Retinal Imaging for Alzheimer’s Disease
The retina is actually part of the central nervous system, originating as an outgrowth of the developing brain and shares many similarities with the brain. The retina is easily accessible for direct and noninvasive imaging with high spatial resolution and sensitivity. Today we hear from one company that is focusing on that for Alzheimers disease. The presentation does contain some technical detail but provides a look at new possibilities for diagnosis and risk assessment.
Steve Verdooner serves as the CEO of NeuroVision Imaging, a company developing retinal and fluid biomarkers for neurodegenerative disease. Steve was co-founder and CEO of Ophthalmic Imaging Systems (1984-2007). OIS was a company dedicated to research, development, marketing, selling and manufacturing diagnostic medical imaging systems and informatics software for the eye care market.
#longevity #wellness ##retina #retinalimaging #RobertLufkinMD #rsteveverdooner #alzheimers
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Robert Lufkin 0:00
Welcome back to the health longevity Secret show and I’m your host, Dr. Robert Lufkin. The retina is actually part of the central nervous system, originating as an outgrowth of the developing brain and it shares many similarities with the brain. The retina is also easily accessible for direct and non invasive imaging with very high spatial resolution and sensitivity. Today we hear from one company that is focusing on that possibility for Alzheimer’s disease. The presentation does contain some technical detail but provides a look at new possibilities for diagnosis and risk assessment. Steve for dooner serves as the CEO of neuro vision imaging a company developing retinal and fluid biomarkers for neurodegenerative disease. Steve was co founder and CEO of a Palmach imaging systems. Oh is was a company dedicated to research development, marketing, selling manufacturing, diagnostic medical image, Ching systems and informatics software for the eye care market. And now, Steve for dooner
Steve Verdooner 1:11
Hello, my name is Steven for dinner and I’m the CEO and co founder of neuro vision imaging. Today I’m going to speak to you about retinal imaging for Alzheimers disease and some of the recent breakthroughs that we’ve made. First, I’d like to tell you just a little bit of background about neuro vision neuro vision is a company that is developing diagnostic solutions for Alzheimer’s disease, both in the area of retinal imaging, and also in the area of blood biomarkers. Today’s talk is going to be about retinal imaging. At the end of the presentation, I am going to tell you a little bit about the work that we’re doing in blood biomarkers, and how we’re combining biomarkers in order to improve predictive power. Before we jump into the current technology, I’d like to tell you a little bit about the foundational work that was done at Cedars Sinai Medical Center quite some years ago, but has proven to be extremely foundational and important in the work that we’re doing today. This work came out of the coronial lab at Cedars where it first started in transgenic mice, where they showed in two and a half month and five month old transgenic mice that in fact, plaque were present in transgenic mice in the retina using curcumin as a fluorophore. And this was shown very exquisitely and in fact, you can see both in brain and retina and in fact, in the younger transgenic mice, the plaque first appeared in the retina before it appeared in the hippocampus and the cortex. Moving on from that work, they also obtained some tissue from Dr. Carol Miller at USC, from very well characterized Alzheimer’s patients MCI and also some normal controls. And they looked at brain tissue, and they looked at retinal tissue. And in fact, they saw that when they looked at retinal tissue, using curcumin and also 12, f4 and other antibodies that they were able to see increasing numbers of plaque in the retina with increasing disease, as you see illustrated on the slide. So in the far right hand bottom corner, you see that in a definitive ad patient, late stage, severe late stage, you see a lot of plaque in MCI you see less, but of course, we also in cognitively normal, we also see plaque accumulation, just as we see in the brain. Again, this was the earliest evidence of amyloid beta plaque in the retina. Also to further prove out the concept and understand how the retina might behave compared to the brain. They actually had an experimental immunotherapy, whereby they treated the transgenic mice and in fact saw that they could observe plaque clearance, as you see on the far right hand side of the slide, with the black circles, plaques that were previously present actually, in fact, cleared. And in fact, they had some new plaque formation as well. And it’s been hypothesized that this is a dynamic
process in the human brain and somewhat demonstrated here in transgenic mice, seeing this dynamic process play out in the retina as well also additional foundational work where they had again, well characterized brains and retinas. And they were able to see not only the plaque, as you saw on the previous slides, but the actual structure of the plaque having very punctate centers. And so this is from the same patient retina on the left hand side of the slide brain on the right hand side of the slide, where you see the structure of the plaque actually is quite similar. One observation is that the plaques are smaller in the retina, but they still retain a very similar structure to what we see in the brain. Here you see other illustrative examples on the left hand side of the slide with the same patient illustrating plaque in retina and corresponding in brain, we see it also Peri vascular, and you see the bottom slide, bottom half of the slide, where you see deposition actually along blood vessels. And again, we also know that there’s a very high incidence of cerebral amyloid angiopathy in Alzheimer’s patients, which could have implications for deposition along blood vessels. On the right hand side of the slide was a study that we did with Wills Eye Center, where in fact, we had patients that had ocular melanoma, these patients were imaged before their eye was extracted, and then after we looked at the tissue, and in fact, you can see samples where there’s actually a deposition long vessels in the in vivo on the left and actually in the ex vivo on the right that corresponds to those fluorescent spots, the quality is not quite as good because these are patients who had undergone radiation treatment for ocular melanoma, there’s a lot of compromise in the retina in the lens and other aspects of the eye. But nonetheless, we did see correlation there as well. There’s a very nice comprehensive review, in active neuro pathological JCI insight and others where you can get more information on the foundational aspects of this technology. Let me tell you now a little bit about the technology development, we’ve come a very long way since the coronial
foundational work at Cedars Sinai Medical Center, we actually have a device that you see on the top left hand side of the slide that’s 510 K cleared and CE marked. This device is taking all of the aggregation of work that was done, as well as experimental work by neuro vision to optimize the camera to be able to image amyloid beta plaque in vivo in humans, the device has automated alignment device has automated focus. And the basic idea is that a series of images are captured, they’re uploaded securely to the cloud. They’re analyzed, and then they’re reported back. As you can see, there’s a product called Athena, Athena is software as a medical device. So afina does the automated assessment, the segmentation, the image processing, in order to characterize and quantitate the autofluorescence spots that we are seeing in the retina. In the bottom half, you see the original slide that was captured. And then after aggregation of a number of images together, and image processing, you can see these punctate autofluorescence spots. These are quite different than the autofluorescence spots that you see in age related macular degeneration, where you might have drusen that also exhibit autofluorescence characteristics that are large and puffier and irregularly shaped the use case that we see for this, and the application is actually as a screen before someone gets a PET scan. And the way that the system is being tuned is to have high negative predictive value greater than 90%. For the target population with basically subjective memory complaints or mild cognitive impairment. The first use case is really to be used as a screen to pet which we know PET scans are expensive, radioactive. And the accessibility worldwide of PET scans is very, very challenging. Alternatives to that are cerebral spinal fluid, but lumbar punctures also very invasive. There’s also the opportunity to screen prior to clinical trials. This is to be used first as a
screen to pet aspirationally. With combining with blood biomarkers. If our area under the curve is good enough with blood together with retina which I’ll show at the end of the presentation, then we have an opportunity to potentially replace pet and we are doing some human cadaver studies to support that but that’s well down the road first objective is to screen to pet our commercial target is assessment amyloid status relative to amyloid pet with a high negative predictive value of greater than 90%. essentially eliminating true negatives from further pet and CSF tests. The target population is 60 years older cognitively impaired, and with a 20 to 25% incidence of amyloid beta positivity upon PET scan. The operating point is 75% positive agreement is 65% Negative agreement, which brings us to that NPV and then a positive result requires a follow up PET scan, or CSF. So the basic idea is to have a prediction model to predict amyloid pet status positive or negative. This was all developed originally on a cognitively normal elderly population, one of the most challenging populations, although I’m not able to share specific results because they’re in the process of being submitted for publication. I can at least anecdotally share with you what that is looking like in various studies. So again, we developed a model with self validation and then independent data sets that we’ve done for internal verification and validation based on pet standards and based on CSF standards. And we’re currently in the range of point seven, five 2.8 area under the curve. And again, that’s an A cognitively normal, elderly population. We expect as we gain more data in MCI for that to improve cognitively normal detection of amyloid pet status is the most challenging thing that we could try to do. We’re pleased with the results that we’re getting in the area under the curve given the challenging cohort that that is and also the model performed consistently with overlapping 95% confidence intervals. A little bit about the regulatory status Radia is currently cleared back in 2018. As a retinal imaging device under the NYC code. afina is actually software as a medical device cleared also in 2018. And again, this is an fully automated software tool chain that’s utilized to process images and quantitate autofluorescence spots. Our final regulatory clearance is subject to validation trials, which are currently underway in nine sites in the United States and the UK ultimately We envision based on meetings with the FDA, a de novo regulatory path that’s been confirmed indication for use, which we have also done additional vetting with FDA with respect to how the product would be used. And as you can see highlighted here and the de novo indication for use as an aid in assessing the presence of amyloid in the central nervous system and adults over 60 years, etc, etc. The report that’s currently produced is to quantitate autofluorescence spots in the retina. And ultimately, in this last regulatory clearance, we will be tying that to an amyloid pet predictive value positive or negative. from a regulatory perspective, we have our ISO 1345 certification. And now a little bit more about the technology. When the images are imported into the afina. Cloud. They go through an importing process. And then here you see the image processing steps that take place at least at a high level, the affino 1.0 has been FDA 510 K clear. So now let me tell you a little bit about the afina product architecture. Again, afina is software as a medical device that’s used to automatically quantitate autofluorescence spots in the retina. First we have a folder, interface module and data organization. And once the images are imported, there’s an automated image quality assessment that looks at things like contrast with respect to how cataractous a patient might be and eliminate those if they’re blinks, if they’re blurs, then goes through a series of image processing steps and combining steps to optimize the image sets rank stack the image quality, we also look at the fovea and optic nerve head and have fully
automated means by which we detect fovea and optic nerve head so that the region of interest can be measured reproducibly. And we create a region of interest mask whereby that is the area where we are doing the automated segmentation. And then we do a number of processes in order to identify these autofluorescence artifacts. Here you see an example of one of the image quality analyses where you see the red X’s on some of these on the bottom row. These are quite obvious where you see blinks and blurs, maybe not so obvious and others. But essentially, we’re taking the best eight images and then combining those together in order to improve the image quality, which you see demonstrated on this slide. So you start with raw images that you see in the middle image. And then you have after corrected normalized you see these punctate white autofluorescence spots, and that is what is utilized then and fed into the engine for segmentation. Also, as part of Athena, we have vascular segmentation software. And this allows us to look at perivascular amyloid, again, amyloid that accumulates along the vessels, it also gives us the opportunity to look at other features in the vasculature that might be implicated in Alzheimer’s disease. I now like to speak with you about the concept of combining rental imaging with blood biomarkers, we have a partnership with a company called mag array, nervous and has exclusive rights to the magnetic platform in the field of neurology. What’s special about the Magnetic Platform is that it’s very simple. It’s ultra high sensitivity. And it has the ability to multiplex multiple biomarkers in effect to create a bio signature, instead of just looking at amyloid beta 40 and 42. We’re currently looking at other biomarkers total tau, three flavors of p tau GFAP, neurofilament, light and others. And from the beginning, since the inception of the company, we felt that it was a combination biomarker approach that would be most useful for Alzheimer’s disease, given the complexity of the disease. So the concept is to take what we learned in blood biomarkers and combine the dataset with what we are producing in retina so as to improve the predictive power. Here you see some of the targets that we’re currently looking at in blood biomarkers, and it covers amyloid tau pathology synaptic dysfunction, and those that are highlighted in yellow are ones where we already have data available those in green is future work that’s ahead of us. But we currently have antibodies and tools for those. And the idea is to combine this together. Here you see essentially where we have a MonteCarlo simulation, whereby we are combining retina and blood together looking at a number of different possible outcomes. What you’re looking at on the bottom of the slide is when you combine data vectors together, in other words, taking an area under the curve of approximately point seven, eight for afina. For retina combining it with blood biomarker of also about point seven, eight, you get a hypothetical combination, that is five to 10 points higher, and as you see on the left the combination increase the AUC by point o seven, on average, as long as these datasets are orthogonal to each other there, that opportunity exists. Now again, we do have some work that is in progress here that I can’t share results with you on but it’s looking very promising at least an initial datasets of combining blood and retina together to improve predictive power looks very, very promising. Finally, we’re obtaining very encouraging results in our retinal imaging business with respect to detection of amyloid status as we compare to PET scan. Unfortunately, due to COVID There have been delays in our clinical studies who For those who are starting back up again. Also, as I’ve mentioned, we’ve made great progress in our blood biomarker initiative. And it’s our goal to combine these together ultimately over time to improve overall predictive power. I’d like to thank the organizers again for the opportunity to speak here today. Thank you.
Unknown Speaker 15:16
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