Multimodal Artificial Intelligence to Predict Glaucomatous Progression and Surgical Intervention

多模态人工智能预测青光眼进展和手术干预

• 批准号: 10504041
• 负责人: Sally Liu Baxter
• 金额: $ 41.04万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 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 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10504041
• 关键词: Address; African American population; African ancestry; Age; Algorithms; Artificial Intelligence; Blindness; California; Caucasians; Clinical; Clinical Research; Communities; Complement; Cornea; Data; Data Set; Development; Diagnosis; Diagnostic; Disease; Disease Progression; Electronic Health Record; Enrollment; Evaluation Studies; Eye; Family; Funding; Future; Glaucoma; Healthcare; Hispanic; Image; Individual; Infrastructure; Knowledge; Latino Population; Minority; Minority Groups; Modeling; Monitor; National Eye Institute; Operative Surgical Procedures; Ophthalmology; Optic Disk; Optical Coherence Tomography; Outcome; Patients; Physiologic Intraocular Pressure; Population; Randomized Clinical Trials; Research; Retina; Risk; Science; Secure; Technology; Testing; Thick; Time; Training; Universities; Update; Vision research; Visual Fields; artificial intelligence algorithm; base; blind; clinical care; clinical decision support; clinical practice; cloud based; cohort; comparison intervention; data curation; data sharing; deep learning; deep learning algorithm; deep learning model; design; diverse data; follow-up; glaucoma surgery; high risk; improved; individualized medicine; innovation; learning strategy; model development; multimodality; personalized management; precision medicine; research study

Project Summary The overall objective of this proposal, “Multimodal Artificial Intelligence to Predict Glaucomatous Progression and Surgical Intervention”, is to use multimodal artificial intelligence (AI) and deep learning strategies to predict which glaucoma patients will need glaucoma surgery and which are likely to have progressive visual field loss in the future. This study is designed to leverage longstanding well characterized clinical and research cohorts of glaucoma patients and its validated decision support AI infrastructure to predict which glaucoma patients will progress and which will need surgery. The proposal includes the following two Specific Aims. Aim 1 will use baseline electronic health records (EHR), optic nerve head (ONH) optical coherence tomography (OCT) imaging, visual field (VF) data, intraocular pressure (IOP) and central corneal thickness (CCT) in a multimodal DL model to predict the likelihood of surgical intervention for glaucoma. Aim 2 will use baseline EHR, ONH OCT imaging, VF data, IOP and CCT in a multimodal DL model to predict the likelihood of fast glaucomatous visual field progression. To address these aims, existing data from glaucoma patients 1) enrolled in the National Eye Institute funded Diagnostic Innovations in Glaucoma Study (DIGS 1995-present) and African Descent and Glaucoma Evaluation Study (ADAGES 2009-2021), and 2) managed at the UCSD Viterbi Family Department of Ophthalmology will be used in the AI model development and testing. We will also leverage UCSD's existing cloud-based AI pipeline to build a glaucoma-specific platform to train, test and in the future, update the deep learning models developed. In the future, this infrastructure can be used to support randomized clinical trial testing of AI guided glaucoma management and enable real-time decision support for clinicians.

项目概要 该提案的总体目标是“预测青光眼进展的多模式人工智能” 和手术干预”，是利用多模态人工智能（AI）和深度学习策略来预测 哪些青光眼患者需要进行青光眼手术，哪些患者可能会出现进行性视野丧失 将来。这项研究旨在利用长期特征明确的临床和研究队列 青光眼患者及其经过验证的决策支持 AI 基础设施来预测哪些青光眼患者将 进展以及哪些需要手术。该提案包括以下两个具体目标。目标 1 将使用 基线电子健康记录 (EHR)、视神经乳头 (ONH) 光学相干断层扫描 (OCT) 多模态成像、视野 (VF) 数据、眼压 (IOP) 和中央角膜厚度 (CCT) DL 模型预测青光眼手术干预的可能性。目标 2 将使用基线 EHR、ONH 多模态 DL 模型中的 OCT 成像、VF 数据、IOP 和 CCT，用于预测快速青光眼的可能性 视野进展。为了实现这些目标，青光眼患者的现有数据 1) 纳入了 国家眼科研究所资助青光眼诊断创新研究（DIGS 1995 年至今）和非洲 后裔和青光眼评估研究 (ADAGES 2009-2021)，以及 2) 由 UCSD Viterbi Family 管理 眼科将用于AI模型的开发和测试。我们还将利用 加州大学圣地亚哥分校现有的基于云的人工智能管道，用于构建青光眼专用平台来进行训练、测试，并在未来， 更新开发的深度学习模型。未来，该基础设施可用于支持 AI 指导青光眼管理的随机临床试验测试，并为以下患者提供实时决策支持 临床医生。