 偏高而 RNFL 偏低的不匹配中，小到大血管占 62.9%；其余的不匹配可能是由于视网膜裂孔或视网膜外层包含在 BMO-MRW 测量中。结论虽然RNFL和BMO-MRW测量值在晚期损伤区域的总体一致性良好，但血管和其他解剖因素会导致这两种结构测量值之间的差异，因此在评估此类测量值对检测变化的实用性时需要加以考虑。

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引用次数: 0

Diagnosing Glaucoma Based on the Ocular Hypertension Treatment Study Dataset Using Chat Generative Pre-Trained Transformer as a Large Language Model 使用聊天生成预训练变换器作为大语言模型，根据眼压高治疗研究数据集诊断青光眼

IF 3.2 Q1 OPHTHALMOLOGY

Ophthalmology science

Pub Date : 2024-08-22 DOI: 10.1016/j.xops.2024.100599

Hina Raja PhD , Xiaoqin Huang PhD , Mohammad Delsoz MD , Yeganeh Madadi PhD , Asma Poursoroush PhD , Asim Munawar PhD , Malik Y. Kahook MD , Siamak Yousefi PhD

Purpose

To evaluate the capabilities of Chat Generative Pre-Trained Transformer (ChatGPT), as a large language model (LLM), for diagnosing glaucoma using the Ocular Hypertension Treatment Study (OHTS) dataset, and comparing the diagnostic capability of ChatGPT 3.5 and ChatGPT 4.0.

Design

Prospective data collection study.

Participants

A total of 3170 eyes of 1585 subjects from the OHTS were included in this study.

Methods

We selected demographic, clinical, ocular, visual field, optic nerve head photo, and history of disease parameters of each participant and developed case reports by converting tabular data into textual format based on information from both eyes of all subjects. We then developed a procedure using the application programming interface of ChatGPT, a LLM-based chatbot, to automatically input prompts into a chat box. This was followed by querying 2 different generations of ChatGPT (versions 3.5 and 4.0) regarding the underlying diagnosis of each subject. We then evaluated the output responses based on several objective metrics.

Main Outcome Measures

Area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, and F1 score.

Results

Chat Generative Pre-Trained Transformer 3.5 achieved AUC of 0.74, accuracy of 66%, specificity of 64%, sensitivity of 85%, and F1 score of 0.72. Chat Generative Pre-Trained Transformer 4.0 obtained AUC of 0.76, accuracy of 87%, specificity of 90%, sensitivity of 61%, and F1 score of 0.92.

Conclusions

The accuracy of ChatGPT 4.0 in diagnosing glaucoma based on input data from OHTS was promising. The overall accuracy of ChatGPT 4.0 was higher than ChatGPT 3.5. However, ChatGPT 3.5 was found to be more sensitive than ChatGPT 4.0. In its current forms, ChatGPT may serve as a useful tool in exploring disease status of ocular hypertensive eyes when specific data are available for analysis. In the future, leveraging LLMs with multimodal capabilities, allowing for integration of imaging and diagnostic testing as part of the analyses, could further enhance diagnostic capabilities and enhance diagnostic accuracy.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

目的利用眼压治疗研究（OHTS）数据集评估作为大型语言模型（LLM）的聊天生成预训练变换器（ChatGPT）诊断青光眼的能力，并比较 ChatGPT 3.5 和 ChatGPT 4.0 的诊断能力。方法我们选取了每位受试者的人口统计学、临床、眼底、视野、视神经头照片和病史等参数，并根据所有受试者双眼的信息将表格数据转换成文本格式，编写了病例报告。然后，我们使用基于 LLM 的聊天机器人 ChatGPT 的应用程序接口开发了一个程序，将提示信息自动输入聊天框。随后，我们查询了两代不同的 ChatGPT（3.5 版和 4.0 版），以了解每个受试者的基本诊断情况。结果聊天生成预训练转换器 3.5 的 AUC 为 0.74，准确率为 66%，特异性为 64%，灵敏度为 85%，F1 得分为 0.72。结论基于 OHTS 输入数据的 ChatGPT 4.0 诊断青光眼的准确率很高。ChatGPT 4.0 的总体准确率高于 ChatGPT 3.5。不过，ChatGPT 3.5 的灵敏度要高于 ChatGPT 4.0。目前的 ChatGPT 可以作为一种有用的工具，在有具体数据可供分析的情况下，用于探索眼底高血压眼的疾病状态。未来，利用具有多模态功能的 LLM，将成像和诊断测试整合为分析的一部分，可以进一步增强诊断能力，提高诊断准确性。

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引用次数: 0

A Computational Framework for Intraoperative Pupil Analysis in Cataract Surgery 白内障手术术中瞳孔分析计算框架

IF 3.2 Q1 OPHTHALMOLOGY

Ophthalmology science

Pub Date : 2024-08-22 DOI: 10.1016/j.xops.2024.100597

Binh Duong Giap PhD , Karthik Srinivasan MD, MS , Ossama Mahmoud MD , Dena Ballouz MD , Jefferson Lustre BS , Keely Likosky BS , Shahzad I. Mian MD , Bradford L. Tannen MD, JD , Nambi Nallasamy MD

Purpose

Pupillary instability is a known risk factor for complications in cataract surgery. This study aims to develop and validate an innovative and reliable computational framework for the automated assessment of pupil morphologic changes during the various phases of cataract surgery.

Design

Retrospective surgical video analysis.

Subjects

Two hundred forty complete surgical video recordings, among which 190 surgeries were conducted without the use of pupil expansion devices (PEDs) and 50 were performed with the use of a PED.

Methods

The proposed framework consists of 3 stages: feature extraction, deep learning (DL)-based anatomy recognition, and obstruction (OB) detection/compensation. In the first stage, surgical video frames undergo noise reduction using a tensor-based wavelet feature extraction method. In the second stage, DL-based segmentation models are trained and employed to segment the pupil, limbus, and palpebral fissure. In the third stage, obstructed visualization of the pupil is detected and compensated for using a DL-based algorithm. A dataset of 5700 intraoperative video frames across 190 cataract surgeries in the BigCat database was collected for validating algorithm performance.

Main Outcome Measures

The pupil analysis framework was assessed on the basis of segmentation performance for both obstructed and unobstructed pupils. Classification performance of models utilizing the segmented pupil time series to predict surgeon use of a PED was also assessed.

Results

An architecture based on the Feature Pyramid Network model with Visual Geometry Group 16 backbone integrated with the adaptive wavelet tensor feature extraction feature extraction method demonstrated the highest performance in anatomy segmentation, with Dice coefficient of 96.52%. Incorporation of an OB compensation algorithm improved performance further (Dice 96.82%). Downstream analysis of framework output enabled the development of a Support Vector Machine–based classifier that could predict surgeon usage of a PED prior to its placement with 96.67% accuracy and area under the curve of 99.44%.

Conclusions

The experimental results demonstrate that the proposed framework (1) provides high accuracy in pupil analysis compared with human-annotated ground truth, (2) substantially outperforms isolated use of a DL segmentation model, and (3) can enable downstream analytics with clinically valuable predictive capacity.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

目的瞳孔不稳定是白内障手术并发症的一个已知风险因素。本研究旨在开发和验证一种创新、可靠的计算框架，用于自动评估白内障手术各阶段的瞳孔形态变化。方法所提出的框架包括三个阶段：特征提取、基于深度学习（DL）的解剖识别和阻塞（OB）检测/补偿。在第一阶段，使用基于张量的小波特征提取方法对手术视频帧进行降噪处理。在第二阶段，对基于 DL 的分割模型进行训练，并将其用于分割瞳孔、瞳孔边缘和睑裂。在第三阶段，使用基于 DL 的算法检测并补偿瞳孔的视觉障碍。为了验证算法的性能，我们收集了 BigCat 数据库中 190 例白内障手术的 5700 个术中视频帧数据集。结果基于特征金字塔网络模型的架构，以视觉几何组 16 为骨干，集成了自适应小波张量特征提取特征提取方法，在解剖分割方面表现最佳，Dice 系数达到 96.52%。加入转播补偿算法后，性能进一步提高（Dice 96.82%）。通过对框架输出的下游分析，开发出了基于支持向量机的分类器，该分类器可以预测外科医生在放置 PED 之前的使用情况，准确率为 96.67%，曲线下面积为 99.44%。结论实验结果表明，所提出的框架（1）与人类标注的基本事实相比，在瞳孔分析方面具有很高的准确性；（2）大大优于单独使用 DL 分割模型；（3）能够进行下游分析，具有临床价值的预测能力。

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引用次数: 0

Relationship between Neighborhood-Level Social Risk Factor Measures and Presenting Glaucoma Severity Utilizing Multilevel Modeling 利用多层次模型分析邻里层面的社会风险因素测量值与青光眼发病严重程度之间的关系

IF 3.2 Q1 OPHTHALMOLOGY

Ophthalmology science

Pub Date : 2024-08-22 DOI: 10.1016/j.xops.2024.100598

Patrice M. Hicks PhD, MPH , Ming-Chen Lu MS , Maria A. Woodward MD, MS , Leslie M. Niziol MS , Deborah Darnley-Fisch MD , Michele Heisler MD , Kenneth Resnicow PhD , David C. Musch PhD, MPH , Jamie Mitchell PhD, MSW , Roshanak Mehdipanah PhD, MS , Nauman R. Imami MD , Paula Anne Newman-Casey MD MS

Purpose

The neighborhood and built environment social determinant of health domain has several social risk factors (SRFs) that are modifiable through policy efforts. We investigated the impact of neighborhood-level SRFs on presenting glaucoma severity at a tertiary eye care center.

Design

A cross-sectional study from August 2012 to May 2022 in the University of Michigan electronic health record (EHR).

Participants

Patients with a diagnosis of any open-angle glaucoma with ≥1 eye care visit at the University of Michigan Kellogg Eye Center and ≥1 reliable visual field (VF).

Methods

Participants who met inclusion criteria were identified by International Classification of Diseases ninth and tenth revision codes (365.x/H40.x). Data extracted from the EHR included patient demographics, address, presenting mean deviation (MD), and VF reliability. Addresses were mapped to SRF measures at the census tract, block group, and county levels. Multilevel linear regression models were used to estimate the fixed effects of each SRF on MD, after adjusting for patient-level demographic factors and a random effect for neighborhood. Interactions between each SRF measure with patient-level race and Medicaid status were tested for an additive effect on MD.

Main Outcome Measures

The main outcome measure was the effect of SRF on presenting MD.

Results

In total, 4428 patients were included in the analysis who were, on average, 70.3 years old (standard deviation = 11.9), 52.6% self-identified as female, 75.8% self-identified as White race, and 8.9% had Medicaid. The median value of presenting MD was −4.94 decibels (dB) (interquartile range = −11.45 to −2.07 dB). Neighborhood differences accounted for 4.4% of the variability in presenting MD. Neighborhood-level measures, including worse area deprivation (estimate, β = −0.31 per 1-unit increase; P < 0.001), increased segregation (β = −0.92 per 0.1-unit increase in Theil’s H index; P < 0.001), and increased neighborhood Medicaid (β = −0.68; P < 0.001) were associated with worse presenting MD. Significant interaction effects with race and Medicaid status were found in several neighborhood-level SRF measures.

Conclusions

Although patients’ neighborhood SRF measures accounted for a minority of the variability in presenting MD, most neighborhood-level SRFs are modifiable and were associated with clinically meaningful differences in presenting MD. Policies that aim to reduce neighborhood inequities by addressing allocation of resources could have lasting impacts on vision outcomes.

Financial Disclosure(s)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

目的邻里和建筑环境是健康的社会决定因素，其中有几个社会风险因素 (SRF)，可通过政策努力加以改变。我们在密歇根大学的电子健康记录（EHR）中对 2012 年 8 月至 2022 年 5 月期间的横断面研究进行了调查。方法根据《国际疾病分类》第九版和第十版修订代码 (365.x/H40.x) 确定符合纳入标准的患者。从电子病历（EHR）中提取的数据包括患者的人口统计学特征、地址、出现的平均偏差（MD）和 VF 可靠性。地址被映射到人口普查区、街区组和县一级的 SRF 指标。多层次线性回归模型用于估算每个 SRF 对 MD 的固定效应，然后再对患者层面的人口统计学因素和邻里随机效应进行调整。结果总共有 4428 名患者被纳入分析，他们的平均年龄为 70.3 岁（标准差 = 11.9），52.6% 自认为是女性，75.8% 自认为是白种人，8.9% 有医疗补助。出现 MD 的中位值为-4.94 分贝（dB）（四分位间范围 = -11.45 至 -2.07dB）。邻近地区的差异占呈现 MD 变异的 4.4%。邻里层面的衡量指标，包括更严重的地区贫困（估计值，β = 每增加 1 个单位为 -0.31；P <；0.001）、更严重的隔离（Theil's H 指数每增加 0.1 个单位为 β = -0.92；P <；0.001）和更严重的邻里医疗补助（Medicaid）（β = -0.68；P <；0.001），均与更严重的出现 MD 相关。结论虽然患者的邻里 SRF 指标只占现症 MD 变异的一小部分，但大多数邻里 SRF 是可以改变的，并且与现症 MD 的临床意义差异有关。旨在通过解决资源分配问题减少邻里不平等的政策可能会对视力结果产生持久影响。

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引用次数: 0

The Impact of Race, Ethnicity, and Sex on Fairness in Artificial Intelligence for Glaucoma Prediction Models 种族、民族和性别对人工智能青光眼预测模型公平性的影响

IF 3.2 Q1 OPHTHALMOLOGY

Ophthalmology science

Pub Date : 2024-08-14 DOI: 10.1016/j.xops.2024.100596

Rohith Ravindranath MS , Joshua D. Stein MD, MS , Tina Hernandez-Boussard , A. Caroline Fisher , Sophia Y. Wang MD, MS

Objective

Despite advances in artificial intelligence (AI) in glaucoma prediction, most works lack multicenter focus and do not consider fairness concerning sex, race, or ethnicity. This study aims to examine the impact of these sensitive attributes on developing fair AI models that predict glaucoma progression to necessitating incisional glaucoma surgery.

Design

Database study.

Participants

Thirty-nine thousand ninety patients with glaucoma, as identified by International Classification of Disease codes from 7 academic eye centers participating in the Sight OUtcomes Research Collaborative.

Methods

We developed XGBoost models using 3 approaches: (1) excluding sensitive attributes as input features, (2) including them explicitly as input features, and (3) training separate models for each group. Model input features included demographic details, diagnosis codes, medications, and clinical information (intraocular pressure, visual acuity, etc.), from electronic health records. The models were trained on patients from 5 sites (N = 27 999) and evaluated on a held-out internal test set (N = 3499) and 2 external test sets consisting of N = 1550 and N = 2542 patients.

Main Outcomes and Measures

Area under the receiver operating characteristic curve (AUROC) and equalized odds on the test set and external sites.

Results

Six thousand six hundred eighty-two (17.1%) of 39 090 patients underwent glaucoma surgery with a mean age of 70.1 (standard deviation 14.6) years, 54.5% female, 62.3% White, 22.1% Black, and 4.7% Latinx/Hispanic. We found that not including the sensitive attributes led to better classification performance (AUROC: 0.77–0.82) but worsened fairness when evaluated on the internal test set. However, on external test sites, the opposite was true: including sensitive attributes resulted in better classification performance (AUROC: external #1 - [0.73–0.81], external #2 - [0.67–0.70]), but varying degrees of fairness for sex and race as measured by equalized odds.

Conclusions

Artificial intelligence models predicting whether patients with glaucoma progress to surgery demonstrated bias with respect to sex, race, and ethnicity. The effect of sensitive attribute inclusion and exclusion on fairness and performance varied based on internal versus external test sets. Prior to deployment, AI models should be evaluated for fairness on the target population.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

目的尽管人工智能（AI）在青光眼预测方面取得了进展，但大多数研究都缺乏多中心关注，也没有考虑性别、种族或民族方面的公平性。本研究旨在研究这些敏感属性对开发公平的人工智能模型的影响，这些模型可预测青光眼进展到必须进行青光眼切开手术的程度。方法我们使用三种方法开发了 XGBoost 模型：(1) 排除敏感属性作为输入特征；(2) 明确将敏感属性作为输入特征；(3) 为每个组别训练单独的模型。模型输入特征包括电子健康记录中的人口统计学细节、诊断代码、药物和临床信息（眼压、视力等）。模型在 5 个站点（N = 27999）的患者身上进行了训练，并在内部测试集（N = 3499）和由 N = 1550 和 N = 2542 名患者组成的 2 个外部测试集上进行了评估。结果39 090 名患者中有 662 人（17.1%）接受了青光眼手术，平均年龄 70.1 岁（标准差 14.6），54.5% 为女性，62.3% 为白人，22.1% 为黑人，4.7% 为拉丁/西班牙裔。我们发现，在内部测试集上进行评估时，不包含敏感属性会提高分类性能（AUROC：0.77-0.82），但会降低公平性。然而，在外部测试点上，情况却恰恰相反：包含敏感属性会带来更好的分类性能（AUROC：外部 #1 - [0.73-0.81]，外部 #2 - [0.67-0.70]），但根据等化几率衡量，性别和种族的公平性程度各不相同。敏感属性的包含和排除对公平性和性能的影响因内部测试集和外部测试集而异。在部署之前，应评估人工智能模型在目标人群中的公平性。财务披露专利或商业披露见本文末尾的脚注和披露。

{"title":"The Impact of Race, Ethnicity, and Sex on Fairness in Artificial Intelligence for Glaucoma Prediction Models","authors":"Rohith Ravindranath MS , Joshua D. Stein MD, MS , Tina Hernandez-Boussard , A. Caroline Fisher , Sophia Y. Wang MD, MS","doi":"10.1016/j.xops.2024.100596","DOIUrl":"10.1016/j.xops.2024.100596","url":null,"abstract":"<div><h3>Objective</h3><div>Despite advances in artificial intelligence (AI) in glaucoma prediction, most works lack multicenter focus and do not consider fairness concerning sex, race, or ethnicity. This study aims to examine the impact of these sensitive attributes on developing fair AI models that predict glaucoma progression to necessitating incisional glaucoma surgery.</div></div><div><h3>Design</h3><div>Database study.</div></div><div><h3>Participants</h3><div>Thirty-nine thousand ninety patients with glaucoma, as identified by International Classification of Disease codes from 7 academic eye centers participating in the Sight OUtcomes Research Collaborative.</div></div><div><h3>Methods</h3><div>We developed XGBoost models using 3 approaches: (1) excluding sensitive attributes as input features, (2) including them explicitly as input features, and (3) training separate models for each group. Model input features included demographic details, diagnosis codes, medications, and clinical information (intraocular pressure, visual acuity, etc.), from electronic health records. The models were trained on patients from 5 sites (N = 27 999) and evaluated on a held-out internal test set (N = 3499) and 2 external test sets consisting of N = 1550 and N = 2542 patients.</div></div><div><h3>Main Outcomes and Measures</h3><div>Area under the receiver operating characteristic curve (AUROC) and equalized odds on the test set and external sites.</div></div><div><h3>Results</h3><div>Six thousand six hundred eighty-two (17.1%) of 39 090 patients underwent glaucoma surgery with a mean age of 70.1 (standard deviation 14.6) years, 54.5% female, 62.3% White, 22.1% Black, and 4.7% Latinx/Hispanic. We found that not including the sensitive attributes led to better classification performance (AUROC: 0.77–0.82) but worsened fairness when evaluated on the internal test set. However, on external test sites, the opposite was true: including sensitive attributes resulted in better classification performance (AUROC: external #1 - [0.73–0.81], external #2 - [0.67–0.70]), but varying degrees of fairness for sex and race as measured by equalized odds.</div></div><div><h3>Conclusions</h3><div>Artificial intelligence models predicting whether patients with glaucoma progress to surgery demonstrated bias with respect to sex, race, and ethnicity. The effect of sensitive attribute inclusion and exclusion on fairness and performance varied based on internal versus external test sets. Prior to deployment, AI models should be evaluated for fairness on the target population.</div></div><div><h3>Financial Disclosures</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 1","pages":"Article 100596"},"PeriodicalIF":3.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524001325/pdfft?md5=a7947c05f20d148756a130892f021b56&pid=1-s2.0-S2666914524001325-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311980","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}

引用次数: 0

XOLARIS: A 24-Month, Prospective, Natural History Study of 201 Participants with Retinitis Pigmentosa GTPase Regulator-Associated X-Linked Retinitis Pigmentosa XOLARIS：对 201 名视网膜色素变性 GTPase 调节器相关 X 连锁视网膜色素变性患者进行为期 24 个月的前瞻性自然病史研究

IF 3.2 Q1 OPHTHALMOLOGY

Ophthalmology science

Pub Date : 2024-08-13 DOI: 10.1016/j.xops.2024.100595

Robert E. MacLaren DPhil, FACS , Jacque L. Duncan MD , M. Dominik Fischer MD, DPhil , Byron L. Lam MD , Isabelle Meunier MD, PhD , Mark E. Pennesi MD, PhD , Eeva-Marja K. Sankila MD, PhD , James A. Gow MD, MBA , Jiang Li MS, MA , So-Fai Tsang MD

Objective

To improve the understanding of the natural disease progression of retinitis pigmentosa GTPase regulator (RPGR)-associated X-linked retinitis pigmentosa (XLRP).

Design

A multicenter, prospective, observational natural history study over 24 months.

Participants

Male participants aged ≥7 years with a pathogenic variant in the RPGR gene, a best-corrected visual acuity (BCVA) score of ≥34 ETDRS letters, and a mean 68-loci retinal sensitivity (assessed by microperimetry) of 0.1 to 20 decibels (dB).

Methods

Participants were divided into subgroups based on their BCVA score at baseline: 34 to 73 (lower BCVA) or ≥74 (higher BCVA) ETDRS letters. There were 7 visits over 24 months.

Main Outcome Measures

Change from baseline in BCVA, retinal sensitivity, low luminance visual acuity (LLVA), fixation stability, contrast sensitivity, visual field, anatomical measures, 25-item Visual Function Questionnaire (VFQ-25), intraocular pressure, and adverse events (AEs).

Results

Overall, 201 participants were included. The mean (standard deviation [SD]) age was 30.3 (11.9) years in the lower BCVA subgroup (n = 170) and 27.7 (10.1) years in the higher BCVA subgroup (n = 31). The study eye baseline mean (SD) BCVA scores were 59.4 (10.30) and 77.3 (3.95) in the lower and higher BCVA subgroups, respectively; the lower BCVA subgroup had lower retinal sensitivity in the study eye at baseline than the higher BCVA subgroup. Over 24 months, there were small observed changes in BCVA, retinal sensitivity, LLVA, fixation, contrast sensitivity, and fundus photography findings. There were observed mean (SD) changes at 24 months in the lower and higher BCVA subgroups of −1.01 (4.67) and 0.03 (5.83) dB-steradians in the volume of full-field hill of vision, −330.6 (869.51) and −122.7 (22.01) μm in distance from foveal center to the nearest border of preserved fundus autofluorescence, −104.3 (277.80) and −207.1 (171.01) μm in central ellipsoid width, and −2.8 (9.7) and −0.6 (7.6) in VFQ-25 composite score, respectively. There was 1 death from completed suicide. There were no ocular serious adverse events, and most AEs were mild/moderate.

Conclusions

This study provides evidence of the slow natural progression of XLRP over 24 months in both subgroups and provides important functional, anatomical, and safety data.

Financial Disclosure(s)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

设计一项为期 24 个月的多中心、前瞻性、观察性自然病史研究。方法根据基线时的 BCVA 得分将参与者分为几个亚组：34 至 73 分（较低的 BCVA）或≥74 分（较高的 BCVA）ETDRS 字母。主要结果测量BCVA、视网膜敏感度、低亮度视力（LLVA）、固定稳定性、对比敏感度、视野、解剖测量、25项视觉功能问卷（VFQ-25）、眼压和不良事件（AEs）与基线相比的变化。BCVA 较低亚组（n = 170）的平均年龄（标准差 [SD] ）为 30.3（11.9）岁，BCVA 较高亚组（n = 31）的平均年龄（标准差 [SD] ）为 27.7（10.1）岁。低BCVA亚组和高BCVA亚组的研究眼基线平均（标度）BCVA评分分别为59.4（10.30）和77.3（3.95）；低BCVA亚组的研究眼基线视网膜灵敏度低于高BCVA亚组。在 24 个月中，观察到的 BCVA、视网膜敏感度、LLVA、定点、对比敏感度和眼底摄影结果的变化较小。在 24 个月时，观察到 BCVA 值较低和较高亚组的全视野山丘视力分别为-1.01 (4.67) 和 0.03 (5.83) dB-steradians，-330.6 (869.51) 和 -122.7 (22.01) μm-steradians，而 BCVA 值较低和较高亚组的全视野山丘视力分别为-1.01 (4.67) 和 0.03 (5.83) dB-steradians。01）μm，中心椭圆体宽度分别为-104.3（277.80）和-207.1（171.01）μm，VFQ-25 综合评分分别为-2.8（9.7）和-0.6（7.6）。有 1 例患者死于自杀。没有发生眼部严重不良事件，大多数不良事件为轻度/中度。结论这项研究提供了两个亚组的XLRP在24个月内缓慢自然进展的证据，并提供了重要的功能、解剖和安全性数据。

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引用次数: 0

Evaluation of the Appropriateness and Readability of ChatGPT-4 Responses to Patient Queries on Uveitis 评估 ChatGPT-4 对患者有关葡萄膜炎询问的回复的适当性和可读性

IF 3.2 Q1 OPHTHALMOLOGY

Ophthalmology science

Pub Date : 2024-08-08 DOI: 10.1016/j.xops.2024.100594

S. Saeed Mohammadi MD , Anadi Khatri MD , Tanya Jain MBBS, DNB , Zheng Xian Thng MD , Woong-sun Yoo MD, PhD , Negin Yavari MD , Vahid Bazojoo MD , Azadeh Mobasserian MD , Amir Akhavanrezayat MD , Ngoc Trong Tuong Than MD , Osama Elaraby MD , Battuya Ganbold MD , Dalia El Feky MD , Ba Trung Nguyen MD , Cigdem Yasar MD , Ankur Gupta MD, MS , Jia-Horung Hung MD , Quan Dong Nguyen MD, MSc

Purpose

To compare the utility of ChatGPT-4 as an online uveitis patient education resource with existing patient education websites.

Design

Evaluation of technology.

Participants

Not applicable.

Methods

The term “uveitis” was entered into the Google search engine, and the first 8 nonsponsored websites were selected to be enrolled in the study. Information regarding uveitis for patients was extracted from Healthline, Mayo Clinic, WebMD, National Eye Institute, Ocular Uveitis and Immunology Foundation, American Academy of Ophthalmology, Cleveland Clinic, and National Health Service websites. ChatGPT-4 was then prompted to generate responses about uveitis in both standard and simplified formats. To generate the simplified response, the following request was added to the prompt: 'Please provide a response suitable for the average American adult, at a sixth-grade comprehension level.’ Three dual fellowship-trained specialists, all masked to the sources, graded the appropriateness of the contents (extracted from the existing websites) and responses (generated responses by ChatGPT-4) in terms of personal preference, comprehensiveness, and accuracy. Additionally, 5 readability indices, including Flesch Reading Ease, Flesch–Kincaid Grade Level, Gunning Fog Index, Coleman–Liau Index, and Simple Measure of Gobbledygook index were calculated using an online calculator, Readable.com, to assess the ease of comprehension of each answer.

Main Outcome Measures

Personal preference, accuracy, comprehensiveness, and readability of contents and responses about uveitis.

Results

A total of 497 contents and responses, including 71 contents from existing websites, 213 standard responses, and 213 simplified responses from ChatGPT-4 were recorded and graded. Standard ChatGPT-4 responses were preferred and perceived to be more comprehensive by dually trained (uveitis and retina) specialist ophthalmologists while maintaining similar accuracy level compared with existing websites. Moreover, simplified ChatGPT-4 responses matched almost all existing websites in terms of personal preference, accuracy, and comprehensiveness. Notably, almost all readability indices suggested that standard ChatGPT-4 responses demand a higher educational level for comprehension, whereas simplified responses required lower level of education compared with the existing websites.

Conclusions

This study shows that ChatGPT can provide patients with an avenue to access comprehensive and accurate information about uveitis, tailored to their educational level.

Financial Disclosure(s)

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

目的比较 ChatGPT-4 作为在线葡萄膜炎患者教育资源与现有患者教育网站的实用性。方法在谷歌搜索引擎中输入 "葡萄膜炎 "一词，然后选择前 8 个非赞助网站作为研究对象。从 Healthline、Mayo Clinic、WebMD、美国国家眼科研究所、眼葡萄膜炎和免疫学基金会、美国眼科学会、克利夫兰诊所和国家卫生服务网站上提取患者葡萄膜炎相关信息。然后提示 ChatGPT-4 生成标准和简化格式的葡萄膜炎回复。为了生成简化回复，在提示中添加了以下要求：请提供适合普通美国成年人六年级理解水平的回复。三位接受过双重研究员培训的专家均不透露信息来源，他们根据个人偏好、全面性和准确性对内容（从现有网站中提取）和回复（由 ChatGPT-4 生成的回复）的适当性进行了评分。此外，还使用在线计算器 Readable.com 计算了 5 个可读性指数，包括 Flesch Reading Ease、Flesch-Kincaid Grade Level、Gunning Fog Index、Coleman-Liau Index 和 Simple Measure of Gobbledygook Index，以评估每个答案的易懂程度。结果共记录了497条内容和回答，包括71条来自现有网站的内容、213条标准回答和213条来自ChatGPT-4的简化回答，并进行了评分。受过双重培训（葡萄膜炎和视网膜）的专科眼科医生更喜欢标准版 ChatGPT-4 回答，并认为其更全面，同时与现有网站相比保持了相似的准确度。此外，简化版 ChatGPT-4 在个人偏好、准确性和全面性方面几乎与所有现有网站相匹配。值得注意的是，几乎所有的可读性指数都表明，标准版 ChatGPT-4 回答需要较高的教育水平才能理解，而与现有网站相比，简化版回答需要较低的教育水平。

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引用次数: 0

Comparison between Spectral-Domain and Swept-Source OCT Angiography for the Measurement of Persistent Hypertransmission Defects in Age-Related Macular Degeneration 光谱域和扫描源 OCT 血管造影在测量年龄相关性黄斑变性的持续性高传输缺陷方面的比较

IF 3.2 Q1 OPHTHALMOLOGY

Ophthalmology science

Pub Date : 2024-08-07 DOI: 10.1016/j.xops.2024.100593

Gissel Herrera MD , Mengxi Shen MD, PhD , Omer Trivizki MD , Jeremy Liu MD , Yingying Shi MD , Farhan E. Hiya MD , Jianqing Li MD , Yuxuan Cheng BS , Jie Lu MD, MS , Qinqin Zhang PhD , Robert C. O’Brien PhD , Giovanni Gregori PhD , Ruikang K. Wang PhD , Philip J. Rosenfeld MD, PhD

Purpose

Spectral-domain OCT angiography (SD-OCTA) scans were tested in an algorithm developed for use with swept-source OCT angiography (SS-OCTA) scans to determine if SD-OCTA scans yielded similar results for the detection and measurement of persistent choroidal hypertransmission defects (hyperTDs).

Design

Retrospective study.

Participants

Forty pairs of scans from 32 patients with late-stage nonexudative age-related macular degeneration (AMD).

Methods

Patients underwent both SD-OCTA and SS-OCTA imaging at the same visit using the 6 × 6 mm OCTA scan patterns. Using a semiautomatic algorithm that helped with outlining the hyperTDs, 2 graders independently validated persistent hyperTDs, which are defined as having a greatest linear dimension ≥250 μm on the en face images generated using a slab extending from 64 to 400 μm beneath Bruch’s membrane. The number of lesions and square root (sqrt) total area of the hyperTDs were obtained from the algorithm using each imaging method.

Main Outcome Measures

The mean sqrt area measurements and the number of hyperTDs were compared.

Results

The number of lesions and sqrt total area of the hyperTDs were highly concordant between the 2 instruments (r_c = 0.969 and r_c = 0.999, respectively). The mean number of hyperTDs was 4.3 ± 3.1 for SD-OCTA scans and 4.5 ± 3.3 for SS-OCTA scans (P = 0.06). The mean sqrt total area measurements were 1.16 ± 0.64 mm for the SD-OCTA scans and 1.17 ± 0.65 mm for the SS-OCTA scans (P < 0.001). Because of the small standard error of the differences, the mean difference between the scans was statistically significant but not clinically significant.

Conclusions

Spectral-domain OCTA scans provide similar results to SS-OCTA scans when used to obtain the number and area measurements of persistent hyperTDs through a semiautomated algorithm previously developed for SS-OCTA. This facilitates the detection of atrophy with a more widely available scan pattern and the longitudinal study of early to late-stage AMD.

Financial Disclosure(s)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

目的用一种为扫源 OCT 血管造影（SS-OCTA）扫描而开发的算法对谱域 OCT 血管造影（SD-OCTA）扫描进行测试，以确定 SD-OCTA 扫描在检测和测量持续性脉络膜高透射缺陷（hyperTDs）方面是否能产生相似的结果。方法患者在同一次就诊中使用 6 × 6 mm OCTA 扫描模式接受 SD-OCTA 和 SS-OCTA 成像检查。使用半自动算法帮助勾画超视网膜病变的轮廓，由两名分级人员独立验证持续性超视网膜病变，超视网膜病变的定义是：在布鲁氏膜下 64 到 400 μm 范围内生成的正视图像上，最大线性尺寸≥250 μm。主要结果测量比较了平均平方根面积测量值和 hyperTDs 的数量。结果两种仪器的病变数量和 hyperTDs 的平方根总面积高度一致（rc = 0.969 和 rc = 0.999）。SD-OCTA 扫描的高TD平均数量为 4.3 ± 3.1，SS-OCTA 扫描的高TD平均数量为 4.5 ± 3.3（P = 0.06）。SD-OCTA 扫描的平均平方总面积测量值为 1.16 ± 0.64 毫米，SS-OCTA 扫描的平均平方总面积测量值为 1.17 ± 0.65 毫米（P = 0.001）。由于差异的标准误差较小，扫描之间的平均差异具有统计学意义，但无临床意义。结论通过之前为 SS-OCTA 开发的半自动化算法，用谱域 OCTA 扫描获得持续性超视网膜病变的数量和面积测量结果与 SS-OCTA 扫描结果相似。这有助于使用更广泛的扫描模式检测萎缩，并对早期至晚期AMD进行纵向研究。

{"title":"Comparison between Spectral-Domain and Swept-Source OCT Angiography for the Measurement of Persistent Hypertransmission Defects in Age-Related Macular Degeneration","authors":"Gissel Herrera MD , Mengxi Shen MD, PhD , Omer Trivizki MD , Jeremy Liu MD , Yingying Shi MD , Farhan E. Hiya MD , Jianqing Li MD , Yuxuan Cheng BS , Jie Lu MD, MS , Qinqin Zhang PhD , Robert C. O’Brien PhD , Giovanni Gregori PhD , Ruikang K. Wang PhD , Philip J. Rosenfeld MD, PhD","doi":"10.1016/j.xops.2024.100593","DOIUrl":"10.1016/j.xops.2024.100593","url":null,"abstract":"<div><h3>Purpose</h3><p>Spectral-domain OCT angiography (SD-OCTA) scans were tested in an algorithm developed for use with swept-source OCT angiography (SS-OCTA) scans to determine if SD-OCTA scans yielded similar results for the detection and measurement of persistent choroidal hypertransmission defects (hyperTDs).</p></div><div><h3>Design</h3><p>Retrospective study.</p></div><div><h3>Participants</h3><p>Forty pairs of scans from 32 patients with late-stage nonexudative age-related macular degeneration (AMD).</p></div><div><h3>Methods</h3><p>Patients underwent both SD-OCTA and SS-OCTA imaging at the same visit using the 6 × 6 mm OCTA scan patterns. Using a semiautomatic algorithm that helped with outlining the hyperTDs, 2 graders independently validated persistent hyperTDs, which are defined as having a greatest linear dimension ≥250 μm on the en face images generated using a slab extending from 64 to 400 μm beneath Bruch’s membrane. The number of lesions and square root (sqrt) total area of the hyperTDs were obtained from the algorithm using each imaging method.</p></div><div><h3>Main Outcome Measures</h3><p>The mean sqrt area measurements and the number of hyperTDs were compared.</p></div><div><h3>Results</h3><p>The number of lesions and sqrt total area of the hyperTDs were highly concordant between the 2 instruments (r<sub>c</sub> = 0.969 and r<sub>c</sub> = 0.999, respectively). The mean number of hyperTDs was 4.3 ± 3.1 for SD-OCTA scans and 4.5 ± 3.3 for SS-OCTA scans (<em>P</em> = 0.06). The mean sqrt total area measurements were 1.16 ± 0.64 mm for the SD-OCTA scans and 1.17 ± 0.65 mm for the SS-OCTA scans (<em>P</em> < 0.001). Because of the small standard error of the differences, the mean difference between the scans was statistically significant but not clinically significant.</p></div><div><h3>Conclusions</h3><p>Spectral-domain OCTA scans provide similar results to SS-OCTA scans when used to obtain the number and area measurements of persistent hyperTDs through a semiautomated algorithm previously developed for SS-OCTA. This facilitates the detection of atrophy with a more widely available scan pattern and the longitudinal study of early to late-stage AMD.</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":"5 1","pages":"Article 100593"},"PeriodicalIF":3.2,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666914524001295/pdfft?md5=1d219e52f1d3cce12729e047e485c32e&pid=1-s2.0-S2666914524001295-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232580","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}

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