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Analytics 4 Life nets $25M for AI-based cardiac diagnostic

Analytics 4 Life, which is developing a noninvasive, artificial intelligence-based platform that quickly diagnoses coronary artery disease, raised $25 million in series B financing to carry its device through clinical trials.

The Toronto-based company’s CorVista platform is built on its Phase Space Tomography technology. The system identifies areas of ischemia—to which blood flow is blocked—by measuring and mathematically modeling the signals emitted by the heart.

“The heart produces energy, and the healthy heart does so in a very predictable way,” said CEO Don Crawford. “Beat after beat, energy moves through the heart in a very predictable fashion that can be seen by our sensors.”

A patient undergoes a scan, which is sent to the cloud, where the company’s software analyzes it. The software creates a 3D model of the patient’s heart that shows potential areas of CAD. This, along with a patient’s symptoms and medical history, can guide physicians treating patients with chest pain.

Diagnosing coronary artery disease can take several weeks and involve the injection of contrast agents and putting stress on the heart. With its noninvasive test, Analytics 4 Life aims to cut down on the time it takes to return a diagnosis, as well as remove the need for radiation, contrast agents and cardiac stress.

Specifically, the company will use its new funding to bring the device through the final stage of clinical testing, Crawford said. It is currently in a two-stage clinical trial at 13 U.S. sites with more than 2,000 patients enrolled. Analytics 4 Life plans to announce data at the Transcatheter Cardiovascular Therapeutics conference next month and will file for de novo clearance with the FDA later this year. It expects to launch the device in the U.S. in 2018.

SOURCE: FierceBiotech

A new world of non-invasive cardiac diagnostics

Toronto-based Analytics 4 Life just raised $25 million in Series B financing to develop and ultimately commercialize their non-invasive cardiovascular diagnostic. The device, called CorVista, reads electrical signals from the heart and, with a little AI assistance, converts them into images to help clinicians assess cardiovascular damage. To some degree, the technology is adapted from rocket science.

“Our founding scientist (Sunny Gupta) was investigating signal processing for missile defense,” said President and CEO Don Crawford in a phone interview. “He was taking signals from a thousand miles away and figuring out what they come from – airplane, balloon, missile? If he could identify signals, he could apply the same technology to the body. He focused on the heart because it’s a giant electro-mechanical pump.”

CorVista is noninvasive, requires no drugs, radiation or stress testing. Clinicians place seven sensors on the patient, which collect 10 million data points in about three minutes. The data is sent to the cloud, analyzed and sent back as three-dimensional images physicians can interpret.

“Heart cells under stress give off different frequencies and amplitudes,” said Crawford. “As energy moves through the heart, it moves differently through areas of ischemia or disease. We’re looking at one-millionth of a volt in differences. That’s where the machine-learned algorithm comes into play.”

Analytics 4 Life is conducting a clinical trial with more than 2,000 patients at 13 sites. “We should wrap up the trial before the end of the year and file with the FDA in the first quarter of next year, said Crawford.”

Across the border, near Cincinnati, Genetesis is taking a similar, noninvasive approach to detecting heart disease, looking for a better way to rule out cardiac events.

“The standard of care is EKG, serial blood troponins, but you’ve got very low negative predictive values with these tests,” said co-founder and CEO Peeyush Shrivastava in a phone interview. “The population health indicates 75 percent of these cases aren’t cardiac. But with these low negative predictive tests, physicians aren’t necessarily confident ruling out cardiac-origin chest pain.”

The main concern is that a patient will have a myocardial infarction shortly after they leave the hospital. At present, the solution is to observe the patient over time, possibly days, put them on a treadmill for stress testing or conduct a catheterization procedure.

“Where our technology fits in is we have a really high negative predictive value,” said Shrivastava, “giving physicians the ability to rule out cardiac-origin chest pain, very quickly, without any invasiveness.”

The Genetesis device, called CardioFlux, requires more infrastructure than CorVista, including a shielded chamber to filter out magnetic sources. The sensors don’t touch the patient, so no prep is required, and the scan takes around 60 seconds. Algorithms convert the magnetic data into maps that highlight coronary artery disease, particularly ischemia.

So far, the technology has received some high-profile endorsements, including seed round funding from Mark Cuban and CincyTech. The company is collecting clinical data and hopes for FDA clearance sometime next year.

“Innovative technologies are needed,” said Shrivastava. “Not just incremental improvements but something that can really stand the test of time to solve a public health burden as large as this one.”

SOURCE: MedCity News

Med device, drug delivery companies join J&J’s Toronto JLABS incubator

Johnson & Johnson (NYSE:JNJ) said today it has added 24 companies, including those in the medical device and drug delivery fields, to its JLABS incubator facility in Toronto, Canada.

The facilities in Toronto now host over 40 companies, according to Johnson & Johnson, all of which are provided with lab space and offices, modular lab suites and access to scientific, industry and capital funding experts.

“Our goal is to support early stage innovators with the resources and network needed to grow, and as evident by the 40 companies that reside within JLABS @ Toronto, we are already accomplishing what we set out to do in just one year of operation. The no-strings attached model has been very important to our success in attracting so many quality companies, as it allows entrepreneurs the freedom to operate and do what is best for their company. We are hopeful that providing JLABS to the life sciences ecosystem in Toronto will support continued economic growth and development in the region,” JLABS head Melinda Richter said in a press release.

The new additions included a number of medical device and drug delivery companies, including:

  • Analytics 4 Life, a company developing machine-learned imaging solutions on its Agilytics artificial intelligence platform to non-invasively detect disease using intrinsic physiological signals.
  • Clerio Vision, a company developing a vision correction technology platform with applications in refractive surgery, cataract surgery and contact lenses.
  • Densitas, an advanced imaging analytics platform developer looking to improve mammography quality.
    ExCellThera, a novel stem cell therapy developer looking at therapies for blood-related diseases and cancers.
  • MIMOSA Diagnostics, a mobile health application and device developer aimed at aiding diabetes patients monitor foot health.
  • NerveVision, an FDA 510(k)-cleared nerve visualization and analysis software platform developer specializing in creating 3D, volume-rendered, reconstruction and segmentation of nerves from a standard MRI exam.
  • Pendant Biosciences, a materials company developing novel surface coatings and drug delivery technologies for the orthopedic market.
  • Tracery, a clinical-stage ocular imaging and health tech company developing diagnostic tools and individualized therapeutic strategies for retinal diseases.
  • Winterlight Labs, a company developing technology to detect and monitor cognitive and mental illness through short snippets of speech using AI.

“Through their world-class incubator, Johnson & Johnson Innovation is providing much needed infrastructure and access to funding sources for early-stage innovators to help drive their ideas forward. Ontario welcomes JLABS @ Toronto’s innovative and flexible platform, providing a total of 40 companies with access to incredible business resources while allowing researchers to keep the freedom and flexibility they need to be successful,” Economic Development & Growth Minister Brad Duguid said in a prepared release.

“Since opening its doors a year ago, JLABS @ Toronto has successfully attracted a multitude of promising companies from our province’s life sciences community, led by academic hospitals, world-class research institutes, top scientists and a strong health start-up network. They have helped our province continue to build up Ontario’s vibrant innovation ecosystem, create good jobs, and strengthen our position in the global knowledge economy while also providing access to incredible resources for our life science entrepreneurs,” Research, Innovation & Science Minister Reza Moridi said in prepared remarks.

SOURCE: MassDevice

I was worried about artificial intelligence—until it saved my life

Earlier this month, tech moguls Elon Musk and Mark Zuckerberg debated the pros and cons of artificial intelligence from different corners of the internet. While SpaceX’s CEO is more of an alarmist, insisting that we should approach AI with caution and that it poses a “fundamental existential risk,” Facebook’s founder leans toward a more optimistic future, dismissing “doomsday scenarios” in favor of AI helping us build a brighter future.
I now agree with Zuckerberg’s sunnier outlook—but I didn’t used to.

Beginning my career as an engineer, I was interested in AI, but I was torn about whether advancements would go too far too fast. As a mother with three kids entering their teens, I was also worried that AI would disrupt the future of my children’s education, work, and daily life. But then something happened that forced me into the affirmative.

An untraditional treatment
Imagine for a moment that you are a pathologist and your job is to scroll through 1,000 photos every 30 minutes, looking for one tiny outlier on a single photo. You’re racing the clock to find a microscopic needle in a massive data haystack.

Now, imagine that a woman’s life depends on it. Mine.

This is the nearly impossible task that pathologists are tasked with every day. Treating the 250,000 women in the US who will be diagnosed with breast cancer this year, each medical worker must analyze an immense amount of cell tissue to identify if their patient’s cancer has spread. Limited by time and resources, they often get it wrong; a recent study found that pathologists accurately detect tumors only 73.2% of the time.
In 2011 I found a lump in my breast. Both my family doctor and I were confident that it was a Fibroadenoma, a common noncancerous (benign) breast lump, but she recommended I get a mammogram to make sure. While the original lump was indeed a Fibroenoma, the mammogram uncovered two unknown “spots.” My journey into the unknown started here.

Since AI imaging was not available at the time, I had to rely solely on human analysis. The next four years were a blur of ultrasounds, biopsies, and surgeries. My well-intentioned network of doctors and specialists were not able to diagnose or treat what turned out to be a rare form of cancer, and repeatedly attempted to remove my recurring tumors through surgery.

After four more tumors, five more biopsies, and two more operations, I was heading toward a double mastectomy and terrified at the prospect of the cancer spreading to my lungs or brain.

I knew something needed to change. In 2015, I was introduced to a medical physicist that decided to take a different approach, using big data and a machine-learning algorithm to spot my tumors and treat my cancer with radiation therapy. While I was nervous about leaving my therapy up to this new technology, it—combined with the right medical knowledge—was able to stop the growth of my tumors. I’m now two years cancer-free.

I was thankful for the AI that saved my life— but then that very same algorithm changed my son’s potential career path.

A short-term shakeup
The positive impact of machine learning is often overshadowed by the doom-and-gloom of automation. Fearing for their own jobs and their children’s future, people often choose to focus on the potential negative repercussions of AI rather than the positive changes it can bring to society.

After seeing what this radiation treatment was able to do for me, my son applied to a university program in radiology technology to explore a career path in medical radiation. He met countless radiology technicians throughout my years of treatment and was excited to start his training off in a specialized program. However, during his application process, the program was cancelled: He was told it was because there were no longer enough jobs in the radiology industry to warrant the program’s continuation. Many positions have been lost to automation—just like the technology and machine learning that helped me in my battle with cancer.

This was a difficult period for both my son and I: The very thing that had saved my life prevented him from following the path he planned. He had to rethink his education mid-application when it was too late to apply for anything else, and he was worried that his back up plans would fall through.

He’s now pursuing a future in biophysics rather than medical radiation, starting with an undergraduate degree in integrated sciences. In retrospect, we both now realize that the experience forced him to rethink his career and unexpectedly opened up his thinking about what research areas will be providing the most impact on people’s lives in the future.

Although some medical professionals will lose their jobs to AI, the life-saving benefits to patients will be magnificent. Beyond cancer detection and treatment, medical professionals are using machine learning to improve their practice in many ways. For instance, Atomwise applies AI to fuel drug discovery, Deep Genomics uses machine learning to help pharmaceutical companies develop genetic medicines, and Analytics 4 Life leverages AI to better detect coronary artery disease.

While not all transitions from automated roles will be as easy as my son’s pivot to a different scientific field, I believe that AI has the potential to shape our future careers in a positive way, even helping us find jobs that make us happier and more productive.
Forging a path forward

As this technology rapidly develops, the future is clear: AI will be an integral part of our lives and bring massive changes to our society. It’s time to stop debating (looking at you, Musk and Zuckerberg) and start accepting AI for what it is: both the good and the bad.
Throughout the years, I’ve found myself on both sides of the equation, arguing both for and against the advancement of AI. But it’s time to stop taking a selective view on AI, choosing to incorporate it into our lives only when convenient. We must create solutions that mitigate AI’s negative impact and maximize its positive potential. Key stakeholders—governments, corporates, technologists, and more—need to create policies, join forces, and dedicate themselves to this effort.

And we’re seeing great progress. AT&T recently began retraining thousands of employees to keep up with technology advances and Google recently dedicated millions of dollars to prepare people for an AI-dominated workforce. I’m hopeful that these initiatives will allow us to focus on all the good that AI can do for our world and open our eyes to the potential lives it can save.
One day, yours just might depend on it, too.

SOURCE: Quartz

A4L uses machine learning to detect coronary artery disease

Analytics 4 Life (A4L) is an artificial intelligence company developing non-invasive, cost-effective medical devices. In January of 2017, the company moved into new office space at JLABS @ Toronto, where it employs a team of approximately 20 people, with another 10 located at its U.S. headquarters in Research Triangle Park, North Carolina, and around the Eastern U.S. “We’re really lucky to be at JLABS inside of MaRS Discovery, and the fact that the Vector Institute for Artificial Intelligence recently launched within the same building, it’s very fortunate for us,” says Shyam Ramchandani, Ph.D., Vice President of Clinical Affairs at A4L.

“The idea for A4L came from the company’s Founder and Chief Scientific Officer, Sunny Gupta, who spent time with the Canadian Armed Forces in Kingston,” says Ramchandani. “Canada has strengths in signal analysis due to its involvement in NORAD.” Canada’s signal intelligence facility at Canadian Forces Station Alert, the most Northern permanently inhabited settlement in the world, is where advanced technology is deployed to warn against military threats, such as the presence of nuclear submarines and torpedo launches.

“My family has a history of heart disease,” recalls Gupta, “And one day while working at the Canadian Armed Forces, it struck me: why couldn’t advanced signal analysis apply to physiologic signals emitted by the heart to better diagnose disease?” The company launched officially in July 2012, set to tackle the #1 cause of death in the developed world: heart disease. But as Ramchandani explains, they actually started about a year and a half earlier out of a 100 sq. ft. administrative office above a church in Kingston, Ontario. “It was just three of us and our laptops, exploring ways to analyze biological signals using machine learning,” he recalls. A4L went on to leverage the SOSCIP R&D consortium, which gave them access to IBM cloud computing infrastructure and highly qualified local talent through the TalentEdge Program.

A4L sees machine learning as a promising opportunity to assist doctors in diagnosing disease
The American College of Cardiology defines significant coronary artery disease as when a patient has at least one major coronary artery blocked to 70% or greater, and the best way to diagnose the disease is through catheterization. During the procedure, a specialist feeds a catheter through a patient’s vein, usually from their leg, to their heart. A dye is then released into their bloodstream and x-ray images are taken in real time, so a physician can see where the dye is going, with the pinch points representing blockages within the vessels. Doctors can then go in and open up the vessels using stents. “The problem is, you don’t just send patients into the cath lab without just cause,” says Ramchandani. “While it is a fairly routine procedure, there still is a mortality rate, and patients are exposed to radiation.”

While there are several different tests doctors send patients in a workup to catheterization, one of the most common is the Nuclear Perfusion Test or Nuclear Stress Test. Every year, between six to eight million nuclear stress tests are performed in the U.S. on patients identified as at risk of having heart disease. It’s similar to a routine exercise stress test but with images taken so they can detect whether areas in your heart are receiving less blood flow. After fasting for 6 to 12 hours, the patient is injected with radioactive dye. Images are then taken before and after intense exercise in order to compare blood flow. “The entire procedure takes approximately 4 to 6 hours,” Ramchandani explains, “and usually a follow-up appointment to review results takes place days or weeks after the procedure, as the images need to be read by a radiologist or cardiologist. What’s more, the radioactive dye is equivalent to receiving about 600 chest x-rays, and the test records false positives about 60% of the time as demonstrated in the PROMISE study.”

In terms of infrastructure, all you really need is a good 3G Wi-Fi connection

– Shyam Ramchandani on the simplicity of A4L’s solution

With A4L’s test, the patient lies down and six electrodes are applied to the chest and one on the back. The data is pushed to the cloud, where a machine-learned algorithm crunches the signals. By the time the patient’s shirt is back on, the results are ready for the doctor and patient to review. “In terms of infrastructure, all you really need is a good 3G Wi-Fi connection,” says Ramchandani. “There are actually three components of the product at work. 1) The Phase Signal Recorder device collects the physiologic signals and transmits the signal package to the cloud for analysis. 2) Phase signals are analyzed by the cloud-based, machine-learned algorithm. 3) And results are available on our web-based physician portal.”

Opportunities abound for A4L’s proprietary machine-learning platform
The information within these signals was previously hidden, but that is where the machine learning comes into play. “We take the signals from patients who we know have coronary disease and those who do not, and we use that data to train our algorithm to separate the positives and negatives. We then test that algorithm prospectively on the next set of data to assess how well it runs,” explains Ramchandani. He believes the opportunities for this platform are substantial. Not only can it be used for diagnosing other types of cardiovascular disease and other forms of medical imaging, but it could also be applied to other industries. “You could literally use this in mining for precious metals and gas if you wanted to,” he says. “The mining industry uses different types of seismic signals in the discovery process. If you have the right training data, you could use artificial intelligence to accurately predict whether or not you’re sitting on top of a mineral deposit.” A4L’s device is not yet FDA approved or available for commercial use, as the company plans to share early results of their first clinical trial in late 2017.

SOURCE: Invest in Ontario

Taking collaborative innovation to a higher level

Driving innovation is the focus of SOSCIP—an ambitious research and development consortium that is supporting ground-breaking scientific advances in areas with significant commercialization opportunities. Originally established with funding from FedDev Ontario’s Prosperity Initiative, and expanded with a contribution from the Investing in Commercialization Partnerships initiative,  SOSCIP brings academia and industry together to accomplish life-improving research that has led to job creation and prosperity in southern Ontario.

“From the beginning, FedDev Ontario has played an important role in the development and success of SOSCIP,” says Elissa Strome, Executive Director of SOSCIP. “Similar to FedDev Ontario, we believe that collaboration fuels innovation and ultimately contributes to a better, healthier and sustainable economy.”

SOSCIP is a collaborative R&D platform that pairs academic researchers, small- and medium-sized companies, along with IBM as a lead industry partner, to harness advanced computing and big data analytics technology. It was created out of the understanding that researchers and industry could work together to bring about successful innovation strategies.

The consortium owns multiple advanced computing resources operated by partner academic institutions across the province. These include a cloud-based platform that provides project partners free access to IBM data analytics software and Canada’s fastest supercomputer—the IBM Blue Gene/Q—which is particularly suited for large-scale modelling and simulation applications.

Since its beginning in 2012, SOSCIP has grown to include 15 of Ontario’s most research-intensive academic institutions that are committed to solving problems that improve our everyday lives—from our cities and health, to cybersecurity and advanced manufacturing. The results of this important collaboration have been extremely positive and the number of active projects is growing.

Analytics 4 Life (A4L) was one of the first start-ups to benefit from SOSCIP. A4L uses artificial intelligence to develop machine-learned solutions to identify and assess disease, and IBM’s cloud infrastructure and computing power is supporting the development of the company’s mathematical models. A4L is currently running a clinical trial with over 1,200 participants to help find a better and noninvasive way to diagnose cardiac dysfunction.

“Having access to SOSCIP’s resources has accelerated our development timeline,”says Sunny Gupta, Founder and Chief Scientific Officer. “Without SOSCIP, it would have been much more difficult for a small company like A4L to obtain the funding and computational power necessary to analyze billions of data points.”

Spring 2017 marks the five-year anniversary of SOSCIP, something that Strome says is “both humbling and inspiring.” There are more than 40 active projects and over 150 students and post-doctoral fellows actively engaged in research and gaining invaluable skills.

“It’s difficult to pinpoint one great achievement,” says Strome. “Our achievements are found in the continued support and expertise that we offer our researchers and industry partners.”

SOURCE: FedDev Ontario

Straight from the heart

The human body emits millions of physiologic signals, much of which is conventionally believed to be noise, but what if these signals could provide a deeper understanding of our biology and distinguish a healthy heart from a diseased one, even before symptoms appear?

That is precisely what Analytics 4 Life (A4L) is trying to achieve. A4L is using advanced signal processing techniques to identify and assess diseases, particularly coronary artery disease.

Traditionally, patients have to endure a nuclear stress test to evaluate the heart’s blood flow. The invasive test can take hours to days to deliver test results.

A4L’s approach involves machine learning on non-invasive, passively collected signals for heart function. The process of signal collection results in no discomfort to the patient, and is easy to use for the health care provider.

If successful, A4L’s device will streamline the process of identifying cardiac dysfunction to mere minutes and have the results available to the patient’s doctor the same day, with no exposure of the patient to radiation.

A4L is the brainchild of founder and chief scientific officer Sunny Gupta. Coming from a family with a history of heart disease, he wanted to know what information might be taken from the heart’s signals.

The idea derived from the research of Prof. Terence Ozolin at Queen’s University who sought Gupta’s help in identifying diseased hearts in developing mice with pre-existing conditions. Gupta used machine learning to determine which mice would eventually demonstrate the conditions they were born with.

“With 50,000 physiologic signals, we needed more horsepower,” explained Shyam Ramchandani, vice-president, clinical affairs.

“SOSCIP and IBM provided the cloud infrastructure and the computers that we needed,” said Gupta.

The team was able to predict which mice would develop heart conditions later in life.

Gupta founded A4L out of a small room above Chalmers United Church in Kingston, Ontario, and incorporated in July 2012. The company has since expanded to multiple office locations across Ontario and has raised over C$20 million from investors. A4L holds three issued U.S. patents with eighteen more U.S. and international applications pending and have been cleared for testing the device on humans in the U.S. They hope to complete the clinical trials by the end of 2017.

The team perform mathematical extrapolations from the signals and convert them into 3D shapes that have geometric form. They can use these forms to identify and pinpoint disease.

If successful, the device may be able to be used to examine countless other diseases and conditions.

SOURCE: Southern Ontario Smart Computing for Innovation Platform (SOSCIP)

OCE Success Story: Analytics 4 Life

Heart disease is the second leading cause of death in Canada and an estimated 1.3 million Canadians are currently living with it. Early detection is crucial to decreasing death rates, but current diagnostic techniques can be invasive and costly.

Analytics 4 Life (A4L) has developed an analytics platform with the potential to disrupt the health-care space by enabling non-invasive, affordable screening and diagnosis of various diseases. The platform uses proprietary machine-learned algorithms to reveal previously hidden information about a patient’s health status from within their physiological signals. The first application of the platform will be cardiac conditions. The technology could replace expensive, cumbersome methodologies such as nuclear stress tests and x-ray based computed tomography. Its ease of use will enable diagnostic testing for cardiac diseases in a broader range of settings, including at home.

OCE first supported A4L in 2014 through a Medical Sciences Proof-of-Principle (MSc PoP) project with Queen’s University that explored predictive measures of cardiotoxicity. A TalentEdge Internship project helped the company develop a Quality Management System to ensure it meets compliance for Canadian and European markets.

In 2016, A4L became a primary tenant in IBM’s Innovation Space in Toronto, part of a partnership between OCE and IBM that offers an integrated suite of globally disruptive, advanced computing technology infrastructure and programming to Ontario SMEs. With support from a SOSCIP-OCE TalentEdge Fellowship, the start-up is further developing the machine learning platform that forms the basis of its cardiac-specific system.

A4L recently launched a two-stage, U.S.-based clinical study that is enrolling up to 2,500 subjects at as many as 25 sites. The first stage is designed to gain insight for its algorithms. The company plans on commercializing its system in the U.S. followed by Europe and Canada.

Return on Innovation

  • Raised $10 million in Series A financing
  • Currently has 16 full-time employees
  • OCE Investment: $134,899


Source: Ontario Centres of Excellence

IBM launches Toronto startup hub, enrolls first participant

IBM and the Ontario Centres of Excellence launched a startup hub in Toronto this week, enrolling e-health player Analytics 4 Life (A4L) as its first participant.

The IBM Innovation Space is the first program to launch under IBM’s $54 million Innovation Incubator Project, according to a statement. The company put up $24.75 million for the initiative, while the Government of Ontario’s Jobs and Prosperity Fund contributed $22.75 million and other partners made up the rest.

The startup hub will offer capacity, networking, infrastructure and IBM’s cloud and cognitive business tech to help startups propel their ideas from research to commercialization, according to the statement. Experts from IBM will provide mentoring, support services, education and legal counsel to support startups aiming to attack some of Canada’s biggest challenges in sectors including healthcare and natural resources.

Toronto-based A4L, the flagship company in the program, started with the notion that machine learning could be harnessed to identify disease states by applying it to physiological “signals” emitted by the body.

In May, A4L unveiled its Coronary Artery Disease Learning and Algorithm Development clinical study, which aims to enroll up to 2,500 participants in the U.S. “A4L is currently testing a system that safely records patient signals and wirelessly transmits these files to secure cloud storage that allows powerful computer analytics to access and analyze billions of data points in real time,” said Chief Medical Officer Dr. William Sanders in a statement.


Source: FierceBiotech

Analytics 4 Life Takes The Stress Out Of Coronary Artery Disease Tests

TORONTO, Jun. 8, 2016 /TechPORTFOLIO/ — Data collected and crunched in the cloud aims to replace test that involves radioactive dye injections and running on a treadmill.

For a disease recognized as the most common global cause of death – 8.14 million worldwide in 2013 – coronary artery disease is extremely laborious to diagnose. A Canadian firm, Analytics 4 Life, is looking to cut out a large part of the labor involved – and the danger posed to patients – by leveraging data in new ways.

In a nuclear stress test, patients must exercise on a treadmill to measure blood flow after radioactive dye is injected. Physicians and hospital workers need to spend resources on managing radioactive nuclei. The test can take up to five hours and is only 75% accurate.

Analytics 4 Life, a startup based in Kingston, Ontario, is attempting to develop a much more straightforward method, using machine learning through neural networks and genetic analysis, with physiological information conventionally considered valueless.

“We’ve been able to demonstrate a very simple test that takes about three minutes to do where you don’t have to stress the patients,” says Shyam Ramchandani, Director of Marketing and Business Development at A4L. “It’s just surface electrodes that go on patches on the body: seven of them. Three minutes later, they’re done, and then by the time their patches are off and their shirt’s on the result is on the doctor’s portal.”

This month, A4L is running its first machine learning tests with recruited patients already diagnosed with coronary artery disease, a condition where the vessels that supply oxygenated blood to the heart are obstructed by plaque; if the plaque builds up excessively and hardens, the condition leads to blood clots, angina and cardiac arrest.

After crunching what A4L calls “phase energy” data – which draws on a wide array of physiological signals – from the electrodes, and generating a formula based on the results, the company will then test blind on another selection of patients to see if their formula is an effective predictor.

“‘Phase energy’ is purely a mathematical concept,” explains Ramchandani. “There is no current physiological description of this. We will be the first to demonstrate this.”

The test itself can be administered from a “phase energy signal recorder”, an iPad Mini adapted with proprietary technology to connect to the electrode input. The recorder transfers the data to the cloud. The front-end software and the data processing lives on IBM SoftLayer, and important code infrastructure is hosted on IBM’s Bluemix platform.

This setup makes it all portable. “You technically can take our test anywhere you have a 3G signal,” says Ramchandani. “You wouldn’t have to fly people in from remote areas to a place that has a special camera.” According to Ramchandani, IBM infrastructure is ideal for handling healthcare data. “We have an almost off-the-shelf HIPAA compliant tool. When you’re collecting medical information, you have to either de-identify it in a way that it can’t connect it back to the patient, or it needs to be hosted on and transmitted on infrastructure that’s been validated for security purposes.”

A4L completed series A funding last August for CA$10 million, and is hoping to complete series B at the beginning of 2017 to help it fund commercialization activity and a pivotal clinical trial.

That pivotal clinical trial will support their next key step: the FDA approval process. “If you can’t get through regulatory affairs in an efficient manner and get the kind of reimbursement you need, it’s not going to be a business,” Ramchandani says. The company has made senior level hires in order to facilitate interaction with the FDA.

Another option for A4L is expanding in Europe. (They will not start with Canada initially, because the market is too small.)

Although a price for the test hasn’t been set, A4L says that the overheads for its new system, once approved and functioning, are going to be astronomically less than the status quo. “We have no regulated nuclei that needs to be injected, purchased, or handled,” says Ramchandani. “You don’t need a specialized technician, or a specialized camera.”

And no more running on a treadmill, either.