Machine Learning and OCT Images — the Future of Ophthalmology
“Optical coherence tomography (OCT) has become the most commonly used imaging modality in ophthalmology, with 4.39 million, 4.93 million, and 5.35 million OCTs performed in 2012, 2013, and 2014, respectively, in the U.S. Medicare population.” — Cms.gouv
1) AI for the diagnosis of AMD
Age-Related Macular Degeneration (AMD) is a common eye condition and a leading cause of vision loss among people age 50 and older. It causes damage to the macula, a part of the retina, causing loss of central vision. It affects all “straight-ahead” activities, such as reading, driving, seeing faces, etc. Imagine looking at a clock with hands, patients with AMD might see the clock’s numbers but not the hands (Figure 1).
There are two major types of AMD: dry and wet. Dry AMD accounts for 80% of patients. It’s the less urgent of the two as it is slowly progressive. On the other hand, Wet AMD is much more serious. It has a much faster progression and can lead to vision loss if treatment is not promptly initiated. The underlying disease in Wet AMD is the formation of abnormal blood vessels on the retina. These vessels can leak blood and cause inflammation. This will cause scarring of the macula (retina) and subsequent vision loss.
Fortunately, we’ve found treatments that work quite well in Wet AMD. These are intravitreal injections — injections inside the posterior chamber of the eye — of agents that stop the growth and formation of bad blood vessels. They are also commonly called anti-VEGF Injections (anti-Vascular Endothelial Growth Factor).
Correct classification of the type of AMD is therefore crucial. We want to initiate injections promptly in those with Wet AMD.
This problem turns out to be a great fit for computer vision and machine learning. Researchers from University of Washington trained a modified version of the VGG16 convolutional neural network (CNN) using 2.6 million OCT images of 43,328 macular OCT scans from 9285 patients. Their CNN is able to correctly distinguish AMD from normal OCT images with an accuracy of 93%. Peak sensitivity and specificity with optimal cutoffs were 92.64% and 93.69%.
Read more here: Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images
2) AI & Anti-VEGF Injections in AMD
After correctly identifying AMD in the retina, the physicians were challenged with another question: when is the best time to give anti-VEGF injections? OCT scans again play a major role in this treatment decision.
Ideally, we want to use the least amount of injections while maintaining no active blood vessel leakage in the retina. It turns out machine learning can also be helpful in addressing this problem.
In this study, the authors used 183,402 retinal OCT B-scans to train a GoogLeNet inception deep convolutional neural network to solve the binary classification problem of whether a patient needs an injection or not. Two groups were created: 1) an “injection group” containing OCT images that have a following intravitreal injection during the first 21 days after image acquisition. 2) the rest without an injection is labelled as “no injection”. After training, the CNN had a prediction accuracy of 95.5%. For single retinal B-scans in the validation dataset, a sensitivity of 90.1% and a specificity of 96.2% were achieved.
Read more here: OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications
AI applications in ophthalmology is still in its infancy.
The above shows applications of automated image classification using historical clinical data. Thus, the algorithms are learning from historical physician decisions, including both the good and less good ones. However, we can imagine that much more can be done using this technology in the future. For instance, we can feed the algorithm with outcome data — such as improvement or deterioration of visual acuity — so that it can make even more intelligence predictions to inform physicians of potential treatment outcomes.
While the accuracy of these models are incredibly impressive, we need to remain prudent and sober when considering how to deploy these systems to the real world, especially as it applies to critical applications in healthcare. Today’s limitations of machine learning include a poor understanding of what the model is actually doing. As far as we know, the classifier output is not the result of a reasoning that leads to a decision, but rather a computation based on prior clinical treatment data. It can provide valuable information to clinicians, acting like a second opinion to trained physicians. However, care should be taken to avoid acting on results without final human approval.
Read more: Deep Learning in Ophthalmology — How DeepMind Did It
