Data-driven creative for sensitive verticals

With big privacy changes, creative has become even more important with verticals like health and wellness and finance. Learn how to make data the backbone of your campaigns.

C. Light Technologies has raised an investment from the Alzheimer’s Drug Discovery Foundation (ADDF) for its AI technology that spots changes in eye motion to detect the earliest stage of Alzheimer’s disease.

The company received this investment via ADDF’s Diagnostics Accelerator, a collaborative research initiative supported by Leonard A. Lauder, Bill Gates, the Dolby family, the Charles and Helen Schwab Foundation, Jeff Bezos, and MacKenzie Scott, among others. C. Light recently completed its second and final seed round, raising $500,000, including the ADDF investment, which brings its total seed funding to more than $3 million.

Research efforts

This new investment will fund a novel pilot study with the University of California at San Francisco Memory and Aging Center to look at changes in fixational eye motion during the earliest stage of Alzheimer’s disease, generally known as mild cognitive impairment.

The ADDF’s Diagnostics Accelerator has made previous investments in more than two dozen world-class research programs to explore blood, ocular, and genetic biomarkers, as well as technology-based biomarkers to identify the early, subtle changes that happen in people with Alzheimer’s.

The goal is to try to get something into the market by 2023.

As new therapeutics for Alzheimer’s are introduced to the clinic, this UCSF technology has the potential to provide clinicians with a better method to measure disease progression, and ultimately therapeutic efficacy, using C. Light’s novel retinal motion technology.

The current lack of objective tools to assess neurological progression and disability highly impacts pharmaceutical developers. Therapeutic development for the central nervous system is extremely difficult, with success rates of only 8% going from conception to market. Clinical trials can take a decade to come to fruition in this space, due to inefficient methodologies for assessing the efficacy of drugs. This is where C. Light could make the biggest impact.

C. Light cofounder Christy Sheehy said in an interview with VentureBeat that the company hopes to get a diagnosis machine into the market.

Sheehy started the company after doing a lot of research for her doctorate at the University of California at Berkeley.

“I had really started to think of the power of using AI motion, and retinal imaging, as biomarkers. And so I have family members that have, unfortunately died from neurodegenerative disease. And so for me, this is a personal passion area as well to be able to make sure that for other families, I can extend longevity and quality of life for loved ones,” Sheehy said.

While C. Light is looking for automating the diagnosis, current methods are a lot more labor intensive, as doctors and other specialists have to evaluate patients with manual testing. Sheehy hopes to build a $15,000 machine that can be used at the offices of the specialists.

Before this year it had been almost two decades since an Alzheimer’s drug was brought to market. Part of the reason for the slow progress is that drug developers haven’t had viable biomarkers they can use to effectively stratify patients and track disease on a fine scale. The ADDF’s investment will enable C. Light to tackle that.

Eye motion diagnostics

Eye motion has been used for decades to quickly triage brain health, which is why a doctor asks you to “follow my finger” with your eyes. In more than 30 years of research, studies have revealed that patients with Alzheimer’s exhibit eye movements that are affected by the disease, though these movements have only been measured on a larger scale to date.

Sheehy said that AI’s advances are just now coming into the technology for diagnosing Alzheimer’s. There are other companies using AI for retinal diagnosis for diabetes, but it is new in the Alzheimer’s and neuro degenerative space.

In the past, C. Light has done studies on multiple sclerosis and concussion scanning via retina analysis. But the new study will focus on mild cognitive impairment as a possible early sign of Alzheimer’s. Dementia affects a larger body of people, but C. Light’s focus for now is on detecting Alzheimer’s, which is often fatal.

“Using eye motion is a super powerful tool. So it involves many areas of the brain,” said Sheehy. “A lot of different areas of the brain are interrelated while we move our eyes. So where you’re doing eye motion tracking, and you have an individual execute a motor task or be able to memorize or look at how to draw a clock — all of those can be potentially used as, as biomarkers. And so one of the things that we’re trying to tackle with our device is look for small movements down to about a micron in size.”

C. Light’s research takes the eye movement tests to the microscopic level for quicker and earlier assessments. The company says its fast, non-invasive, and objective technology enables clinicians to study and measure eye motion on the cellular scale — meaning eye motion as small as 1/100th the size of a human hair — helping physicians better monitor a patient’s disease and treat it more effectively.

C. Light said its tests are also easy for clinicians to administer. Patients put their chin in a chinrest and then focus on a target for 10 seconds. The test does not require eye dilation, and patients are permitted to blink during the test. A very low-level laser light is shown through the pupil and reflects off the patient’s retina, while a sensitive camera records the cellular-level motion in a high-resolution video. This eye motion is then extracted up to 1000 times per second and fed into C. Light’s advanced analytical platform.

Sheehy said C. Light is creating an entirely new data stream around the status of brain health via the eye. She said the company’s growing databases and accompanying AI can change the way we monitor and treat neurological disease for future generations.

Second-round seed funders included ADDF, the Wisconsin River Business Angels, and Abraham Investments.

Howard Fillit, a doctor and founding executive director at the ADDF, said in a statement that more than 6 million people in the U.S. are affected by Alzheimer’s. Technology can now collect data from patients in many ways, and AI can find patterns to detect early signs of dementia. C. Light’s retinal measurement solution, along with the UCSF study, will help clinicians identify deficits and may allow for early intervention and quicker therapeutic intervention, he said.

The machines capture eye movements for roughly 10 seconds. The tech extracts the data for the movement, which can be up to 1,000 times per second, for about 5,000 total data points. C. Light feeds that into a neural net that allows for the diagnosis of the different visual signatures that would be indicators of disease. The data can be sent to the cloud, where many machines could access it. Over time, the AI would get better.

Data-driven creative for sensitive verticals

With big privacy changes, creative has become even more important with verticals like health and wellness and finance. Learn how to make data the backbone of your campaigns.

Presented by MongoDB

Success in the digital age is predicated on the ability to deliver new  experiences to customers quickly. That’s why companies are rethinking not just the front-end of their company, but every layer beneath as well. They are streamlining their supply chains, optimizing customer feedback loops, maintaining less inventory, and applying metrics and AI/ML to generate operational insight. All in an effort to accelerate.

So when it comes to digital innovation, faster is better. Right? Not so fast.

Forward-thinking business leaders know that the decisions they make today will determine their competitiveness for years or maybe even decades to come. With this relentless push for speed comes the temptation to skip the basics. Cutting corners with security or privacy, locking into proprietary technologies, or accruing massive tech debt have long-term consequences.

Over time, these consequences add up, amounting to an “innovation tax” that must be continually paid in the form of inflexible technology, lost productivity, and slower time to market. And companies that don’t pay attention to this risk losing their best employees — a silent and hard-to-measure tax that nevertheless has killed some of the most innovative products in the market.

But there is a way for business and IT leaders to exercise both fast-twitch and slow-twitch muscles in this race; to think short-term and long-term. At MongoDB, we call it “sustainable speed,” and it starts with ensuring the proper digital foundations are in place. We believe that the cornerstone of your digital foundation is your data — the raw material of innovation in the digital age. In our work with thousands of customers, we’ve identified four pillars of sustainable speed, each of which allows organizations to accelerate innovation without courting long-term disaster.

Multi-cloud agility

Not all clouds are created equal — and neither are data centers. The fact is that each cloud provider can be the “best” cloud provider — albeit to different users, in different situations. While each provider offers a portfolio of services, they aren’t the same in terms of functionality or maturity. Developers should be able to use best-of-breed technologies across clouds — not just for different apps, but for the same application.

Imagine your devs being able to utilize AWS Lambda, Google Cloud’s AI Platform, and Microsoft’s Azure DevOps within a unified console. In addition, despite the energy around cloud, very few of the large companies I speak to are all-in on cloud — some plan to move slowly or even never move fully to the cloud because of regulation, compliance, or even cost at scale. Don’t fall for any kind of mantra about “all-in” on one thing or another — listen to your business units and listen to the developers in them.

Innovation velocity

If applications are the currency of the new economy, then development teams are the market makers. And yet despite the relentless strategic emphasis on speed and innovation in the digital economy, these teams continue to be mismanaged and malnourished inside both large and small companies. To maximize the innovation output of developers, companies must make an effort to understand the fundamental nature of development work, providing the most intuitive and flexible tools on the market, and removing time-consuming, undifferentiated work, like database administration.

Listen to your developers when they talk about wanting to fix the underpinnings of their test, deployment, or monitoring systems. And invest in the daily developer workflows, removing barriers and streamlining the process whenever possible.

Predictability (a.k.a., reliability)

Here is where we start to think about the ability to build quickly, but with confidence. Creating or updating mission-critical applications is always high-stakes work, with inherent risks of losing data or running afoul of regulatory requirements. Executives must feel certain their application development platform will protect the integrity of the customer and business data, handle outages with no significant impact (internally or externally), and scale to meet the ambition of the business.

You build this confidence by regularly asking your leaders and their teams to bring up areas of concern around the layers of what I call “The Onion of Requirements.”

These are:

• Security
• Durability
• Correctness
• Availability
• Scalability
• Operability
• Features
• Performance
• Efficiency

The first six are the ones that, if you get them wrong, can completely trash your business predictability. In most companies, you won’t hear about these things as much as you should; because all executives ever ask about is one layer of the onion: Features. That’s all great until the breach or the outage or the release you have to pull back. Builders build buildings that behave predictably, with thousands of years of best practices behind them. Technology teams should do the same.

Privacy and Compliance

I can’t tell you how many times I’ve heard things like “we innovate quickly, with no compromises on security, compliance, and safety.” That’s really hard to put into practice. We all know that the only way to be absolutely sure you never have an outage caused by a software deployment is to…not deploy software. The research and my own personal experience shows that more than 65% of outage minutes are caused by bad software deployments.

What happens after an outage? Executives sow the fear of consequences among engineers. But this fear can be a debilitating force in the race toward digital innovation. Think of an athlete with blazing speed, holding back because they are terrified of pulling a hamstring or blowing an ACL. This is the effect that cyber-attacks, privacy concerns, and ever-changing regulatory standards can have on the innovation process.

Security is often seen as a counter-weight to innovation. But the opposite can be true. The more secure the data platform, the more testing, the quicker the cycle time between development and production. And the more confidence a team has in moving quickly, identifying problems early and rolling them back before damage is done. To achieve this, security must be baked in, compliance testing must be mandatory, and continuous integration and delivery must be a priority.

Much of this work should be automated, because, whenever I hear that humans are doing security and compliance testing at a company, I want to take my business somewhere else.

A Fortune 500 CTO once said that technical debt should be shown on the balance sheet so the CFO can see it. Why? Because tech debt comes with costly interest payments and the same morale-crushing impact of personal debt

The same could be said for all innovation taxes: the short-sighted, lift-and-shift strategies, the organizational data silos, the vendor lock-in, and the lack of a rock-solid testing infrastructure.

The companies that focus on both innovation and rigor can manage these long-term impediments. They don’t have to choose between the tortoise and the hare. They can be both.

To learn more about this topic, check out The Foundations of Sustainable Speed white paper.

Mark Porter is CTO of MongoDB.

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