Improved Glaucoma Monitoring Using Artificial-Intelligence Enabled Dashboard

使用人工智能仪表板改进青光眼监测

• 批准号: 10683037
• 负责人: Siamak Yousefi
• 金额: $ 10万
• 依托单位: UNIVERSITY OF TENNESSEE HEALTH SCI CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10683037
• 关键词: 3-Dimensional; Address; Adopted; Affect; Algorithms; Artificial Intelligence; Axon; Big Data to Knowledge; Clinical; Clinical Trials; Communities; Complement; Computer Systems; Consensus; Data; Data Analyses; Data Set; Detection; Diagnosis; Disease; Early Diagnosis; Eye; Floor; Glaucoma; Goals; Health; High Performance Computing; Image; Incidence; Knowledge; Learning; Linear Regressions; Location; Machine Learning; Maps; Measurement; Measures; Methods; Modeling; Monitor; Ophthalmology; Optic Disk; Optic Nerve; Optical Coherence Tomography; Outcome; Patients; Pattern; Persons; Principal Component Analysis; Prognosis; Quality of life; Reproducibility; Research; Retinal Ganglion Cells; Savings; Scanning; Severities; Specific qualifier value; Specificity; System; Techniques; Technology; Test Result; Testing; Treatment Cost; United States National Institutes of Health; Vision; Visual; Visual Fields; Visualization; Visualization software; base; computerized tools; dashboard; field study; glaucoma test; hands-on learning; improved; large datasets; longitudinal dataset; multidimensional data; open source tool; preservation; retinal nerve fiber layer; three-dimensional visualization; treatment strategy; unsupervised learning

Detecting functional and structural loss due to glaucoma is critical to making treatment decisions with the goal of preserving vision and maintaining quality of life. However, most of the approaches for glaucoma assessment through visual fields (VFs) or optical coherence tomography (OCT) measurements have several limitations that poses critical challenge to their clinical utility. Identifying glaucoma-induced changes from a sequence of VF or OCT data is challenging either if the patients is in the early stages of the disease with subtle manifested structural and functional signs or if the patients are in the later stages of the disease with significant VF variability and OCT flooring effect. A major limitation of the current glaucoma monitoring techniques is that they generate a binary outcome of whether the glaucoma is worsening or not while current high-throughput data (e.g., OCT) has more information than a binary outcome. Another major drawback of some of these approaches is that they rely on traditional paradigms for progression detection such as linear regression. However, rates of glaucomatous progression may be non-linear and rapid, particularly during the later stages of the disease. Another limitation is that ad-hoc rules are adopted to define glaucoma progression while objective criteria are required to define thresholds for progression. Finally, a major deficiency of most of these methods is that they lack advanced visualization and interpretation. We propose to address these limitations by developing artificial intelligence (AI)-enabled visualization tools for effectively monitoring the functional and structural loss in patients with glaucoma. This approach provides qualitative and quantitative means to monitor 1) global visual functional and structural worsening, 2) extent of loss in hemifields, and 3) local patterns of functional and structural loss on advanced 2-D visualization tools. To achieve these objectives, we have assembled a team of interdisciplinary experts with access to large clinically annotated glaucoma data. The central hypothesis of this proposal is that advanced interpretable machine learning applied to a complete profile of VFs in all test locations (e.g., 54 in 24-2 system) and OCT-derived measurements of retinal nerve fiber layer (RNFL) (e.g., 768 A-scans around the optic disc and 7 global sectoral regions) can objectively and automatically learn and quantify the most important features, yielding a more specific and sensitive means for monitoring of glaucoma worsening than current subjectively-specified or statistically-identified approaches. We also hypothesize that machine learning can provide interpretable models with several layers of glaucoma knowledge that may provide a promising complement to current glaucoma assessment tests. Our proposed studies may offer substantial improvements in prognosis and management of glaucoma through effective use of analysis and visualization to improve glaucoma management and making more informed treatment options.

检测青光眼引起的功能和结构损失对于制定目标治疗决策至关重要 保护视力和维持生活质量。然而，大多数青光眼评估方法 通过视野 (VF) 或光学相干断层扫描 (OCT) 测量有几个限制： 对其临床实用性提出了严峻的挑战。 从 VF 或 OCT 数据序列中识别青光眼引起的变化具有挑战性 处于疾病的早期阶段，具有微妙的结构和功能体征，或者如果患者 在疾病的后期阶段，具有显着的 VF 变异性和 OCT 地板效应。一个主要限制是 目前的青光眼监测技术是，它们生成青光眼是否患有青光眼的二元结果 恶化与否，而当前的高通量数据（例如 OCT）比二进制结果具有更多信息。 其中一些方法的另一个主要缺点是它们依赖于传统的进展范式 检测，例如线性回归。然而，青光眼的进展速度可能是非线性且快速的， 特别是在疾病的后期阶段。另一个限制是采用临时规则来定义 青光眼进展，而需要客观标准来定义进展阈值。最后，一个主要的 大多数这些方法的缺点是缺乏先进的可视化和解释。 我们建议通过开发支持人工智能 (AI) 的可视化工具来解决这些限制 有效监测青光眼患者的功能和结构损失。这种方法提供了 定性和定量手段监测 1) 整体视觉功能和结构恶化，2) 半视野的损失，以及 3) 先进的二维可视化工具上的功能和结构损失的局部模式。到 为了实现这些目标，我们组建了一支跨学科专家团队，可以接触大型临床 带注释的青光眼数据。 该提案的中心假设是先进的可解释机器学习应用于完整的 所有测试位置（例如 24-2 系统中的 54 个）的 VF 轮廓以及 OCT 衍生的视网膜神经测量 纤维层（RNFL）（例如，视神经盘周围的 768 个 A 扫描和 7 个全局扇形区域）可以客观且 自动学习和量化最重要的特征，从而产生更具体和更灵敏的方法 与目前主观指定或统计确定的方法相比，监测青光眼恶化情况。 我们还假设机器学习可以提供具有多层青光眼的可解释模型 这些知识可能为当前的青光眼评估测试提供有希望的补充。 我们提出的研究可能会通过以下方式显着改善青光眼的预后和治疗： 有效利用分析和可视化来改善青光眼管理并提供更多信息 治疗方案。

项目成果

Artificial Intelligence and Glaucoma: Illuminating the Black Box.

人工智能和青光眼：照亮黑匣子。

• DOI: 10.1016/j.ogla.2020.04.008
• 发表时间: 2020
• 期刊: Ophthalmology. Glaucoma
• 作者: Yousefi,Siamak;Pasquale,LouisR;Boland,MichaelV
• 通讯作者: Boland,MichaelV

An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging.

• DOI: 10.1016/j.xops.2023.100389
• 发表时间: 2024-03
• 期刊: OPHTHALMOLOGY SCIENCE
• 作者: Yousefi, Siamak;Huang, Xiaoqin;Poursoroush, Asma;Majoor, Julek;Lemij, Hans;Vermeer, Koen;Elze, Tobias;Wang, Mengyu;Nouri-Mahdavi, Kouros;Mohammadzadeh, Vahid;Brusini, Paolo;Johnson, Chris
• 通讯作者: Johnson, Chris

ChatGPT Assisting Diagnosis of Neuro-ophthalmology Diseases Based on Case Reports.

ChatGPT 基于病例报告辅助诊断神经眼科疾病。

• DOI: 10.1101/2023.09.13.23295508
• 发表时间: 2023
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Madadi,Yeganeh;Delsoz,Mohammad;Lao,PriscillaA;Fong,JosephW;Hollingsworth,TJ;Kahook,MalikY;Yousefi,Siamak
• 通讯作者: Yousefi,Siamak

Estimating the Severity of Visual Field Damage From Retinal Nerve Fiber Layer Thickness Measurements With Artificial Intelligence.

• DOI: 10.1167/tvst.10.9.16
• 发表时间: 2021-08-02
• 期刊: Translational vision science & technology
• 影响因子: 3
• 作者: Huang X;Sun J;Majoor J;Vermeer KA;Lemij H;Elze T;Wang M;Boland MV;Pasquale LR;Mohammadzadeh V;Nouri-Mahdavi K;Johnson C;Yousefi S
• 通讯作者: Yousefi S

The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports.

• DOI: 10.1007/s40123-023-00805-x
• 发表时间: 2023-12
• 期刊: OPHTHALMOLOGY AND THERAPY
• 影响因子: 3.3
• 作者: Delsoz, Mohammad;Raja, Hina;Madadi, Yeganeh;Tang, Anthony A.;Wirostko, Barbara M.;Kahook, Malik Y.;Yousefi, Siamak
• 通讯作者: Yousefi, Siamak