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.

检测因青光眼引起的功能和结构损失对于以目标做出治疗决策至关重要 维护视力并维持生活质量。但是，大多数青光眼评估方法 通过视野（VFS）或光学相干断层扫描（OCT）测量有几个局限性 对他们的临床实用性构成关键挑战。 如果患者识别一系列VF或OCT数据，则识别青光眼诱导的变化是有挑战性的 处于疾病的早期阶段，具有微妙的结构和功能迹象，或者患者是 在疾病的后期，具有显着的VF变异性和OCT地板效应。一个主要限制 当前的青光眼监测技术是它们产生了青光眼是否为 当前的高通量数据（例如OCT）比二元期结果更具恶化。 其中一些方法的另一个主要缺点是它们依靠传统范式进行进步 检测如线性回归。但是，青光眼进展的速率可能是非线性和快速的， 特别是在疾病后期。另一个限制是采用临时规则来定义 青光眼进展虽然需要客观标准来定义进展的阈值。最后，一个少校 大多数这些方法的缺乏是它们缺乏先进的可视化和解释。 我们建议通过开发人工智能（AI）启用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 监视的定性和定量手段1）全局视觉功能和结构恶化，2） 半场的损失和3）高级二维可视化工具上功能和结构损失的局部模式。到 实现这些目标，我们组建了一个跨学科专家团队 注释的青光眼数据。 该提案的中心假设是，应用于完整的高级可解释的机器学习 VF在所有测试位置（例如，24-2 in 24 in System）和视网膜神经的测量值的轮廓 光纤层（RNFL）（例如，光盘周围的768个A扫描和7个全球部门区域）可以客观地和 自动学习和量化最重要的功能，从而产生更具体和更敏感的手段 与当前主观指定或统计鉴定的方法相比，对青光眼恶化的监测恶化。 我们还假设机器学习可以提供几层青光眼的可解释模型 可能为当前青光眼评估测试提供有希望的补充的知识。 我们提出的研究可能通过通过 有效利用分析和可视化来改善青光眼管理并使知情 治疗选择。

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