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

I-Corps项目更广泛的影响/商业潜力是通过增加获得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护理点诊断，成本不到目前的一半。通过授权一线医生在不需要眼科保健专家的情况下提供拯救视力的眼科检查，拟议的技术可能从根本上改变目前的视网膜检查格局，从而产生更有效和更容易获得的诊断测试。这个I-Corps项目将产生一个基于人工智能（AI）的便携式视网膜成像系统，该系统由两个主要部分组成：(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。硬件解决方案提供视网膜的高分辨率图像。红外线照明对人眼是不可见的，不需要瞳孔扩张，这是目前医疗人员进行的最先进的眼底成像中广泛使用的。目前训练的卷积神经网络的准确率为97%，与训练有素的眼科医生的诊断准确率相当。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.