Nikon Retinal Imaging – Solution Profile

• Will Thoreson-Green (Student ID: 12148843)
• Curt Ginder (Student ID:440345)
• Holly Tu (Student ID: 12137544)
• Tom Kozlowski (Student ID: 10452411)
• Ram Nayak (Student ID: 12131499)

We pledge our honor that we have not violated the Honor Code during this assignment.

1. Outline of the Problem

Diabetic retinopathy is a disease due to diabetes that causes damage to the retina. It is the leading cause of blindness among Americans ages 20-64, affecting 40-50% of people with diabetes and accounting for 12% of all new cases of blindness in the United States. Diabetic retinopathy often does not have early warning signs, but the disease can be detected early on by the presence of microaneurysms in the eye. If new cases are detected early, at least 90% of cases could be reduced by proper monitoring and treatment. Diabetic macular edema is a complication of diabetic retinopathy due to fluid buildup, affecting 10% of people with diabetes. Early detection of these two diseases could lead to prevention of significant vision loss and blindness.

2. Nature of the Solution

Nikon and Google’s Verily Life Sciences have partnered to develop a machine learning-enabled retinal imaging solution that will allow for earlier detection of diabetic retinopathy and diabetic macular edema. The underlying technology is Nikon’s ultra-widefield high resolution digital images that capture approximately 82% of the retina. Verily is working on developing a machine learning algorithm that can then read these images and detect early signs of retinopathy in patients with diabetes. They are most likely using the recently acquired data on over one million eye scans from the National Health Service (NHS) to help build this algorithm.1 Interestingly, Kaggle had launched a competition back in 2015 with this exact objective, and the winning algorithm had a 10% higher “agreement rate” than the human-only approach (indicating that the algorithm and a single human expert agreed on a diagnosis more often than two human experts did).2

3. Evaluation of Effectiveness

According to a peer-reviewed article, the Verily system performed at a high level of sensitivity and specificity (97.5% and 93.4%, respectively) compared to the “gold-standard,” as determined by majority decision of a panel of expert ophthalmologists.4 These results were duplicated in an entirely separate dataset. While the results of the technology are promising, the chance of competition replicating the technology is great given the publicly available dataset of retina images that can be used to train the algorithms. Additionally, there are some outstanding concerns regarding the generalizability of the technology to images outside of the datasets used for training and validation. Further testing in actual clinical settings is a necessary step prior to a mass scale implementation.

4. Proposed Alterations

The addition of additional physiologic and early pathologic variables might improve the accuracy of the solution. For example, laboratory values of other end organ damage (microalbinuria, creatinine kinase, HbA1c) could augment the visual findings from the retina imaging.

Additionally, the visual ophthalmologic findings could be applied to early detection of other diseases, such as hypertension, that are not typically evaluated through retinal imaging. Augmented with a medical professional’s judgement of other risk factors, this combined approach might improve targeted preventative treatment for a variety of diseases that are otherwise difficult to predict.

The application of the algorithm in an appropriate clinical context could ensure an improvement in the detection and treatment of diabetic retinopathy, especially in resource constrained settings. Currently, the American Diabetes Association recommends patients with diabetes have an annual examination by an ophthalmologist.5 For areas without access to ophthalmologists, this solution could improve the diagnostic capabilities of a mid-level or general practice provider that lacks specialized training.

Sources:

1. Hodsden, Suzanne. 2017. “Nikon, Verily Partnership Combines Machine-Learning With Advanced Retinal Imaging.” Med Device Online. https://www.meddeviceonline.com/doc/nikon-verily-partnership-combines-machine-learning-with-advanced-retinal-imaging-0001
2. Farr, Christina. 2015. “This Robo Eye Doctor May Help Patients With Diabetes Keep Sight.” KGED Science. https://ww2.kqed.org/futureofyou/2015/08/20/this-robo-eye-doctor-may-help-patients-with-diabetes-keep-sight/
3. All figures taken from https://en.wikipedia.org/wiki/Diabetic_retinopathy (all scholarly articles cited in wikipedia) and https://news.fastcompany.com/verily-and-nikon-will-develop-machine-learning-tools-to-screen-for-vision-loss-4027884
4. Varun Gulshan, PhD; Lily Peng, MD, PhD; Marc Coram, PhD1; et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photograph. Journal of the American Medical Association. http://jamanetwork.com/journals/jama/article-abstract/2588762
5. David K McCulloch, MD, et al. Diabetic retinopathy: Screening. UpToDate. https://www.uptodate.com/contents/diabetic-retinopathy-screening