This startup says its AI can better spot a healthy embryo — and improve IVF success

With every year, AI is beginning to bring more standardized levels of diagnostic accuracy in medicine. This is true of skin cancer detection, for example, and lung cancers.

Now, a startup in Israel called Embryonics says its AI can improve the odds of successfully implanting a healthy embryo during in vitro fertilization. What the company has been developing, in essence, is an algorithm to predict embryo implantation probability, which they have trained through IVF time-lapsed imaging of developing embryos.

It’s just getting started, to be clear. So far, in a pilot involving 11 women ranging in age from 20 to 40, six of those individuals are enjoying successful pregnancies, and the other five are awaiting results, says Embryonics.

It’s a big business to be chasing. The global in-vitro fertilization market is expected to grow from roughly $18.3 billion to nearly double that number in the next five years by some estimates.

But Embryonics is more interesting for its potential to shake up the status quo, wherein tens of thousands of women who undergo IVF each year face costs of anywhere from $10,000 to $15,000 per cycle (at least in the U.S.), along with long-shot odds that grow worse as a woman ages.

Indeed, it’s reducing the number of IVF rounds, and attendant expenses, that drives Embryonics, which was founded three years ago by CEO Yael Gold-Zamir, an M.D. who studied general surgery at Hebrew University, yet became a researcher in an IVF laboratory owing to an abiding interest in the science behind fertility.

As it happens, she would be introduced to two individuals with complementary interests and expertise. One of them was David Silver, who had studied bioinformatics at the prestigious Technion-Israel Institute of Technology and who, before joining Embryonics last year, spent three years as a machine learning engineer at Apple and three years before that as an algorithm engineer at Intel.

The second individual to whom Gold-Zamir was introduced was Alex Bronstein, a serial founder who spent years as a principal engineer with Intel and who is today the head of the Center for Intelligent Systems at Technion as well as involved with several efforts involving deep learning AI, including at Embryonics and at Sibylla AI, a nascent outfit focused on algorithmic trading in capital markets.

It’s a small outfit, in short, but the three, along with Embryonics’s now 13 other full-time employees, appear to be making progress.

Fueled in part by $4 million in seed funding led by the Shuctermann Family Investment Office (led by the former president of Soros Capital, Sender Cohen) and the Israeli Innovation Authority, the company says it’s about to receive regulatory approval in Europe that will enable it to sell its software — which the team says can recognize patterns and interpret image in small cell clusters with greater accuracy than a human —  to fertility clinics across the continent.

Using a database with millions of (anonymized) patient records from different centers around the world that representing all races and geographies and ages, says Gold-Zamir, the company is already eyeing next steps, too.

Most notably, beyond analyzing which of several embryos ismost likely to thrive, for example, Embryonics wants to work with fertility clinics on improving what’s called hormonal stimulation, so that their patients produce as many mature eggs as possible.

As Bronstein explains it, every woman who goes through IVF or fertility preservation goes through this hormonal stimulation process — which involves getting injected with hormones from 8 to 14 days — to induce their ovaries to produce numerous eggs. But right now, there are three general protocols and  a “lot of trial and error in trying to establish the right one,” he says. Though deep learning, Embryonics thinks it can begin to understand not just which hormones each individual should be taking but the different times they should be taken.

In addition to embryo selection, Embryonics has also have developed a non-invasive genetic test based on analysis of visual information, together with clinical data, that in some cases can detect major chromosomal aberrations like down syndrome, says Gold-Zamir.

And there’s more in the works. “Embryonics’s goal is to provide a holistic solution, covering all aspects of the process,” says Gold-Zamir, who notes that she is raising four children of her own, along with running the company.

It’s too soon to say whether the nascent outfit will succeed, naturally. But it certainly seems to be at the forefront of a technology that is fast changing after more than 40 years wherein many IVF clinics worldwide have simply assessed embryo health by looking at days-old embryos on a petri dish under a microscope to assess their cell multiplication and shape.

In 2019, for instance, investigators from Weill Cornell Medicine in New York City published own their conclusion  that AI can evaluate embryo morphology more accurately than the human eye after using 12,000 photos of human embryos taken precisely 110 hours after fertilization to train an algorithm to discriminate between poor and good embryo quality.

The investigators said that each embryo was first assigned a grade by embryologists that considered various aspects of the embryo’s appearance. The investigators then performed a statistical analysis to correlate the embryo grade with the probability of a successful pregnancy outcome. Embryos were considered good quality if the chances were greater than 58 percent and poor quality if the chances were below 35%.

After training and validation, the algorithm was able to classify the quality of a new set of images with 97% accuracy.

Photo Credit: Tammy Bar-Shay

This startup says its AI can better spot a healthy embryo — and improve IVF success

With every year, AI is beginning to bring more standardized levels of diagnostic accuracy in medicine. This is true of skin cancer detection, for example, and lung cancers.

Now, a startup in Israel called Embryonics says its AI can improve the odds of successfully implanting a healthy embryo during in vitro fertilization. What the company has been developing, in essence, is an algorithm to predict embryo implantation probability, which they have trained through IVF time-lapsed imaging of developing embryos.

It’s just getting started, to be clear. So far, in a pilot involving 11 women ranging in age from 20 to 40, six of those individuals are enjoying successful pregnancies, and the other five are awaiting results, says Embryonics.

It’s a big business to be chasing. The global in-vitro fertilization market is expected to grow from roughly $18.3 billion to nearly double that number in the next five years by some estimates.

But Embryonics is more interesting for its potential to shake up the status quo, wherein tens of thousands of women who undergo IVF each year face costs of anywhere from $10,000 to $15,000 per cycle (at least in the U.S.), along with long-shot odds that grow worse as a woman ages.

Indeed, it’s reducing the number of IVF rounds, and attendant expenses, that drives Embryonics, which was founded three years ago by CEO Yael Gold-Zamir, an M.D. who studied general surgery at Hebrew University, yet became a researcher in an IVF laboratory owing to an abiding interest in the science behind fertility.

As it happens, she would be introduced to two individuals with complementary interests and expertise. One of them was David Silver, who had studied bioinformatics at the prestigious Technion-Israel Institute of Technology and who, before joining Embryonics last year, spent three years as a machine learning engineer at Apple and three years before that as an algorithm engineer at Intel.

The second individual to whom Gold-Zamir was introduced was Alex Bronstein, a serial founder who spent years as a principal engineer with Intel and who is today the head of the Center for Intelligent Systems at Technion as well as involved with several efforts involving deep learning AI, including at Embryonics and at Sibylla AI, a nascent outfit focused on algorithmic trading in capital markets.

It’s a small outfit, in short, but the three, along with Embryonics’s now 13 other full-time employees, appear to be making progress.

Fueled in part by $4 million in seed funding led by the Shuctermann Family Investment Office (led by the former president of Soros Capital, Sender Cohen) and the Israeli Innovation Authority, the company says it’s about to receive regulatory approval in Europe that will enable it to sell its software — which the team says can recognize patterns and interpret image in small cell clusters with greater accuracy than a human —  to fertility clinics across the continent.

Using a database with millions of (anonymized) patient records from different centers around the world that representing all races and geographies and ages, says Gold-Zamir, the company is already eyeing next steps, too.

Most notably, beyond analyzing which of several embryos ismost likely to thrive, for example, Embryonics wants to work with fertility clinics on improving what’s called hormonal stimulation, so that their patients produce as many mature eggs as possible.

As Bronstein explains it, every woman who goes through IVF or fertility preservation goes through this hormonal stimulation process — which involves getting injected with hormones from 8 to 14 days — to induce their ovaries to produce numerous eggs. But right now, there are three general protocols and  a “lot of trial and error in trying to establish the right one,” he says. Though deep learning, Embryonics thinks it can begin to understand not just which hormones each individual should be taking but the different times they should be taken.

In addition to embryo selection, Embryonics has also have developed a non-invasive genetic test based on analysis of visual information, together with clinical data, that in some cases can detect major chromosomal aberrations like down syndrome, says Gold-Zamir.

And there’s more in the works. “Embryonics’s goal is to provide a holistic solution, covering all aspects of the process,” says Gold-Zamir, who notes that she is raising four children of her own, along with running the company.

It’s too soon to say whether the nascent outfit will succeed, naturally. But it certainly seems to be at the forefront of a technology that is fast changing after more than 40 years wherein many IVF clinics worldwide have simply assessed embryo health by looking at days-old embryos on a petri dish under a microscope to assess their cell multiplication and shape.

In 2019, for instance, investigators from Weill Cornell Medicine in New York City published own their conclusion  that AI can evaluate embryo morphology more accurately than the human eye after using 12,000 photos of human embryos taken precisely 110 hours after fertilization to train an algorithm to discriminate between poor and good embryo quality.

The investigators said that each embryo was first assigned a grade by embryologists that considered various aspects of the embryo’s appearance. The investigators then performed a statistical analysis to correlate the embryo grade with the probability of a successful pregnancy outcome. Embryos were considered good quality if the chances were greater than 58 percent and poor quality if the chances were below 35%.

After training and validation, the algorithm was able to classify the quality of a new set of images with 97% accuracy.

Photo Credit: Tammy Bar-Shay

Deci raises $9.1M to optimize AI models with AI

Deci, a Tel Aviv-based startup that is building a new platform that uses AI to optimized AI models and get them ready for production, today announced that it has raised a $9.1 million seed round led by Emerge and Square Peg.

The general idea here is to make it easier and faster for businesses to take AI workloads into production — and to optimize those production models for improved accuracy and performance. To enable this, the company built an end-to-end solution that allows engineers to bring in their pre-trained models and then have Deci manage, benchmark and optimize them before they package them up for deployment. Using its runtime container or Edge SDK, Deci users can also then serve those models on virtually any modern platform and cloud.

Deci’s insights screen combines all indicators of a deep learning model’s expected behavior in production, resulting in the Deci Score – a single metric summarizing the overall performance of the model.

The company was co-founded by co-founded by deep learning scientist Yonatan Geifman, technology entrepreneur Jonathan Elial, and professor Ran El-Yaniv, a computer scientist and machine learning expert at the Technion – Israel Institute of Technology.

“Deci is leading a paradigm shift in AI to empower data scientists and deep learning engineers with the tools needed to create and deploy effective and powerful solutions,” says Yonatan Geifman, CEO and co-founder of Deci. “The rapidly increasing complexity and diversity of neural network models make it hard for companies to achieve top performance. We realized that the optimal strategy is to harness the AI itself to tackle this challenge. Using AI, Deci’s goal is to help every AI practitioner to solve the world’s most complex problems.”

Deci’s lab screen enables users to manage their deep learning models’ lifecycles, optimize inference performance, and prepare models for deployment. Image Credits: Deci

The company promises is that, on the same hardware and with comparable accuracy, Deci-optimized models will run between five and ten times faster than before. It can make use of CPUs and GPUs for running its inference workloads and the company says that it is already working with customers in autonomous driving, manufacturing, communication and healthcare, among others.

“Deci‘s ability to automatically craft top-performing deep learning solutions is a paradigm shift in artificial intelligence and unlocks new opportunities for many businesses across different industries,” said Liad Rubin, Partner at Emerge. “We are proud to have partnered with such incredible founders and be part of Deci’s journey from day one.”

 

The rise of the new crypto “mafias”

In the early 2000s, journalists popularized the term “PayPal mafia” to describe the PayPal founders and employees who left to start their own wildly successful tech companies, including Peter Thiel, Reid Hoffman, and Elon Musk. Drawing from that idea, this article seeks to cover the formation and flow of talent within the crypto landscape today.

The crypto world is in a constant state of flux, with new startups entrants joining the industry every single day. These new startups have the potential either to be superstars within a portfolio company or to start the next Coinbase. Additionally, there are already impressive spin-outs from some of the more established crypto companies.

For ease of framing, I’ve separated these early-forming mafias into four categories: CryptoTechWall Street, and Academia. Since 2009, there have been 186 spinout companies originating from those four categories (33% from Academia, 28% from Crypto, 24% from Tech, and 15% from Wall Street).

crypto mafias

Obvious but important disclaimer: this article does not intend to promote organized crime within crypto.

Criteria

Regulus Cyber launches with a technology to secure autonomous vehicles

Over the next twenty years the autonomous vehicle market is expected to grow into a $700 billion industry as robots take over nearly every aspect of mobility.

One of the key arguments for this shift away from manually operated machines is that they offer greater safety thanks to less risk of human error. But as these autonomous vehicles proliferate, there needs to be a way to ensure that these systems aren’t exposed to the same kinds of hacking threats that have bedeviled the tech industry since its creation.

It’s the rationale behind Regulus Cyber, a new Israeli security technology developer founded by Yonatan Zunger and Yoav Zangvil — two longtime professionals from Israel’s aerospace and defense industry.

“We’re building a system that is looking at different sensors and the first system is GPS,” Zunger says. Using a proprietary array of off-the-shelf antennas and software developed internally, the system Regulus has designed can determine whether a GPS signal is legitimate or has been spoofed by a hacker (think of it as a way to defend against the kind of hack that the bad guys in “Die Hard 2“).

 

Zunger first had the idea to launch the company three years ago while he was working with drones at the Israeli technology firm, Elbit. At the time, militaries were beginning to develop technologies to combat drone operations and Zunger figured it was only a matter of time before those technologies made their way into the commercial drone market as well.

While the technology works for unmanned aerial vehicles, it also has applications for pretty much any type of autonomous transportation technology.

Backing the company are a clutch of well-known Israeli and American investors including Sierra Ventures, Canaan Partners Israel, Technion, and F2 Capital.

Regulus, which raised $6.3 million in financing before emerging from stealth, said that the money will be used to expand its sales and marketing efforts and to continue to develop its technology.

The company’s first two products are a spoofing protection module that integrates with any autonomous vehicle; and a communication security manager that protects against hacking and misdirection.

“We are very excited to lead this round of financing. Sensors security for autonomous machines will become as important as processors security. Regulus identified the key vulnerabilities and developed the best-in-class solutions,” said Ben Yu, a managing director of Sierra Ventures, in a statement. “Having been working with the company since seed funding, Sierra invested with strong confidence in the team to build Regulus into the category leader.”