Multimodal Artificial Intelligence to Predict Glaucomatous Progression and Surgical Intervention

项目摘要 这项提案的总体目标是，“多模式人工智能预测青光眼的进展” 和外科手术干预“，是使用多模式人工智能(AI)和深度学习策略来预测 哪些青光眼患者需要青光眼手术，哪些患者可能出现进行性视野丧失 在未来。这项研究旨在利用长期存在的具有良好特征的临床和研究队列 及其有效的决策支持AI基础设施来预测哪些青光眼患者将 进展情况，需要手术治疗。该提案包括以下两个具体目标。AIM 1将使用 基线电子健康记录(EHR)、视神经头(ONH)光学相干断层扫描(OCT) 多模式成像、视野(VF)数据、眼压(IOP)和中央角膜厚度(CCT) 用DL模型预测青光眼手术治疗的可能性。目标2将使用基线EHR、ONH 多模式DL模型中的OCT成像、VF数据、眼压和CCT预测快速青光眼的可能性 视野进展。为了解决这些目标，现有的青光眼患者数据1)登记在 国家眼科研究所资助的青光眼诊断创新研究(DIGS，1995年至今)和非洲 下降和青光眼评估研究(2009-2021年)，以及2)在加州大学圣迭戈分校维特比家族管理 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将构建一个专门针对青光眼的平台，用于培训、测试和未来 更新开发的深度学习模型。将来，这个基础设施可以用来支持 人工智能引导的青光眼治疗的随机临床试验测试，并实现实时决策支持 临床医生。

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.