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July 16, 2018
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Sight Diagnostics starts selling an AI-based diagnostics device for faster blood tests

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Sight Diagnostics, an Israeli medical devices startup that’s using computer vision and machine learning technology to speed up blood testing, is launching a point-of-care blood diagnostics system today.

It claims the compact, desktop machine — called OLO — which analyzes single-use cartridges manually loaded with drops of the patient’s blood, can deliver “lab-grade” complete blood count (CBC) tests from only a finger prick of blood.

The idea being for clinicians to use the device to perform the most prevalent medical blood diagnostics test directly in their office, rather than a patient having venous blood drawn and sent away to a lab for analysis — a process that can take a few days.

They’re also intending to offer a high tech alternative to carrying out manual microscopy on a blood smear — another technique that can be used to conduct an point-of-care CBC test, but which requires specialist personnel taking the time, care and attention to get it right.

The team hasn’t previously disclosed total funding but are now confirming they’ve raised $25 million in equity financing (Series A and B) from VC firms, including Eric Schmidt’s Innovation Endeavors — which they say they’re expecting to take them through their US clinical trials. They are also in the process of raising a Series C. 

Sight Diagnostics is touting OLO as the high tech alternative that healthcare providers have been waiting for — with AI-powered analysis performing a blood count right then and there, after a healthcare worker has pipetted a few drops of the patient’s blood into place.

Sight Diagnostics points out that CBC tests are used to diagnose a broad range of common medical conditions, as well as for the vast majority of baseline tests ordered during routine ‘well visits’, arguing that speeding up this type of routine blood test could support faster diagnostics of medical problems. Or, indeed, speedier reassurance that a person is okay. 

The OLO system uses a patented process for ‘digitizing’ patient blood into a set of specifically colored microscope images. It then applies proprietary machine vision algorithms to the images to identify and count different blood-cell types — with the company claiming its technology simplifies blood testing so that even non-professionals can perform the tests.

According to the company, new sample-preparation methods allow them to present a small amount of blood to OLO’s microscope in a way that is tolerant to inaccuracies in the preparation process — placing what they describe as “minimal burden” on the user — as well as being robust in the face of inaccuracies in any manufacturing processes, saying this means the cost of their testing kits can remain low.

“This novel way of digitizing blood is equally important to our approach as the artificial intelligence driving the analysis,” they add.

Of course any novel blood testing technology claiming a disruptive advantage must be able to prove it is as accurate and robust as traditional lab testing methods.

Very clearly, lives are at stake.

And, well, on the disruptive startup side, the shadow cast by Theranos’ implosion is a very long one.

But — to be clear — Theranos had claimed it could deliver a full battery of laboratory tests from a few drops of blood — not just a CBC count, which is at least the initial aim for OLO. And for CBC tests having only a small blood sample to work is actually not so unusual.

“CBC tests operate even today with low sample volumes,” it says. “For example, several central-lab instruments have been cleared for capillary samples (200-300uL of blood, of which less than 10uL is actually counted), and the older manual method for CBC analysis — the traditional blood smears on microscope slides — uses less than 10uL of blood in total. This is to say that in our domain the use of low sample-volumes stands on solid scientific ground.”

Sight Diagnostics has been working on the OLO system for more than eight years at this stage.

The co-founder duo — Yossi Pollak and Daniel Levner — combine machine vision and AI expertise on the one hand (Pollak worked on algorithms for automotive machine-vision giant Mobileye), with a medical background, via Levner’s postdoctoral fellowship at Harvard Medical School (and later a CTO role at a biotech company, called Emulate).

Their key claim is that OLO produces “lab-quality” CBCs.

More specifically, they say a recent clinical trial compared its CBC analysis against Sysmex XN (“a top-of-the-line lab-grade analyzer”) to determine equivalence.

Here’s what Levner — who’s also chairman of its scientific advisory board — told us on that:

The study included the 19 CBC parameters that make up the 5-differential (‘5-diff’) CBC, as well as a number of medical/diagnostic ‘flags’. The results were analyzed statistically, including an analysis of the correlation of each parameter between the two instruments, bias (whether there is a systematic shift between the two instruments), and slope (whether there is a systematic scaling factor between the two instruments’ results).

To ascertain what quality of results was necessary to declare OLO equivalent to the Sysmex XN, we relied on values that we discussed with the FDA in our three pre-submission meetings. We applied these quality targets to our recent clinical study despite the CE Mark not sharing the same stringent requirements as the FDA, and we found that we surpassed the targets. Accordingly, we believe that our data supports the claim that OLO is equivalent to standard central-lab tests, which is our goal: testing at the point-of-care without compromising accuracy or depth of information.

As Levner notes there, they have completed a 250-person clinical trial, which took place at Israel’s Shaare Zedek Medical Center — a testing process that led to them obtaining CE Mark registration for OLO; aka the health & safety certification that’s necessary for commercial sale within certain European countries. 

“For the CE Mark declaration, we have verified that OLO complies with the CE in vitro diagnostics directive (Directive 98/79/EC IVD). Accordingly, OLO meets the full list of harmonized standards that the directive requires, including ISO 13485 (quality management system), ISO 14971 (medical device risk management), CEN 13612 (medical device performance evaluation), and various safety, stability and labeling requirements,” he further says on that.

One important point to flag is that Sight Diagnostics has not yet published peer reviewed results of any of its clinical trials for OLO.

But Levner says the results of its most recent clinical trial (testing OLO as a CBC analyzer) are “currently in preparation” for publication in a peer-reviewed journal.

“We strongly believe in the necessity of sharing our data this way, but unfortunately and as you know, the process of publishing in academic journals tends to take several months,” he says, offering to share the results under a confidentiality agreement “so as not to scoop our own publication”.

Nor is OLO the team’s first blood diagnostic test. Previously they developed a diagnostic test for malaria (called Parasight), using digital fluorescent microscopy and computer vision algorithms — and they have three published journal articles that describe clinical trials on their malaria test.

Parasight was first deployed in 2014, and they say more than 600,000 of the malaria tests have been sold to date — claiming they have “accurately and consistently” diagnosed malaria in 25 countries.

Levner says the malaria test used the same underlying technologies they are now redeploying for OLO — including “common sample-preparation methods, microscope design, and artificial-intelligence based algorithms”.

While malaria testing was their first focus, they’re looking to build a far more expansive point-of-care blood diagnostics business with OLO — beginning with CBC testing but envisaging the system as a platform that will, in time, be capable of running a portfolio of blood tests. 

Although on this Levner is careful to note that each additional test would be added individually — and after “independent clinical validation”.

“We see OLO eventually consolidating a number of tests that are important to the doctor’s office and becoming a diagnostics nerve-center for the clinic,” he tells TechCrunch, adding: “We will introduce these additional tests one-by-one, with each test undergoing independent clinical validation.”

Sight Diagnostics is starting by selling OLO in Europe, with both private doctors’ offices and national health services in its sights. Levner says they’re expecting the device to be in doctors’ offices in the EU in “around three months” — noting they’re in the process of finishing up a couple of initial distribution agreements now.

“Ultimately, we intend to distribute OLO in all of Europe and beyond. However, we are prioritizing European countries that are known for being early adopters — for example, countries without a single-payer system or ones with a well-developed private market,” he adds.

He also confirms OLO has been registered in the EU using a Netherlands-based CE Notified Body.

“We are also pursuing several more national registrations that don’t require additional testing, such as Switzerland and Israel, which otherwise accept the CE Mark.”

The team is also conducting a study as part of FDA testing in the US — with a trial ongoing at three US-based sites. They’re aiming to admit more than 500 participants, and are using eight different OLO instruments for testing.

The initial push is to obtain 510(k) approval from the FDA, which would allow OLO to be used in larger US-based clinics (CLIA certified facilities). Levner says they hope to gain that approval “midway through next year”.

The subsequence step would be to obtain a CLIA waiver from the FDA — which would permit it to place instruments in small clinics and doctor’s offices — necessary to the stated goal of “bringing blood diagnostics to the point-of-care”. And the hope is they obtain that waiver in 2020. Though clearly there’s a long way to go to pass all the necessary clinical regulatory hurdles.

In addition to the co-founders, Levner says the team includes a number of medical, diagnostics and regulatory experts — naming Dr Shai Izraeli (Hematology-Oncology) and Janice Hogan (who he says has previously developed regulatory strategy for hematology analyzers) — as well as several diagnostics-industry experts he says he’s not currently at liberty to disclose.

He also says they recruited “renowned hematology experts” to lead their CBC clinical trials — naming one: Dr Carlo Brugnara, the director of the Hematology Lab at the Boston Children’s Hospital, who they quote in their press release, talking up the challenges for physicians of having to wait for blood test results, and saying: “Previous blood analyzers aimed at in-office testing have involved clinical compromises and are difficult to operate or maintain. OLO has the potential to deliver on the promise of accurate, comprehensive blood testing at the doctor’s office, even with a finger prick sample.”

Nearly half a petabyte of blood image data — sourced from Sight Diagnostics’ own clinical studies over the past four years — has been used to train the AIs powering the OLO blood diagnostics system. (Levner specifies this data “has been anonymized and used in accordance with ethical review (IRB) approvals from their respective clinical institutions”.)

While most of the additional tests they’re envisaging bringing to OLO in future would use the same single-use consumable model as the CBC test, he mentions a subset of tests they’ve been considering which could benefit from sending information digitally to a different facility.

“As one example, imagine that OLO is used to run a CBC for a patient, and an important finding is identified. In the future, the physician could order a follow-on test and have the already digitized blood images streamed to an expert,” he suggests. “The expert (or even multiple experts) could then analyze images remotely, saving the patient additional blood draws or travel.”

So while the initial business model is a traditional sales model — with Sight Diagnostics selling the OLO system plus as many test kits as required, and CBC tests taking place fully onboard the system, with no need for the device to connect to its servers — down the line, should all go to plan, there could be scope to bolt on a SaaS platform element. Such as for enabling clinicians to order additional follow on analyses, and with OLO streaming digitized blood images to remote experts. 

So if their technology is as accurate and robust as they claim, a lot more could flow from just a few drops of digitized blood.

News Source = techcrunch.com

Spring Health raises $6M to help employees get access to personalized mental health treatment

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In recent months, we’ve seen more and more funding flowing into tools for mental wellness — whether that’s AI-driven tools to help patients find help to meditation apps — and it seems like that trend is starting to pick up even more steam as smaller companies are grabbing the attention of investors.

There’s another one picking up funding today in Spring Health, a platform for smaller companies to help their employees get more access to mental health treatment. The startup looks to give employers get access to a simple, effective way to start offering that treatment for their employees in the form of personalized mental wellness plans. The employees get access to confidential plans in addition to access to a network and ways to get in touch with a therapist or psychiatrist as quickly as possible. The company said it has raised an additional $6 million in funding led by Rethink Impact, with Work-Bench, BBG Ventures, and NYC Partnership joining the round. RRE Ventures and the William K. Warren Foundation also participated.

“…I realized that mental health care is largely a guessing game: you use trial-and-error to find a compatible therapist, and you use trial-and-error to find the right treatment regimen, whether that’s a specific cocktail of medications or a specific type of psychotherapy,” CEO and co-founder April Koh said. “Everything around us is personalized these days – like shopping on Amazon, search results on Google, and restaurant recommendations on Yelp – but you can’t get personalized recommendations for your mental health care. I wanted to build a platform that connects you with the right care for you from the very beginning. So I partnered with leading expert on personalized psychiatry, Dr. Adam Chekroud our Chief Scientist, and my friend Abhishek Chandra, our CTO, to start Spring Health.”

The startup bills itself as an online mental health clinic that offers recommendations for employees, such as treatment options or tweaks to their daily routines (like exercise regimens). Like other machine learning-driven platforms, Spring Health puts a questionnaire in front of the end employee that adapts to the responses they are giving and then generates a wellness plan for that specific individual. As more and more patients get on the service, it gets more data, and can improve those recommendations over time. Those patients are then matched with clinicians and licensed medical health professionals from the company’s network.

“We found that employers were asking for it,” Koh said. “As a company we started off by selling an AI-enabled clinical decision support tool to health systems to empower their doctors to make data-driven decisions. While selling that tool to one big health system, word reached their benefits department, and they reached out to us and told us they need something in benefits to deal with mental health needs of their employee base. When that happened, we decided to completely focus on selling a “full-stack” mental health solution to employers for their employees. Instead of selling a tool to doctors, we decided we would create our own network of best-in-class mental health providers who would use our tools to deliver the best mental health care possible.”

However, Spring Health isn’t the only startup looking to create an intelligent matching system for employees seeking mental health. Lyra Health, another tool to help employees securely and confidentially begin the process of getting mental health treatment, raised $45 million in May this year. But Spring Health and Lyra Health are both part of a wave of startups looking to create ways for employees to more efficiently seek care powered by machine learning and capitalizing on the cost and difficulty of those tools dropping dramatically.

And it’s not the only service in the mental wellness category also picking up traction, with meditation app Calm raising $27 million at a $250 million valuation. Employers naturally have a stake in the health of their employees, and as all these apps look to make getting mental health treatment or improving mental wellness easier — and less of a taboo — the hope is they’ll continue to lower the barrier to entry, both from the actual product inertia and getting people comfortable with seeking help in the first place.

“I think VC’s are realizing there’s a huge opportunity to disrupt mental health care and make it accessible, convenient and affordable. But from our perspective, the problem with the space is that there is a lot of unvetted, non-evidence-based technology. There’s a ton of vaporware surrounding AI, big data, and machine-learning, especially in mental health care. We want to set a higher standard in mental healthcare that is based on evidence and clinical validation. Unlike most mental health care solutions on the market, we have multiple peer-reviewed publications in top medical journals like JAMA, describing and substantiating our technology. We know that our personalized recommendations and our Care Navigation approach are evidence-based and proven to work.

News Source = techcrunch.com

Intelligent recruiting platform Greenhouse picks up another $50M

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Finding the right talent is a make-or-break situation for any company — especially smaller ones, which might not have the robust tools (or pocket books) of larger companies like Google that have a complete system in place. Recruiting platform Greenhouse hopes to make that process a little bit easier, and it has caught the attention of investors.

The company said it has raised a new $50 million financing round from Riverwood Capital, bringing its total funding to $110 million. Greenhouse definitely isn’t the only company that’s starting to pick up a significant amount of funding recently by trying to crack open the process of talent acquisition and make it a little more data-driven. But as the cost and difficulty of collecting enormous amounts of data on different kinds of human activity has dropped with the emergence of new machine learning tools, the problems behind recruiting may also be one that can get a lot of help from employing the same data science rigor that powers a smart Google search result.

“Hiring tools and software in the market had been built for the previous generation, with an applicant tracking mindset to cover the basics of collecting resumes on your website,” Greenhouse CEO Daniel Chait said. “We saw that winning companies in the talent market were ones who were able to attract the right talent, identify difference makers in a sea of LinkedIn profiles, make really smart decisions in who to hire, deliver winning experiences, use data to optimize. They needed tools to accomplish those goals and much broader than the recruiting software.”

The typical consumer’s experience with Greenhouse has probably been a bunch of job listings on a website somewhere, where an employee can submit an application or additional information that the company wants. Under the hood, Greenhouse provides companies with ways to find the right funnels for their applications — whether that’s something like GlassDoor or smaller niches on the Internet with more isolated pockets of talent — and discover the right employees for the roles that are available. Data is collected on all this behavior, which in turn helps Greenhouse give better recommendations for companies as to where to find potential recruits that fit their needs.

All that has to be packaged together with a generally nice user experience, both for the typical consumer and for the companies. That can boil down to actually understanding the right questions to ask, the right requirements to post in a job listing, and also making sure the process is pretty quick for people that are applying for jobs. Greenhouse implements scorecards to help interviewers — which can turn out to be a big group, depending on the position — determine whether or not candidates are the right person for the job in a more rigorous manner. And Greenhouse also hopes to work with companies with its tools to eliminate bias in the recruiting process to produce a more diverse set of hires.

“Companies are continuing to invest in recruiting and talent acquisition software,” Chait said. “As issues of talent and hiring have become more central at the C-suite, companies continue to invest in this area. Companies are starting to see the difference between HR and talent acquisition as its own specialty. If you’re a big company that has an all-in-one HR suite, it’s all well and good to have payroll and benefits in your org chart in one place, but when it comes to hiring, iit’s very dynamic.”

Greenhouse is still pretty dependent on its partners, but the startup has a wide array of companies that it works with to ensure that all the right tools are available to clients to find the right candidates. If a change is coming on LinkedIn — one of the biggest homes of candidate profiles on the planet — Greenhouse is going to work with the company to ensure that nothing breaks, Chait said. Greenhouse provides an API-driven ecosystem to ensure that its tools reach all the right spots on the Internet to help companies find the best talent.

But Greenhouse isn’t the only recruiting-driven company to attract a significant round of funding. It isn’t even the only one to do so in the last month — Hired, another recruiting platform, said it raised $30 million just weeks ago to create a sort of subscription model to help funnel the right candidates to companies. But all this interest, including Greenhouse, is a product of attempts to try to find the right talent in what might be unexpected spots powered by machine learning tools that are now getting to the point where the predictions are actually pretty good.

News Source = techcrunch.com

Dirt Protocol raises $3M for a decentralized, blockchain-based approach to information vetting

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The team at Dirt Protocol is using blockchain technology to create a new approach to verify information.

The startup doesn’t plan to launch its platform until later this year, but it announced today that it has raised $3 million in seed funding from General Catalyst, Greylock, Lightspeed, Pantera Capital, Digital Currency Group, SV Angel, Avichal Garg, Elad Gil, Fred Ehrsam Linda Xi and others.

Founder Yin Wu previously created lockscreen startup Echo (acquired by Microsoft in 2015) and laundry startup Prim. She told me that after becoming interested in the cryptocurrency industry, she was concerned about the fear, uncertainty and doubt around coin offerings — after all, we’ve covered several ICOs where companies appear to have disappeared with people’s money.

“The market today is still unregulated, with high incentive for people to spread misinformation for personal gain,” Wu said.

Her solution? Build databases where anyone can contribute information, but where they have “skin in the game,” so there’s a financial penalty if they’re not truthful.

Dirt Protocol isn’t trying to create a single, definitive data repository, but rather to provide the tools for developers to build their own databases. Those databases might focus on things like ICOs (providing information like the team, the investors and the number of tokens in circulation), or online publishers (to help advertisers avoid bots), or professional listings and membership lists.

There will be a single token that works across the Dirt platform. Users will need to stake tokens to add new information to databases, to challenge an entry or to vote in disputes — you’ll be penalized (by losing tokens) for adding misinformation and rewarded for weeding out misinformation.

While that should create an economic incentive for people to not just avoid inaccuracies but also to actively remove them, it doesn’t fully address the question of determining the truth — who, ultimately, gets to decide whether an entry is accurate? Wu said Dirt will support a variety of different “governance structures,” whether that’s centralized moderation, free-for-all voting or a system where votes are weighted by reputation.

Wu also suggested that the system is designed in a way to discourage concerted misinformation campaigns. For one thing, hoaxers will probably want to target the more popular databases, but those are also the ones that should attract more active moderation. Plus, she said, “The more valuable the network, the more people are contributing information, the more expensive [it becomes to contribute].”

A recurring theme in our conversation is the advantage of a “decentralized” approach to data verification. Wu said that isn’t always the right way to go, but she said it makes sense when there’s a big platform with the centralized vetting that works too slowly, or in situations where “you can’t trust the curator” of information, or with data sets that are just proprietary and expensive to access — while you have to buy tokens to contribute information, Wu said that Dirt Protocol datasets should be freely accessible, and “no single party owns that information and can shut off access.”

In a similar vein, she said Dirt Protcool isn’t currently focused on making money. Ultimately, the business model will probably involve some combination of giving the software away for free and charging for additional services.

“We’re focused on creating this open dataset that anyone can use,” Wu said. “If we achieve that goal, I’m confident that some monetization will arise.”

News Source = techcrunch.com

Olio, the app that lets you share unwanted food items with your neighbours, picks up £6M Series A

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Olio, the hyperlocal food sharing app that wants to help tackle the world’s food waste epidemic, has picked up $6 million in Series A funding.

The U.K. startup offers a location-based app and website that lets you list and post a photo of unwanted food items to be shared with other people in the same neighbourhood. That is, food that you might otherwise throw away.

The company, Olio co-founder and CEO Tessa Clarke told me in a call earlier this week, was born out of the idea that a bottom-up, community approach — driven by individual behaviour — is the most scalable way to cut down on the amount of food households typically waste. She says about a third of food production is thrown away and/or allowed to perish, which mostly ends up in landfill, and that food in the home represents about half of this.

The startup helps businesses tackle the problem, too. Dubbed the “Food Waste Heroes Programme,” Olio is enabling companies, such as retailers or those operating events and corporate canteens, to utilise the Olio platform and community to become “zero food waste” organisations.

This sees companies charged a fee and in return Olio will dispatch its thousands of volunteers, who have been vetted and are trained in food hygiene, to come to their stores or outlets and collect unwanted food items. The volunteers then photograph and list the items on the app and offer themselves up as hyperlocal collection points. Most items are made available for sharing and picked up/distributed in just a few hours.

Clarke says the startup is also exploring the possibility of moving to a premium model, where the most active users of the platform pay for a subscription that gives them access to additional value-add features. The Holy Grail of hyperlocal advertising is an untapped opportunity, too, given that the app already boasts over half a million users.

What is most striking when hearing the Olio co-founder talk about the young company is how mission-driven she, her team and the app’s community are. That’s because not only is food waste an expensive problem — over $1 trillion per year, apparently — but the environmental dent food production and distribution makes is huge, while a growing population means that food shortages will realistically become an issue in the future. Factor in that Olio can and, to a certain extent, already is helping to alleviate food poverty, and it’s easy to understand why.

I also questioned Clarke on Olio’s reliance on volunteers and she said that the company currently receives far more volunteer applications than it can process. More broadly, since the time spent being active on the platform is unlikely to translate into a full-time job, since it only works by being very distributed and remaining steadfastly hyperlocal. Volunteers also get to keep up to 10 percent of the food they collect.

Meanwhile, Olio’s Series A round was led by Octopus Ventures, with existing investors including Accel, Quadia, and Quentin Griffiths (co-founder of ASOS & Achica) following on. Other new investors joining Octopus include Lord Waheed Alli’s Silvergate Investments 2, Bran Investments, Julien Codorniou (Facebook) and Jason Stockwood (Match.com, Simply Business).

News Source = techcrunch.com

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