Dr. Hadas Ziso of EndoCure
episode
Israeli Technology Founders Speak: Conversations with Successful Israeli Hitech and Biotech Entrepreneurs
19 min
1 speaker
5 chapters
transcribed 15 days ago
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What is the main topic discussed in this episode?
This is Israeli Technology Founders Speak, a podcast of conversations with successful Israeli high tech and biotech entrepreneurs. With your host, Avraham Hermon.
Dr. Hadas Ziso is the co-founder and CEO of Endocure, an Israeli startup that uses AI to automate ultrasound imaging, turning any manual ultrasound machine into an automated tool with 10 times higher density than MRI. Their innovation removes the need for a trained operator, making reliable imaging accessible to anyone anywhere. Avraham sat down with Hadas to discuss how she came up with the idea, got funded, why she stays in Israel, future goals, tips for tech founders, and much more. This podcast is a creation of JMB Davis Ben David, an intellectual property law firm serving clients around the world. You have great innovations. We keep them safe. It's not enough to just have a great startup idea or innovation.
If you don't legally protect your innovations, products, and brand, anyone can claim them as their own. We keep your great innovations secure. Learn more by going to jmbdavis.com. That's jmb d A V I S dot com. Thank you. I'm here with uh doctor Hadas Ziso in her offices in Haifa Israel, the offices of the company EndoCure. What problem does EndoCure solve?
Endocure is transforming any off the shelf ultrasound machine from being very operator dependent and manual with a range of error from missing a condition to inventing one into three D Operator agnostic high resolution imaging tool with ten times higher density than MRI. And by doing so, we are separating data acquisition from interpretation, mimicking what's happening today in MRI and CD machines. Making the process standardized so that any technician can run the system and get the same results anywhere in the world.
That's interesting. Ultrasound, I guess, for a lot of layman like me, is something, you know, we had an experience of going to the doctor, you know, with a pregnancy and seeing an ultrasound and wondering how does the ultrasound technician see what they see? And all we see is like a black and white image that's moving around a little bit, grainy, hard to define, hard to understand what it is. And there's this person there that's somehow decoding. coding and saying, Oh look, it's a boy, congratulations, or something like that. Yeah. So is your machine going to get rid of some of that? How is it going to work?
You are right, it you touched a very important thing about ultrasound machines. It's a de very difficult task to interpretate in real time and that's what the sonographers have to do today. They have to figure out what they see as they move over the skin. And for that they are trained for years and it's really sensitive to their level of expertise of the operator. So ultrasound is definitely capable of seeing pathologies like cancer, endometriosis, kidney stones, and other conditions. Yet it is very, very difficult to catch in this type of real-time imaging tool. So we are not Changing anything about the ultrasound machine, we are just changing the way it is being used. We are upgrading the existing by reducing the level of expertise that is required.
Okay. See sounds like a great idea. The question is why why hasn't this happened until today? You know, how why how are you the first to do this?
There have been attempts from various uh solutions uh like AI. We hear a lot about AI in imaging and it's working very well for City and MRI because you have a standardized set of data there. Uh reproducible data no matter where you took it. But again in the ultrasound world the data set is very subjective. So you don't have the good data to train the AI with and that's where we come. We provide This standardized data to train the AI on. So that is one reason that AI is not sufficient. Other attempts for 3D ultrasound imaging has been tried before. There are vaginal 3D ultrasound probes but not for abdominal scans and not like ours where we minimize the dependency on the human skill.
Mm-hmm. So you what your machine is doing is essentially standardizing the way that the test is done and then you can have data that is more workable to be able to operate I AI on.
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Chapters
5 chapters
1
What is the main topic discussed in this episode?
0:00–8:24
2
What problem does EndoCure’s AI‑powered ultrasound aim to solve?
8:24–12:06
3
How does EndoCure’s technology make ultrasound operator‑agnostic and higher‑resolution?
12:06–15:20
4
Why is AI training data a challenge for ultrasound and how did EndoCure overcome it?
15:20–18:57
5
What multidisciplinary expertise is required to build EndoCure’s robotic platform?
18:57–19:45