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FierceMedTech’s 2017 Fierce 15

When we were compiling this year’s crop of Fierce 15 companies, some clear themes stood out. Genomics. Bioelectronics. Artificial intelligence. But while multiple companies are working to improve the use of these technologies, there runs a deeper current: making care more accessible.

For example, Allurion Technologies is developing a weight-loss balloon that is swallowed in an outpatient procedure and is naturally passed from the body at the end of the treatment period.

While commercially available balloons are effective, the company’s founders said, they are delivered and removed via endoscope. By removing the need for endoscopy and a specially trained physician to perform it, Allurion aims not only to slash the cost and the time required for the treatment, but also to enable more doctors to offer weight-loss balloons.

In a similar tack, Livongo based its diabetes management program on the glucose meter, not the most cutting-edge of diabetes technology, but a tool that most patients use to monitor their disease.

Meanwhile, Cambridge Medical Robotics is reimagining minimally invasive robotic surgery. Rather than selling expensive systems, it is working on offering modular robotic arms as a service. A hospital signs on to perform a certain number of operations over a period of time. And the company will handle the rest—from the technology and surgeon training to assistance and instrumentation.

And Toronto-based Analytics 4 Life is working on a quicker test for coronary artery disease that does not require the administration of radioactive contrast dyes or the exercising of a damaged heart. The device, dubbed CorVista, is designed for use in any physician’s exam room or the emergency room.

The list also includes Saluda Medical and Axonics, which are both trying to improve on current approaches to neuromodulation, and 7SBio and Velano Vascular, both of which chose to focus on upgrading blood collection methods, bettering the hospital experience for both patients and practitioners.

As for Gecko Biomedical, the Paris-based startup hit the ground running with a surgical sealant. But it is eyeing all sorts of indications for its proprietary polymer platform, from adhesives and plugs to 3D-printed devices and scaffolds for tissue regeneration.

We would like to thank everyone who submitted nominations. We received a remarkable number of them this year. Read on to find out what drives this year’s Fierce 15 companies and what they are working on. As always, let us know what you think, and continue to flag any prospects worthy of watching over the coming year and beyond.

Analytics 4 Life

CEO: Don Crawford
Based: Toronto
Founded: 2012

The scoop

Starting with coronary artery disease (CAD), Analytics 4 Life is combining artificial intelligence and mathematical modeling to accelerate the accurate diagnosis of disease and connect patients to the appropriate treatments more quickly.

Analytics 4 Life is seeking de novo clearance from the FDA for its CorVista platform for the detection of CAD. Based on the company’s Phase Space Tomography technology, CorVista measures and mathematically models signals emitted by the heart to identify areas of ischemia, or where blood flow is blocked.

“This allows us to produce a diagnostic test that a physician can use to identify coronary artery disease without the need for radiation or to exercise a distressed heart,” said CEO Don Crawford.

What makes Analytics 4 Life fierce

CAD, caused by plaque buildup in the coronary arteries, is the most common form of heart disease in the U.S. Physicians look for it with a battery of tests, from stress testing—which requires the patient to exercise—and electrocardiograms to coronary angiography, which involves injecting the patient with a contrast dye.

Existing diagnostic methods are very capital-intensive, Crawford said: “The patient has to travel because only major centers can afford it.” So Analytics 4 Life is working to create a piece of equipment that can be used in any physician’s exam room, emergency room, or hospital room, he said.

A patient comes in and undergoes a noninvasive CorVista scan, which takes under three minutes. The technology scans for six different signals, collecting millions of data points.

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

The patient’s scan is then sent to the cloud, where the company’s software analyzes it, returning a three-dimensional picture of the heart, with areas of ischemia highlighted. The physician may then use this model to guide treatment decisions.

In addition to removing the invasive, radioactive and stressful aspects of CAD diagnostics, the tech could also cut the time it takes to produce a diagnosis, getting patients the correct treatment more quickly.

What to look for

In September, the company bagged $25 million in its series B round, to bring its CorVista system through the final stage of clinical testing. It reported early data from its learning database at the Transcatheter Cardiovascular Therapeutics conference.

Analytics 4 Life expects to complete enrollment for its 3,000-patient clinical trial by the end of the year. It will use the results from this trial to apply for de novo clearance from the FDA in the first half of 2018. Pending approval, the company plans to roll the device out in the second half of next year.

SOURCE: FierceMedTech
READ MORE: Analytics 4 Life

Artificial intelligence is being explored as a new tool for diagnosing coronary artery disease

We are witnessing a new era in which artificial intelligence is being applied to areas of research, imaging, diagnostics, and treatment. In cardiovascular medicine, it is being used in various ways from genomics to cardiac imaging analysis, yielding technology and tools that could potentially change diagnostic testing to improve patient care. Thomas Stuckey explores a new approach that is using cardiac phase space tomography analysis and advanced machine learning to detect significant coronary artery disease.

The current methods of detecting coronary artery disease are cumbersome, often taking weeks or months for a diagnosis, and can expose patients to radiation and stress; and in some cases, unnecessary and invasive heart catheterisations. Manesh Patel (Duke Heart Center, Durham, USA) and colleagues report in The New England Journal of Medicine that up to two thirds of patients who are undergo invasive cardiac catheterisations are subsequently found not to have significant obstructive disease.* As healthcare reimbursement, in the USA, moves into a new value-based healthcare model with capped coverage for patients, there is a timely need to find a new coronary artery disease testing pathway for both patients and physicians.

The CADLAD study

At the LeBauer-Brodie Center for Cardiovascular Research and the Cone Health Heart and Vascular Center, my colleagues and I have been conducting a clinical trial—CADLAD (Coronary artery disease learning and algorithm development)—to evaluate a novel imaging technology. Analytics 4 Life, the company behind the trial, is using artificial intelligence to develop an imaging technology to assess for the presence of coronary artery disease. This technology is designed to use only intrinsic phase signals scanned from the body; therefore, avoiding exposing patients to radiation, heart rate acceleration, or injections of contrast agents.

The CADLAD study is comparing the accuracy of Analytics 4 Life’s cardiac phase space tomography analysis (cPSTA) system to detect the presence of significant coronary artery disease to that of cardiac catheterisation results. The system uses a hand-held digital instrument that non-invasively scans and transmits a patient’s phase signal data to a secure, cloud-based repository, where software then analyses the data to identify areas of ischaemia from coronary artery disease that can be interpreted by physicians.

In stage I of the CADLAD trial, 606 phase signals were obtained from patients at rest, just prior to angiography. Angiographic results were paired with signal data, from which features were extracted, and a training set of 512 signals were used for machine learning, with 94 reserved for blind testing. In the cohort of 94 patients tested, the area under the curve (AUC) was 0.80. The study revealed the machine-learned predictor had a sensitivity of 92% (95% CI: 74%-100%) and specificity of 62% (95% CI: 51%-74%) on blind testing. The negative predictive value was 96% (95% CI: 85%-100%).

Gender-based assessment of coronary artery disease by cPSTA revealed positive initial data to suggest that resting cPSTA imaging performs well overall, and that the results in women are equivalent or better as compared with men. These findings are valuable for the historically under-served and difficult-to-diagnose female subpopulation whose nuclear scans may have breast imaging artifacts that can result in false positives.

Assessing coronary artery disease by cPSTA in elderly and obese patients, other difficult-to-diagnose subpopulations, performed well with negative predictive values of 83% (95% CI: 50%-100%) and 94% (95% CI: 79%-100%), respectively. The current coronary artery disease detection pathways for obese patients are at high-risk for abnormal test results, due to exercise limitations, diaphragmatic attenuation artifacts, and image acquisition limitations. These early results show a promising potential for alternative technology to establish new coronary artery disease detection pathways on specific populations where current conventional CAD detection pathways tend to be less accurate. More promising, no safety issues were observed (no AEs in 606 procedures).

Conclusion

Current CADLAD stage I results were in a population of high-risk coronary artery disease patients already showing signs of coronary artery disease, such as chest pain. The next stage will be to test for coronary artery disease in a low-risk population to prove continued success in the negative predictive value of the test. This innovative approach to cardiac diagnostics is a great example of how to leverage artificial intelligence in healthcare to improve patient care. There is no question that it will take time to adapt to evolving and disruptive technologies that artificial intelligence offers, but it is important that we keep an open mind and embrace change in order to improve patient care.

Thomas Stuckey, M.D. is medical director at LeBauer-Brodie Center for Cardiovascular Research and Education at Cone Health, Greensboro, USA.

Reference
*Patel et al. N Engl J Med 2010; 362: 886–95.

SOURCE: Cardiovascular News

Will Phase Space Tomography Revolutionize Cardiac Diagnostics? Interview with Don Crawford, CEO of Analytics 4 Life

Human body emits all kinds of signals that, if analyzed with the proper sensors and computers, can help us develop completely new diagnostic and therapeutic modalities. Most medical technology advancements are improvements of existing devices, but some people try for bigger leaps.

Analytics 4 Life is a company based in Toronto, Canada, that is developing a new technology called Phase Space Tomography, which doesn’t require any radiation and is easy on the doctor and patient to administer. The company hopes that one day its technology may become a standard part of cardiac workup. We had a chance to ask Don Crawford, CEO of Analytics 4 Life, a few questions about how the technology works and what it could mean for the practice of medicine.

Medgadget: To start us off, please give us a summary of the technology Analytics 4 Life has developed and how it’s to be used in clinical care.

Don Crawford, Analytics 4 Life: Analytics 4 Life is pioneering digital health using artificial intelligence to develop a completely new form of medical imaging. By combining advancements in artificial intelligence, cloud computing, and digital technologies with a novel approach to cardiac imaging based on advanced disciplines of mathematics and physics, we are developing CorVista, a non-invasive, physician-directed diagnostic test that aims to assist physicians in identifying the presence of coronary artery disease (CAD) without radiation, cardiac stress, contrast agents, or patient fasting.

A CorVista procedure can be broken down into four simple steps:

  1. First, the patient undergoes a CorVista scan where signals naturally emitted by the heart are collected while the patient is at rest.
  2. After the scan, the patient’s phase signal data is automatically transferred to our cloud-based repository for…
  3. Cloud-based analysis. There, advanced methods of mathematics and machine-learned algorithms transform and analyze the data to produce clinically meaningful results.
  4. These results are available on a secure, web portal for physician interpretation and physician-patient consultation, which in combination with a patient’s medical history, risk factors, and symptoms, could be used by the interpreting physician to recommend further treatment.

The current diagnosis paradigm for significant CAD entails an escalating pathway of risk, time, and cost in exchange for better accuracy. Typically, a CAD diagnosis starts with a patient going to their doctor complaining of chest pain. The doctor will perform a physical examination and consider other factors like a patient’s medical history. From there, oftentimes a resting—meaning no cardiac stress—EKG is performed on patients considered ‘at-risk’. Under the physician’s discretion, a patient could then be sent for further testing using one or more tests, including nuclear stress testing, stress echocardiography, stress EKG, and CT angiography, before ultimately heading off to cardiac catheterization (coronary angiography or “cath lab”) for definitive diagnosis and treatment. We aim for CorVista to be a new, pre-cath lab cardiac imaging diagnostic with comparable accuracy to other functional tests, but without the radiation exposure, heart rate acceleration, and injections of contrast agents.

CorVista is an investigational device limited by federal law to investigational use. CorVista is not available for commercial distribution. It is currently undergoing a two-stage clinical study at 13 sites in the U.S. to support algorithm development and regulatory filings. The ongoing study, with more than 2,000 patients enrolled so far, will develop and measure the performance of a machine-learned algorithm for CAD detection to gold-standard cardiac catheterization results.

Medgadget: What hardware does your system rely on? What biosignals do you measure?

Don: We have developed a proprietary, hand-held digital device that we call the “Phase Signal Recorder (PSR) device”. This device scans a patient’s phase signals emitted from the chest cavity over 3.5 minutes while the patient is lying down using 7 sensors attached to the patient’s chest and back.

We capture an unfiltered phase signal that contains approximately 10 million data points. Specifically, the PSR device scans the unfiltered voltage gradient at a rate of 8,000Hz at each of six observation points on the patient for 210 seconds, time-synced to 10 quadrillionths of a second (10 femtoseconds), effectively capturing all sources of energy originating from the thorax (from intrinsic physiologic processes such as, but not limited to, electrical conduction, myocyte mechano-electric transduction feedback, responses to the autonomic nervous system, and peripheral resistance).

Beyond our proprietary PSR device, CorVista relies on an Internet-connected device (e.g., computer, phone, tablet, etc.), where a physician can access patient results on our secure, web portal.

Medgadget: Can you give us an understanding of what Phase Space Tomography is and how it’s used in your product?

Don: Phase space analysis is a well-known, advanced field of mathematics and physics used to model dynamic systems (such as the heart). We are pioneering phase space analysis in healthcare, using our proprietary approach to the field, Phase Space Tomography, a novel form of medical imaging.

Phase space analysis is currently used by the military for applications such as missile navigation and defense. In fact, our founder was working on phase-space-based technologies for the Royal Military College of Canada when he was inspired by its potential application in healthcare and more specifically, heart disease. He was utilizing a synchronous array of sensors to collect energy being emitted by missiles from radars to plot their trajectory. Just as militaries use phase space analysis to map out a missile’s course, we use Phase Space Tomography to measure and model cardiac signals with a synchronous array of sensors attached to the chest cavity.

After scanning a patient’s phase signals, the signal package is instantaneously transmitted to the cloud, where it is analyzed by a machine-learned algorithm to generate a unique Phase Space Tomographic image and a heart model indicating areas of potential heart disease (ischemia) associated with the presence of CAD. The results of the test are displayed on a secure, physician web portal that, in combination with a patient’s medical history, risk factors, and symptoms, can be used by the interpreting physician to recommend further treatment.

Medgadget: Your technology is designed for cardiac diagnostic applications. Do you expect that it can be applied to other fields of medicine as well?

Don: Yes, we believe that there are a number of diseases where organs are emitting energy that Phase Space Tomography and our AI platform could be applied, within cardiology (e.g., heart failure, etc.) and beyond. In fact, the brain emits even more energy than the heart! However, right now, we are 100% committed to bringing CorVista, our potentially game-changing cardiac imaging technology, to physicians and patients in need of better ways of assessing coronary artery disease (CAD), because it is the #1 cause of death worldwide.

Medgadget: Please tell us about the clinical study that is currently being conducted to evaluate your company’s technology.

Don: CorVista is currently undergoing a two-stage clinical study at 13 sites in the U.S. to support algorithm development and regulatory filings. The ongoing Coronary Artery Disease Learning and Algorithm Development (CADLAD) study already has more than 2,000 patients enrolled and will be used to develop and measure the performance of a machine-learned algorithm for CAD detection to gold-standard cardiac catheterization results. At TCT 2017, we will be presenting preliminary results from a 606-patient cohort as well as data on some of the most difficult-to-diagnose subpopulations: females (vs. males), obese, and elderly patients. In fact, these subpopulations were specifically highlighted in the FDA’s response to our pre-submission package earlier this year.

Medgadget: Given positive outcomes of this study, what are your next steps before seeking a marketing green light from the FDA?

Don: As auspicious as the preliminary CADLAD data that our principal investigator, Dr. Thomas Stuckey, presented at TCT 2017, we remain focused on completing enrollment in the study in November of this year and submitting our application to the FDA in the first half of 2018.

Medgadget: Would you tell us a bit about your background and how you came to be the President and CEO of Analytics 4 Life?

Don: I have over 25 years of medical device sales and marketing experience with positions of increasing responsibility at Medtronic, Guidant Corporation (now Boston Scientific), Ventritex, and Intermedics, including an international sales director role in Japan, where I was charge of a $100 million cardiovascular business. In 2008, I founded Sapheon Inc., a cardiovascular-focused medical device company, and led it to a $238 million acquisition by Covidien in 2014.

Interestingly, my appointment as President and CEO of Analytics 4 Life can ultimately be traced back to our capital raising strategy at Sapheon (and now at Analytics 4 Life). Sapheon was founded at the worst possible time—right in the depths of the Great Recession. It was very difficult for medtech startups to raise money from institutional investors, so we had to get creative, and that’s how we came upon targeting accredited investors. It worked splendidly, and investors were pleased. And coincidentally, that’s how I wound up at Analytics 4 Life; one of Sapheon’s investors was an early investor in Analytics 4 Life. After the Sapheon exit, he connected me with the team and the rest is history!

SOURCE: Medgadget

The richest families in America are pouring money into healthcare startups

Life sciences companies, especially those that make medical devices, are tapping funding outside of the venture capital community.

Family offices, which manage the money of very wealthy investors, are appealing backers because they can also consider the philanthropic aspect of funding healthcare technology.

Healthcare startups are taking a new tack in their efforts to find financial backers.

Instead of tapping venture capital backing — which comes with perks like a network of like-minded businesses, and board members experienced in taking companies public or selling them off — they’re tapping the super-rich. This other group, the founders say, has its own perks, including less meddling with how a business is run and, sometimes, a more philanthropic view of health-care investments.

These deals are wide-ranging in size and scope. Samumed — a $12 billion company with a pipeline of what could be revolutionary treatments to regenerate hair and bone — raised $220 million from anonymous high net worth individuals, for example. Smaller companies have also raised just a few million from family offices, which manage the assets of the wealthy.

The kinds of institutions that invest in these startups differ too. Some are highly sophisticated financial investors, like Pritzker Group, Flatley Ventures, and Stetson Family Office. Others were set up by former pharma executives, like PBM Capital Group. The Bill and Melinda Gates Foundation is a super-charged kind of version as well.

In some cases, family offices are looking to invest for the first time, driven perhaps by a personal experience with a specific illness.

In part, the money is available because there’s been a surge in multi-millionaires around the world, says Peter Meath, head of life sciences for Middle Market Banking at JPMorgan Chase Commercial Banking.

“This alone has spurred more investing in the space from family offices themselves,” he told Business Insider in an interview.

One challenge for family offices is developing the expertise and the network to make these kinds of investments, according to Meath. However, there are now organizations, including structured multifamily office funds that are helping the newbie health investors find prospects, he said.

“Billionaires are turning to networks of peers more than ever, finding common ground and addressing business issues as well as the biggest human problems like climate change and global health,” according to UBS/PwC’s Billionaires Report. “Increasingly, families are cooperating on new ventures and philanthropic causes.”

Finding a wealthy individual or family also means the biotech entrepreneur has to work harder, to build relationships that can help link up with the people. “They likely are funds that operate under the traditional Life Science investing ‘radar’,” he said.

The movement of family offices into life sciences comes around the same time as other funding moves “upstream,” to later rounds when companies are farther along in development, and there’s less capital for earlier stage companies.

Filling the gap left by venture capital

Venture capital funds anyway may be less interested in medical device companies because they don’t see a straightforward exit like they might from a company with a potential blockbuster drug, said Akhil Saklecha of Artiman Ventures, an early-stage venture firm based in Palo Alto, California. It might take a fund longer to see a return on their investment with a device maker than a drug developer.

Saklecha, who has invested in some medical device companies says, he’s going after big markets, such as hypertension or diabetes, which could make it easier to get the exits that VCs look for.

A medical device company with friends as investors

“Structured VC companies are not your first choice of partners if you have the choice,” said Don Crawford, the CEO of startup Analytics 4 Life. The company has developed a test that’s meant to replace “stress tests” and other measures used to figure out if a person should get a more invasive procedure to see if they have coronary artery disease.

The idea is that by using sensors to gather data, and artificial intelligence to analyze that information, doctors might be able to gather key information faster and with less risk than they’re able to do today. The company is still in clinical trials for the device. So far, the company has raised more than $32.5 million.

Crawford started a medical device company back in 2008 when it wasn’t easy to fundraise from venture capitalists, so he had to turn elsewhere. He tapped family offices, wealthy investors, people who came from the world of medical devices, physicians, and orthopedic surgeons — around the world.

Eventually, he sold the company in 2014, which paid off for his initial investors. From there, he was in a good position to help fund Analytics 4 Life.

“I started with Analytics 4 Life maybe four months later, but I had 300 friends with a quarter of a billion dollars,” Crawford told Business Insider.

And the choice to partner with these investors again was intentional.

“Family offices, a lot of times, they are investing, they want a return like everyone else, but they also have some other philanthropic reasons that align the interest of the family to certain areas,” he said.

Analytics 4 Life raised its latest $25 million round in September, giving them enough capital to get the device to approval in the US. The device has to go through large clinical trials, which are meant to vet whether the technology can work as well as other tests already available at detecting coronary artery disease.

SOURCE: Business Insider

Missile Detection Inspires Safer Cardiac Imaging Approach

Heart disease is the leading cause of death globally, with coronary artery disease (CAD) attributed to one in seven deaths in the United States.

CAD is a condition where major blood vessels that supply the heart with blood, oxygen, and other nutrients become damaged or diseased, as defined by The Mayo Clinic. A buildup of plaques or similar obstructions will decrease blood flow to the heart, potentially leading to eventual chest pain or shortness of breath, and a heart attack down the road.

The disease can be treated, but has no cure.

Physicians diagnosing this condition typically rely on a number of tests to gain insight into what is happening inside a patient’s heart.

The first step doctors take may include routine blood tests, collecting previous medical history, and performing routine blood tests.

For some patients, more comprehensive examinations could follow this first step, including a nuclear stress test where doctors measure blood flow to the heart at rest and during stress. A tracer gets injected into the bloodstream and special cameras monitor areas in the heart that receive less blood flow.

An angiogram is another viable option. In this case, physicians inject a specialized dye into coronary arteries to illuminate blockages that appear on an X-ray.

These diagnostics have been used for years. However, they can result in harmful side-effects (i.e. being exposed to radiation and other foreign chemicals) and are also expensive and time-consuming.

Issues like this are why startup Analytics 4 Life (A4L) developed a CorVista, a specialized device that offers a cheaper, safer alternative to cardiac imaging.

An unexpected origin

The Toronto, Canada-based company was founded in 2012 by Sunny Gupta, who is a bio-electrical engineer by training.

Gupta has held various position with IBM and Microsoft, but the idea for A4L and CorVista came during his time working on a defense contract with the Canadian Military.

He was tasked with researching ways to analyze certain signals that could improve the ability of radar array systems to pinpoint launched missiles, while also establishing a mathematical model capable of intercepting them at 1,000 miles above the earth going at 2,000 miles an hour.

Gupta then felt that he could apply the same techniques to look at biological signals that could yield signs of disease.

This work spawned the invention of CorVista.

“From a digital health standpoint, what I believe is interesting that we are… [a unique]… combination of a medical device and big data analytics,” said A4L CEO Don Crawford in an interview with R&D Magazine.

CorVista is comprised of a four-part system. The first component is a proprietary recording device built with an array of seven sensors. It is attached to the patient where it scans the body for a three-minute period, searching for certain signals that get recorded.

“What makes our approach different than everybody else’s is that we’re using the organic energy as produced by the body so we only have the collectors. Other imaging techniques usually put some sort of energy into the body where almost every one of them have some sort of collector that just senses or detects the energy that bounces back,” continued Crawford.

Next, that three-minute recording becomes a complete signal package that gets instantaneously transmitted to a cloud computing receptacle.

A specialized machine-learning algorithm then gets to work sifting through these signals to produce a unique image and heart model, which highlights areas of disease associated with CAD. When a major artery gets blocked, it is because the heart muscle becomes ischemic because it’s not getting the blood and oxygen to work as it should.

CorVista relies on data obtained from angiograms to help determine which arteries are blocked and which are not so physicians can determine which regions of the heart are diseased and which are healthy.

The final image gets sent to a physician portal where Crawford emphasis that the system provides “predictive analysis” to help doctors make these decisions, considering factors like medical history, risk factors, and other symptoms to deliver a complete diagnosis.

Safety and convenience factors

Currently, there is a two-stage clinical trial occurring at 13 sites across the United States, designed to support further development of the device as well as prepare it for anticipated regulatory filings.

One of the ongoing examinations has over 2,000 patients enrolled so far where it will measure the performance of a machine-learned algorithm for CAD detection to the gold-standard cardiac catheterization results.

Each patient participating in this study will serve as his or her own control while also being at rest.

“In doing some of the early work in the clinical trial, we were already designated as a non-significant risk of the study because we are not injecting energy into the patient and they’re not getting radiation exposure,” said Crawford.

The end result of the trial will also help the company refine its algorithm with the overall goal of showing that this device can become a non-invasive, safe and affordable alternative for diagnosing this cardiac condition.

Being able to measure some of the metrics related to heart failure is viewed as one of the other indications CorVista could target as it progresses down the development pipeline.

“There’s a lot of energy that is produced by the heart, but there’s even more energy produced in the brain,” noted Crawford.

Analyzing brain conditions is another area the company could target, but Crawford elaborated that developers would require different kind of sensor sets to be installed in a headband to record these signals versus electrodes placed on the chest.

A4L expects to have completed enrollment in its clinical trial before the end of the year with the expectation it will submit an application the U.S. Food and Drug Administration in the first quarter of 2018.

The company recently completed a Series B financing where it raised $25 million.

SOURCE: R&D Magazine

Analytics 4 Life Raises $25 Million for AI-Backed Cardiac Imaging Technology

Funding will advance proprietary imaging technology designed to assess coronary artery disease with a machine learning algorithm

October 20, 2017 — Digital health company Analytics 4 Life announced it has completed a $25 million Series B financing. This financing event was supported by an international syndicate of accredited investors, including physicians, healthcare professionals and medical device experts. The company’s novel cardiac imaging technology is under clinical investigation to help physicians assess the presence of coronary artery disease (CAD) using intrinsic signals scanned from the body without radiation, contrast agents or cardiac stress.

Current diagnostic methods for CAD are costly, risky and time-consuming. Analytics 4 Life seeks to address these challenges with its novel method of cardiac imaging. The company’s technology represents a new approach to cardiac imaging based on advanced disciplines of mathematics and physics combined with the power of cloud computing and artificial intelligence. The first application of this technology is CorVista, a non-invasive, physician-directed diagnostic test that aims to identify the presence of CAD without radiation or cardiac stress.

CorVista is designed to scan signals naturally emitted by the body with a synchronous array of seven sensors on Analytics 4 Life’s proprietary collection device. After the signals are acquired, the signal package is instantaneously transmitted to the cloud where it is analyzed by a machine-learned algorithm to generate a unique image and a heart model indicating areas of potential heart disease associated with the presence of CAD. The results of the test are displayed on a secure physician web portal that, in combination with a patient’s medical history, risk factors and symptoms, are used by the interpreting physician to recommend further treatment.

A two-stage clinical trial at 13 sites in the United States is currently underway to support CorVista’s development and regulatory filings. The ongoing study, with more than 2,000 patients enrolled so far, will measure the performance of a machine-learned algorithm for CAD detection to gold-standard cardiac catheterization results.

Preliminary results will be presented at the 2017 Transcatheter Cardiovascular Therapeutics (TCT) conference, Oct. 29-Nov. 2 in Denver. Assuming a positive U.S. Food and Drug Administration (FDA) review, the company anticipates having CorVista available in the U.S. next year.

SOURCE: Diagnostic and Interventional Cardiology Magazine

Analytics 4 Life lands $25m for AI-backed cardiac imaging tech

Analytics 4 Life said today that it landed $25 million in a Series B financing round. A group of investors, including doctors and medical device experts, contributed to the round.

The digital health company’s cardiac imaging tech is designed to help physicians assess the presence of coronary artery disease using signals from the body – without the use of radiation or contrast agents.

The company’s first application of its technology is CorVista, a non-invasive diagnostic test that uses an array of sensors to scan signals naturally given from the body. When the sensors are finished collecting data, the signal package is transmitted to the cloud. There, it is analyzed by a machine-learning algorithm which generates a unique image and model of the heart, pointing towards areas of potential heart disease.

The results of the test are presented on a physician web portal.

“Heart disease is the leading cause of death globally, with 1 in 7 deaths in the U.S. attributed to CAD. We are thankful for the continued support from passionate investors who have made it possible for us to revolutionize the way that CAD is diagnosed,” CEO Don Crawford said in prepared remarks. “Securing this oversubscribed financing fuels our rapid growth to advance development of our diagnostic tool and gives us the resources we need to deliver this game-changing technology to patients and physicians.”

The CorVista test is being evaluated in an on-going, two-stage clinical trial with more than 2,000 patients, the company reported. The study plans to compare the performance of a machine-learned algorithm for CAD detection with cardiac catheterization results.

The company expects to present preliminary results from the trial next month at the Transcatheter Cardiovascular Therapeutics conference in Denver.

Analytics 4 Life also said today that Dr. Aaron Berez, founder & CEO of Alembic LLC, joined the company’s board of directors.

“Today marks an important milestone for the company, but also for the 15.5 million Americans living with coronary heart disease,” Berez said. “I’m excited and honored to join Analytics 4 Life’s Board of esteemed experts as we chart a path toward bringing this potentially transformative way to diagnose cardiovascular disease to patients and physicians.”

SOURCE: MassDevice

Artificial intelligence health care company with RTP roots raises $25M

Analytics 4 Life – a medical device company that is based in Toronto but houses its U.S. headquarters in Research Triangle Park – has raised $25 million in a Series B financing.

The company is focused on using artificial intelligence to develop solutions for improving health care. Its first device, CorVista, scans signals that the human body naturally emits in order to identify coronary artery disease, a leading cause of death in the U.S.

President and CEO Don Crawford says the latest financing will be used to fund remaining clinical work before the end of the year, a U.S. Food and Drug Administration submission for its device and introduction of the device to the U.S. market, expected as early as next year. To date, Analytics 4 Life has raised about $35 million, including about $7 million last year. Crawford previously founded and led another Triangle-based medical device company, Sapheon, until its $238 million acquisition by Covidien in 2014.

Analytics 4 Life was founded back in 2012 by Chief Scientific Officer Sunny Gupta. Crawford joined Analytics 4 Life in 2015, shortly after his Sapheon exit.

While a number of investors in Sapheon have now also invested in Analytics 4 Life, according to Crawford, upward of 50 percent are new investors. The company’s investors include those from the medical device field and physicians as well as family offices based in the U.S., China and Europe.

Analytics 4 Life currently has 25 full-time employees, Crawford says, with about half based in the Triangle and the remainder in Toronto. While Toronto is a hub for artificial intelligence, he says, the company’s executive and clinical management teams are based locally. The company also works with a contract manufacturer in Wake Forest, he says.

“The majority of the headcount increase [going forward] will happen in the Research Triangle,” Crawford says, in areas like sales and marketing as well as customer service and management as the company prepares for the launch of its first device. The potential diagnostics market in the U.S. tops $5 billion, and the company will be looking to Europe and Asia down the line as well.

SOURCE: Triangle Business Journal