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

基本信息

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
项目类别：
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

项目摘要

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该提案包括以下两个具体目标 1 将使用的。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) 预测青光眼手术干预可能性的 DL 模型将使用基线 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为了实现这些目标，青光眼患者的现有数据 1) 参与了研究。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 管理眼科也会用到AI模型的开发和测试。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，未来可以用这个基础设施来支持更新开发的深度学习模型。 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信徒。