I-Corps: Machine Learning-Based Diagnosis of Retinal Images with the Aim of Vision Loss Prevention

I-Corps：基于机器学习的视网膜图像诊断，旨在预防视力丧失

• 批准号: 2053424
• 负责人: Amir Tofighi Zavareh
• 金额: $ 5万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2053424&HistoricalAwards=false
• 关键词: Corps; Machine; Learning; Based; Diagnosis

The broader impact/commercial potential of this I-Corps project is to end preventable blindness by increasing access to early diagnostic testing. Utilizing a novel imaging technology and a proven machine learning algorithm, the device allows for point of care diagnosis in a primary care setting at less than half the current cost. By empowering frontline physicians to provide vision-saving eye exams without the need of an eye care specialist, the proposed technology may fundamentally change the current retinal exam landscape, resulting in more efficient and accessible diagnostic testing. This I-Corps Project will yield a portable Artificial Intelligence (AI)-based retinal imaging system consisting of two major components: (1) a hand held ophthalmic device to capture images of the patient's retina, and (2) a machine learning algorithm to classify images of the retina. The hardware solution provides high-resolution images of the retina. The infrared lighting is invisible to the human eye and eliminates the need for pupil dilation as is widely used in the state-of-art fundus imaging currently conducted by medical personnel. The current trained convolutional neural network yields an accuracy of 97% which is on par with the diagnostic accuracy of trained ophthalmologists.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该 I-Corps 项目更广泛的影响/商业潜力是通过增加早期诊断测试的机会来结束可预防的失明。该设备利用新颖的成像技术和经过验证的机器学习算法，可以在初级保健环境中进行即时诊断，成本不到当前成本的一半。通过使一线医生能够在不需要眼科护理专家的情况下提供保护视力的眼科检查，所提出的技术可能会从根本上改变当前的视网膜检查格局，从而实现更高效、更方便的诊断测试。该 I-Corps 项目将产生一种基于人工智能 (AI) 的便携式视网膜成像系统，由两个主要组件组成：(1) 用于捕获患者视网膜图像的手持式眼科设备，以及 (2) 用于对视网膜图像进行分类的机器学习算法。该硬件解决方案提供高分辨率的视网膜图像。红外照明对人眼来说是不可见的，并且消除了瞳孔扩张的需要，而瞳孔扩张在目前由医务人员进行的最先进的眼底成像中广泛使用。目前训练有素的卷积神经网络的准确度为 97%，与训练有素的眼科医生的诊断准确度相当。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。