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Ada Health closes $90M Series B led by Leaps by Bayer

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The digital health space continues cooking on gas: Berlin-based Ada Health has closed a $90M Series B round of funding led by Leaps by Bayer, the impact investment arm of the German multinational pharma giant, Bayer AG. Other investors in the round include Samsung Catalyst Fund, Vitruvian Partners, Inteligo Bank, F4 and Mutschler Ventures.

The startup last raised around four years’ ago, reporting a $47M Series A round in 2017. But don’t be fooled by the low lettering of these rounds: Ada Health has been working on its symptom assessment tech for around a decade at this point — relying, in the first several years of its mission, on private funding from high net worth individuals in Germany and elsewhere in Europe.

Initially it was also focused on building a decision support tool for doctors before pivoting to directly addressing patients via an AI-driven symptom assessment app.

It’s not alone in offering this type of tool. Others in the space include Babylon, Buoy, K Health, Mediktor, Symptomate, WebMD and Your.MD — but Ada claims its app is the most used and highest rated by users. It can also point to a peer reviewed study it led, which was published in the BMJ, and compared the condition coverage, accuracy and safety of eight competitors. The study found its app led the pack on all fronts.

One reason for that edge is that Ada Health’s medical knowledge base covers around 30,000 ICD-10 codes (aka the alphanumeric codes used by doctors to represent different diagnoses) at this point — which co-founder and CEO, Daniel Nathrath, tells us is “by far the largest coverage of any of the systems in this space”.

The Ada Health app, which launched in late 2016 — and remains free to use — has been downloaded by more than 11 million people across 150 countries so far. Users have completed some 23M assessments using the tool which he likens to having “24/7 access to your trusted family doctor”.

Currently, the app has support for 10 languages. But the goal with the funding is to push for truly massive scale.

“The idea is to help as many people as possible get better access to healthcare around the world,” Nathrath tells TechCrunch. “Our ambition is, in a few years, that a billion people instead of 11M people will be using out technology. In order to get there we think that working with the right investors can help us accelerate that growth path and give more people the benefit of our technology faster.”

“With 11M app downloads I believe we are the most used AI symptom assessment technology that I know of in the world,” he goes on. “We are also the most rated and reviewed app in the medical category of the App Store and Google Play Store ever — after, what, just four years. With about 300,000 ratings and reviews, most of them five-star. So… we have gained some users but we think it’s just the beginning.

“Digital health — with all the things you see going on — is at an inflection point where it’s being realized not only by the users who have already been using our technology but also by health systems, governments, and payers, insurers, and life sciences companies — I think everyone has realized digital health is here to stay.”

As well as putting its symptom assessment app directly in the hands of patients, Ada Health offers a suite of enterprise solutions where partners pay it to be able to embed and deeply integrate its triage technology into their websites and digital services. That means they can use it to offer an entry point for their users — to help direct them to the correct service and provide administrative support by arming clinicians with health information provided by patient via the Ada interface (and the AI’s own assessment) ahead of the appointment.

One publicly disclosed customer for Ada’s enterprise offering is Sutter Health, in the Bay Area.

“They have integrated Ada into their own homepage and into their app so people can use it as a digital front door to the entire service of Sutter,” says Nathrath, explaining that the difference vs the version of the app that patients can download is “people don’t just get generic advice”. “It’s fully integrated. So if it says — for instance — you need to go to the emergency room… then you can go straight into appointment booking.

“And not only that; when you book the appointment the outcome of the Ada pre-assessment can then be shared with the health professional who will then look at you so the doctor doesn’t start from a blank sheet of paper but is already pre-briefed and gets decision support in terms of ‘this is constellation of symptoms the patient is reporting’ and ‘based on that these could be the most likely diagnosis and these should be the tests, examinations or investigations I should consider next to get to the confirmed diagnosis’.”

The added advantage for Ada’s enterprise partners is that patient data arrives with the doctor that sees them already structured so — after a few confirmations — they can easily import it into their documentation, saying precious minutes per patient, per Nathrath. “[If] you save a few minutes with each patient that means you have more time for the patients who really need you and not the patients who maybe has a cold and shows up in the emergency room, which unfortunately is a reality,” he adds.

With this enterprise strand of its business Ada is continuing to provide support for doctors. Nathrath suggests its patient-facing app is also being used for some informal decision support for doctors too.

More and more doctors are using the app “together with their patients”, he tells us, or else recommending it to their patients —  asking them “so what did Ada say?”.

The role of AI in healthcare will be a core one, Nathrath predicts — given that demand for healthcare professionals is always going to outstrip supply.

He argues that’s true even with rising use of telehealth platforms which can certainly make more efficient use of doctors’ time.

Ada did, at one point, offer a telehealth service itself — before deciding to fully focus its efforts on AI — so its approach now is to partner and integrate with other healthcare and health data providers throughout the care ecosystem.

“We think there’s a place for telehealth, obviously. It adds convenience. During the pandemic I guess it had a special role where for many it was almost the only way to interact with a doctor,” he says. “So we do see a place for telehealth but we also see an issue with telehealth in that it doesn’t address the structural issue in healthcare — that simply there aren’t enough doctors to serve the entire population of the world.”

“We’re building Ada as a multi-sided platform,” he adds. “We’ll be computing different sources of input data — which is sensor data, wearables data, lab data, genetic testing data — that’s on the input side — and then on the more downstream, on the next step after Ada, we can partner with any telehealth company in the world. And we’re seeing enormous interest from literally all corners of the world where telehealth companies approach us. And insurance companies and governments — where they say yes there is a use-case for telehealth but we basically need something before that, that filters people to the right next step.”

Whatever that right next step is in a patient’s care journey, “Ada is like the gatekeeper at the beginning of the journey that then sends you on your way,” is how Nathrath puts it.

The overarching vision is that Ada becomes not just an app in your pocket but an omnipresent “personal health companion” — or what it describes as “a personal operating system for health” — which is powerful enough to deliver preventative healthcare by being able to aggregate all sorts of data and spot health issues sooner so as to enable earlier and less costly interventions.

“What we’re building is really much more than a symptom assessment technology,” he tells TechCrunch. “Where you would also take into account lab results which can now be done much more direct to consumer than was previously possible, sensors and wearables data — and you probably say that Samsung is one of our investors but we’re obviously talking to all the large players in the space about this; how we can integrate that data best — and all the way to genetic testing and even the full genome sequence.

“When you take all these different sources of health information and compute them against each other on a continuous basis you’ll have something like an early warning system for your health — which, again, from a population health and system level perspective should be desirable for anyone who’s in charge of providing healthcare or paying for healthcare because you can catch the problem when it’s still a £100 problem and not yet a £100,000 a year problem.”

Given that ambition it’s interesting that big pharma is investing in Ada. (And its PR notes that it’s also in talks with Bayer on a potential strategic partnership.) But Nathrath suggests that the industry is well aware of the shifts being driven by digital health — and keen to avoid its own ‘Kodak moment’, i.e. by not adapting to the coming changes in a timely enough manner.

If AI-powered health interventions end up being so successful that they can shrink drug bills through earlier intervention and more preventative care then it makes good business sense for big pharma to be plugged into the cutting edge of digital health.

At the same time this type of tech might end up driving demand for medicines — exactly because of its scalability and because it can present a higher dimension view of more people’s health — meaning there’s more opportunity for increased prescription. So there’s not really a downside for pharma to get involved here.

“We’re really excited about the possibilities we can find by working together [with pharmaceutical companies] to really deliver a better healthcare experience to patients,” says Nathrath. “If you look at Bayer they have a consumer health business, they also have a pharmaceutical business and if you look at the cases within Ada if you look at the top ten most common ones it’s very comparable to what a GP would see all the time and a lot of those basically can end up in the recommendation towards healthcare where oftentimes an over the counter drug will be enough to address the issue. One area where Bayer has a lot of offerings, of course. But then their spectrum goes all the way towards rare diseases — where we’re also particularly strong. Where they have some drugs that help patients with very rare conditions.”

There are also potentially major research riches to be derived from the health data generated via Ada’s app which could also be interesting to pharma companies doing drug discovery.

Although Nathrath emphasizes that app users’ data is never used for research purpose without explicit consent from the individual (as is required under Europe’s General Data Protection Act).

But he also notes that Ada is able to do some interesting studies based on aggregated user data, too — giving an example of how it looked at kids mental health during COVID-19 lockdowns, comparing areas where schools had been shut vs those where they had remained open. “You could really compare what happened in different countries,” he says, noting that rates of depression in kids in Germany where schools and pre-schools were closed went up by over 100%, whereas in Switzerland where schools remained opened throughout there was no rise and even a slight improvement in children’s mental health.

In another example, involving aggregated data from usage of the app in US, he says it was able to show that it could have spotted a measles epidemic via the cases in the app slightly sooner than the CDC’s official announcement of an epidemic.

“If you think about the potential of that, in terms of spotting outbreaks earlier, that can be quite significant,” he suggests.

“We think there’s really a long list of ways we can work together [with researchers, policymakers and pharma companies] for the benefit of patients,” he adds. “The mission of all the people I spoke to at Bayer was really similar to ours — which is to help people, basically… That’s why we’re really happy to work with them.”

Commenting on the funding in a statement, Dr. Jürgen Eckhardt, head of Leaps by Bayer, added: “Investing in breakthrough technologies that drive digital change in healthcare is one of the strategic imperatives for Leaps by Bayer and for the entire field of healthcare. Ada’s truly transformative technology, combining powerful artificial intelligence with an emphasis on medical rigor and high levels of clinical accuracy will lead the way in helping more patients and consumers in achieving better health outcomes sooner by intervening earlier in their healthcare journey.”

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Source: https://techcrunch.com/2021/05/27/ada-health-closes-90m-series-b-led-by-leaps-by-bayer/

AI

Why Machine Vision Matters to Your Business

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One of the most important kinds of artificial intelligence may be machine vision, also known as computer vision — image processing technology that allows machines to “see” the world like people can.

This tech is already having a major impact on the industry — especially the retail, warehousing, and manufacturing sectors. Any business owner should know about how machine vision may help reshape the economy over the next few years.

What Is Machine Vision and How Does it Work?

At its simplest, machine vision is the use of visual information and artificial intelligence to create algorithms that can process images — breaking them down into identifiable objects, scanning for patterns and looking for important information.

Machine vision technology has existed for decades, but it was rarely used due to the limitations of image processing technology and the high cost of sensors.

Recently, artificial intelligence has made machine vision much more practical.

With an AI-based approach like machine learning, if you have enough visual information — like photographs and recorded video — you can train an algorithm that’s capable of breaking down what a camera sees and picking out distinct, identifiable objects that a machine or robot can use.

For example, a machine vision algorithm trained on information from grocery stores may be able to identify the different products visible in a picture or video feed, as well as objects like shelves, barcodes, displays, customers and floorspace.

One of the better-known applications of machine vision is in self-driving cars. These cars are outfitted with a number of sensors that scan the environment around them — including cameras. Footage from these cameras are processed by a machine-learning algorithm.

This algorithm breaks down the visual data from the cameras into information that the self-driving system can use — like where the road is, the location of other drivers and obstacles the car will have to navigate around.

Fully self-driving cars haven’t hit the market yet — but smart driver assistance systems that use similar tech are starting to become common offerings in high-end vehicles.

The biggest beneficiaries of machine vision, however, are probably companies that can use the tech to streamline business processes.

How AI-Powered Image Processing Is Transforming Business

Across the economy, machine vision is being used in a few different ways.

In retail, machine vision often helps support “smart stores” that use networked sensors and AI to streamline customers’ shopping experience.

These smart stores include the cashierless stores being pioneered by Amazon right now. In these stores, cameras, combined with other sensors like shelf weight sensors and motion detectors, track customers as they move around the store and fill their cart.

Similar tech could also be used to make existing, non-smart stores more intelligent. For example, several companies are experimenting with the use of machine vision to create smart cashierless checkouts in stores that don’t adopt the grab-and-go model.

These could provide a more streamlined alternative to existing self-checkout systems without requiring the same investment that smart stores require.

In manufacturing, machine vision is often used for quality assurance purposes.

For example, you may see a manufacturer use machine vision on a conveyor belt robot that sorts out ideal products from those with obvious defects.

Another algorithm may be used just for color inspection of finished products. Manufacturers sometimes use color inspection for quality assurance processes, using color as a guide to look for chips in paints, defects or errors in components like color-coded wires.

With machine vision, the use of specific lights can help make this process even more effective. By using colored light, rather than pure white light, you can highlight certain colors and help the algorithm to track them.

Manufacturers also use machine vision to support new, self-piloting robots. In factories with warehouses, for example, some manufacturers are using autonomous mobile robots (AMRs) to partially automate picking and packing.

These robots use machine vision like self-driving cars to navigate the factory floor with little or no supervision. They can also use machine vision to read barcodes and identify individual objects, like pallets, allowing them to pick out items to transport around the factory.

How Your Business Can Benefit From Machine Vision

As machine vision becomes more popular, businesses across the economy will be able to benefit from new devices and platforms that use the tech.

A few cutting-edge applications of the tech are already widely available. These may help a number of businesses to automate processes that they couldn’t automate before, or to speed up tedious and difficult labor.

For example, there is a growing number of handwriting analysis and digitization tools on the market that use AI-powered optical character recognition (or OCR). These tools convert scans or photos of handwriting into digital text — reducing the need for transcription and making notes more accessible.

Retailers can benefit from machine vision-powered robots like those used by Walmart for inventory management. These robots move up and down aisles, using cameras to scan for products that need restocking.

Small businesses could also benefit from working with large manufacturers that have adopted the technology. Machine vision can help to reduce costs and improve product quality — for SMBs, this partnership could lower manufacturing expenses and the risk of defective products.

In some cases, it may also be possible to bring this technology in-house to improve quality assurance processes.

The Growing Importance of Machine Vision

AI is likely to become even more important to the business world over the next few years. Tech powered by artificial intelligence, like machine vision, will probably become more sophisticated at the same time.

Right now, businesses can use machine vision in a few different ways — like improving quality control or automating processes like inventory checks. Small businesses without the resources for complex AI-based solutions can also benefit from machine vision through tools like handwriting OCR apps.

Eleanor Hecks is editor-in-chief at Designerly Magazine. She was the creative director at a digital marketing agency before becoming a full-time freelance designer. Eleanor lives in Philly with her husband and pup, Bear.

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Ashirase, a Honda incubation, reveals advanced walking assistance system for visually impaired

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Globally, 225 million people are estimated to suffer from moderate or severe visual impairments, and 49.1 million are blind, according to 2020 data from the Investigative Ophthalmology and Visual Science journal. A Japanese startup that was incubated at Honda Motor Company’s business creation program hopes to make navigating the world easier and safer for the visually impaired.

Ashirase, which debuted as the first business venture to come out of Honda’s Ignition program in June, shared details of its in-shoe navigation system for low-vision walkers on Tuesday. The system aims to help users achieve more independence in their daily lives by allowing them to feel which way to walk through in-shoe vibrations connected to a navigation app on a smartphone. Ashirase hopes to begin sales of the system, also named Ashirase, by October 2022.

Honda created Ignition in 2017 to feature original technology, ideas, and designs of Honda associates with the goal of solving social issues and going beyond the existing Honda business. CEO Wataru Chino had previously worked at Honda since 2008 on R&D for EV motor control and automated driving systems. Chino’s background is evident in the navigation system’s technology, which he said is inspired by advanced driver assist and autonomous driving systems.

“The overlap perspective can be, for instance, the way we utilize sensor information,” Chino told TechCrunch. “We use a sensor fusion technology, meaning we can combine information from the different sensors. I have experience in that field myself so that is helpful. Plus there is overlap with automated driving because when we were thinking of safety walking, the automated driving technology had given us an idea for the concept.”

“Ashirase” comes from the Japanese words ashi, meaning “foot,” and shirase, meaning “notification.” As its name suggests, the device, which is attached to the shoe, vibrates to provide navigation based on the route set within an app. Motion sensors, which consist of an accelerometer, gyro sensors and orientation sensors, enable the system to understand how the user is walking.

While en route outside, the system localizes the user based on global navigation satellite positioning information and data based on the user’s foot movement. Ashirase’s app is connected to a range of different map vendors like Google Maps, and Chino said the device can switch to adapt to different information available on different maps. This capability might be helpful if, say, one map had updated information about a road blockage and could send over-the-air updates.

“Going forward, we want to develop the function to generate a map itself using sensors from the outdoor environment, but that’s maybe five years down the line,” Chino said.

The vibrators are aligned with the foot’s nerve layer, so it’s easy to feel the pulse. To indicate the user should walk straight ahead, the vibrator positioned at the front of the shoe vibrates. Vibrators on the left and the right side of the shoe also indicate turning signals for the walker.

Ashirase says this form of intuitive navigation helps the walker attain a more relaxed state of mind rather than one that is constantly alert, leading to a safer walk and less stress for the user.

This also allows the user to have more attention to spare for audible warnings in their environment, like, for example, if they were at a crosswalk, because the device cannot warn the user of obstacles ahead.

“Going forward, we’re thinking about technical updates for users who are totally blind because they don’t have such information like obstacle awareness like low-vision people,” Chino said. “So at this moment, the device is designed for low-vision walkers.”

While indoors, like in a shopping mall, the GPS won’t reach the user, and there isn’t a map for them to localize to. To solve for this, the company says its plan is to use WiFi or Bluetooth-based positioning, connecting to other devices and cell phones within the store, to localize the visually impaired person.

Ashirase is also considering ways to integrate with public transit systems so that the device can alert a user if they have arrived or are near their next stop, according to Chino.

It’s a lot of tech to pack into one little device that attaches to a shoe — any shoe. Chino said the device, which only needs to be charged once a week based on three hours of use per day, is made to be flexible and fit onto different types, shapes and sizes of shoes.

Ashirase intends to release its beta version for testing and data collection in October or November this year and hopes to achieve mass production by October 2022. It’ll have a direct-to-consumer model, the price of which the company is not yet ready to disclose, and a subscription model, which should cost about 2,000 to 3,000 Japanese Yen ($18 to $27) per month.

Chino estimates it’ll take the company 200 million Yen ($1.8 million), including the funds the company has already raised, to make it to market. So far, the company has raised 70 million Yen ($638,000), which came in the form of an equity investor round and some non-equity rounds, according to Chino.

Honda maintains an investor role in the company, supporting and following the business along the way, but Ashirase’s aim is to go public as a standalone company.

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Source: https://techcrunch.com/2021/07/28/ashirase-a-honda-incubation-reveals-advanced-walking-assistance-system-for-visually-impaired/

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iPhone 13 To Introduce a New Feature From Apple Watch

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In his weekly newsletter, Bloomberg journalist Mark Gurman, who often conveys an accurate understanding of Apple’s plans, said the iPhone 13 may have an Apple Watch-inspired always-on mode.

Always-On Mode Feature

The Apple Watch Series 5 and Apple Watch Series 6 have displays that can stay on with low refresh rates and brightness, allowing the user to see their watch even in low light. The same functionality on the iPhone 13 can allow users to see details such as time, date, and notifications at all times.

The always-on iPhone display will be simplified with a larger iPhone 13 battery and an improved display. Previous rumors have suggested that the iPhone 13 will be getting bigger batteries, which could eliminate some of the extra power consumption of the always-on display.

What’s in It for Gamers?

Some iPhone 13 models are also widely expected to incorporate “ProMotion” power updates up to 120Hz, making movements in games appear smooth. This is believed to be facilitated by the use of the OLED LTPO display panel, which can vary in degree of refreshment while using a limited amount of power in order to save battery life.

Pros & Cons of iPhone 13

The device is expected to get heavier and thicker to support advanced displays and larger batteries. But since they will have the always-on feature, users might feel that it can be justified. The always-on display feature could be limited to advanced Pro models that are expected to get the LTPO display technology with ProMotion performance.

Earlier this year, leaker Mark Weinbach said the iPhone 13 will feature an always-on display, although it is important to note that Weinbach does not have a certified record. He said the always-on mode will look like a “toned down lock screen,” where the clock and battery are always visible, and notifications are displayed “with bars and symbols.”

The “Look” Factor

The iPhone 13 is also expected to offer several other enhancements, including an improved performance with the “A15” chip and enhanced camera capabilities, but the design of the iPhone 13 models is expected to be quite similar to the iPhone 12 models.

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Source: https://1reddrop.com/2021/07/28/iphone-13-to-introduce-a-new-feature-from-apple-watch/?utm_source=rss&utm_medium=rss&utm_campaign=iphone-13-to-introduce-a-new-feature-from-apple-watch

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Financial firms should leverage machine learning to make anomaly detection easier

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Anomaly detection is one of the more difficult and underserved operational areas in the asset-servicing sector of financial institutions. Broadly speaking, a true anomaly is one that deviates from the norm of the expected or the familiar. Anomalies can be the result of incompetence, maliciousness, system errors, accidents or the product of shifts in the underlying structure of day-to-day processes.

For the financial services industry, detecting anomalies is critical, as they may be indicative of illegal activities such as fraud, identity theft, network intrusion, account takeover or money laundering, which may result in undesired outcomes for both the institution and the individual.

There are different ways to address the challenge of anomaly detection, including supervised and unsupervised learning.

Detecting outlier data, or anomalies according to historic data patterns and trends can enrich a financial institution’s operational team by increasing their understanding and preparedness.

The challenge of detecting anomalies

Anomaly detection presents a unique challenge for a variety of reasons. First and foremost, the financial services industry has seen an increase in the volume and complexity of data in recent years. In addition, a large emphasis has been placed on the quality of data, turning it into a way to measure the health of an institution.

To make matters more complicated, anomaly detection requires the prediction of something that has not been seen before or prepared for. The increase in data and the fact that it is constantly changing exacerbates the challenge further.

Leveraging machine learning

There are different ways to address the challenge of anomaly detection, including supervised and unsupervised learning.

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Source: https://techcrunch.com/2021/07/28/financial-firms-should-leverage-machine-learning-to-make-anomaly-detection-easier/

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