I-Corps: A novel diagnostic method of detecting eye diseases using a smartphone

I-Corps项目的更广泛影响/商业潜力在于为潜在客户和合作伙伴开发基于智能手机的眼病检测技术。该技术的潜在商业客户包括：1）眼科医生和眼科诊所，2）医疗中心，3）生活在医疗服务有限的农村或郊区的客户，4）老年人生活和退休社区，5）健康保险公司，以及6）眼科仪器制造商。眼科疾病通常在诊所中使用眼科设备进行检测，例如光学相干断层扫描、角膜地形图和裂隙灯，这些设备体积大、价格昂贵且不便携，并且需要由训练有素的技术人员操作。然而，我们提出的基于智能手机的眼病检测方法体积小，价格实惠，便携式，并且可以由患者以方便的方式操作，这将克服上述局限性。该算法能够及时准确地检测眼部疾病或监测眼部健康状况，并与眼科医生共享监测信息。因此，我们的技术可以在早期阶段检测到眼部疾病。 此外，眼科医生可以更专注于严重的治疗或手术。成本降低5到10倍是一个额外的潜在好处。该I-Corps项目将探索使用智能手机的新型眼科疾病检测技术的商业潜力，并使其广泛用于科学发现和医疗应用。该项目进一步开发了一种基于智能手机的眼病检测技术，该技术使用图像处理和机器学习技术更加准确和方便。所提出的基于智能手机的眼部疾病检测技术利用眼睛在不同角度的全景图像或短视频记录。使用自动图像裁剪技术将记录的眼睛图像分成虹膜、透镜、巩膜和角膜组件。然后，使用形状检测和颜色匹配技术来分析每个分割的分量，以分别识别每个分量的形状和检测每个分量的异常。初步研究结果表明，该技术检测圆锥角膜的准确率超过90%，从30名受试者。为了检测不同类型的眼病，并提高眼病检测的准确性，我们将从患者和正常受试者中收集更多的眼部数据，以形成更大的数据库，并将机器学习技术应用于积累的数据库。将这些创新能力带入商业市场将显着提高学术界和工业界的发现产出。该奖项反映了NSF的法定使命，并且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 in the development of smartphone-based eye disease detection technology for potential clients and partners. Potential commercial clients for the technology include 1) ophthalmologists and eye clinics, 2) medical centers, 3) customers living in rural or suburban areas with limited medical services, 4) senior living and retirement communities, 5) health insurance companies, and 6) ophthalmic instrument manufacturers. Eye diseases are usually detected in clinics with ophthalmic devices, e.g. optical coherence tomography, corneal topography and slit lamp, which are large, expensive and not portable, and need to be operated by trained technicians. However, our proposed smartphone-based eye disease detection method is small, affordable, portable, and it can be operated by patients in a convenient way, which will overcome the limitations mentioned above. The proposed algorithm can detect eye diseases or monitor eye healthiness in a proper and timely manner, and it can share the monitoring information with ophthalmologists. Hence, eye diseases can be detected in the earlier stage with our technology. Moreover, ophthalmologist can focus more on severe treatments or surgeries. A 5- to 10-fold lower cost is an added potential benefit. This I-Corps project will explore the commercial potential of a new eye disease detection technology using a smartphone and make it broadly available for scientific discovery and medical applications. The project further develops a smartphone-based eye disease detection technology that is more accurate and convenient using image processing and machine learning techniques. The proposed smartphone-based eye disease detection technology makes use of panoramic images or short video recordings of the eye at different angles. The recorded eye images are divided into iris, lens, sclera, and cornea components using automatic image cropping techniques. Each divided component is then analyzed using shape detection and color matching techniques to recognize the shape and to detect the abnormality of each component, respectively. Preliminary finding shows that the proposed technology detects keratoconus with more than 90% accuracy from 30 subjects. To detect diverse types of eye diseases and to increase the accuracy of eye disease detection, we will collect additional eye data from patients and normal subjects to form a larger database, and apply machine learning techniques to the accumulated database. Bringing these innovative capabilities to the commercial market will significantly improve discovery output in academia and industry.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.