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Expert Series

10 Questions with….. Ivana Išgum

By | Expert Series, News

Ivana Išgum is the University Professor of AI and Medical Imaging at the Amsterdam University Medical Center.  Graduating in Mathematics from the University of Zagreb in 1999, Išgum became a PhD student in the Netherlands in 2001 at the Image Sciences Institute. Obtaining her PhD degree in 2007, titled ‘Computer-aided detection and quantification of arterial calcifications with CT,’ Ivana then worked at both Leiden University Medical Center and UMC Utrecht, before embarking upon her position at the AMC.

Why do you do what you do?

Growing up in Croatia, both my mother and father were engineers. I remember being taken to my father’s work as a little girl and finding the whole experience and atmosphere so exciting. I knew I wanted to follow in my parents’ footsteps. I  came to the Netherlands 20 years ago as part of my journey to pursue that goal and never left.

Studying mathematics was quite abstract for me. I think that math is everywhere around us but we’re often not aware of it. It is fulfilling for me to connect knowledge with something that is applicable and visible in everyday life. So, I wanted to do something medical.  I wanted to strive for an academic career and surround myself with inspiring people. Of course getting funding for the research we want to do can be a struggle, but ultimately my job comes with the capacity to work on what I wish to work on, there is such creative freedom.  

How has AI proved so transformative to medical imaging? 

It has been the appearance of deep learning and the ability of algorithms to now be able to learn from the data themselves that has boosted the field enormously. Whilst AI is everywhere and indeed everyone is excited about it, for some this may appear as ‘hype’, but I really try to avoid using the word ‘hype’.  It implies that after a few years AI will somehow go away. That’s not the case, I am convinced that AI is here to stay, it will develop further, and it will radically change the way we work. 

Unlike in other industries, in the field of medicine we seem to not be as far as we could be. The main reason for this is probably accessibility of data. AI requires large sets of data that represents different populations. Furthermore, responsible application of AI in clinic while adhering to privacy legislation and ethical standards is far from trivial.   We are actively researching these issues so that the medical field can benefit from current AI much more than it’s already doing.

In a broad sense, how can AI currently benefit healthcare systems? 

Current healthcare systems are not sustainable as they are. The number of medical experts is limited and AI can help relieve their burden. AI might save costs, but it will more likely allow for a more sustainable system and it will certainly enable expert healthcare to be brought to parts of the world where there is a shortage of expertise. 

Where does reservation around AI stem from, and how can it best be addressed? 

One of the fears of AI comes from the caution of un-transparent decision-making. There are big, important and needed conversations currently going on about this. As experts, we do have to look at causality and make sure we validate thoroughly. I think the general public have a fear of AI being depersonalised but I don’t think it necessarily is, or should be. 

Are mindsets becoming more confident in AI powered tools in clinical practice? How does that feel for you at the forefront of research?

More people are seeing the potential benefits of AI to the future of healthcare, and resistance is decreasing. But  people generally resist change, if you look back at history it’s always been the way. Five years ago, people were panicking that radiologists would be replaced, and therefore not trained and that we’d see the takeover of robots. Now that hasn’t happened and undoubtedly never will. The medical world moves slowly and AI brings added value to routine work that radiologists can partner with and check. Ultimately AI makes it faster, more reproducible and cheaper than clinicians doing it all themselves, and that creates impact. 

Physicians understand AI in medical care is here to stay and people want to ride the wave. It opens the door for so many opportunities and for us who have been in the field for a long time, it’s rewarding. 

Where are we currently in terms of the potential of AI being utilised in clinical practice and what are the key challenges when it comes to implementation? 

From a research perspective, you see that a lot is possible. I would say the technology to answer many questions is available. But if you look at the products, the diversity doesn’t match up. For example the number of AI powered radiology tools used on a large clinical scale, is not very many, if at all. So the translation from research to clinical practice is really important. Regulatory bodies such as the FDA for example, are changing their strategy quite considerably in order to keep up with regulations that enable these tools to come into use.

Careful implementation is crucial. We cannot make mistakes. It’s important to consider all aspects of AI application including associated ethical and legal issues.  We are working with an extremely powerful tool and sensitive data; there is a lot to be discovered. Whilst we want to utilise AI to find information which is important to be found, we do not want to generate investigations which are not needed. These are all issues that are fundamental as we explore the possibilities of translating research into clinical practice. 

What do you think are the main technical challenges related to applying AI to medical imaging in comparison to using AI in other fields?

Medical image data is large and high dimensional, often 4D and 5D. At the same time, there are small sets of labelled representative data to learn from and the labels are often noisy. This can be challenging for analysis. Furthermore, data is quite diverse regarding image acquisition protocols, variability in anatomy and pathology. Designing methods that are robust to all these is not straightforward.

After the introduction of deep learning, there was a boom of papers applying similar techniques to different domains. Has the rate of progress slowed down or are there new interesting challenges that (if solved) could represent a major milestone in the field of medical imaging processing?

I am not sure whether the rate has been slowing down. We see that methodological developments from one research domain are being adjusted for application in others, which is good. However, we also see enormous competition in publishing where outperforming previous work, even if very slightly, is expected. While improving on the performance is possibly important and relevant, I think it is even more important that these are reproducible and that we try to understand why some approaches work better than others. 

What’s next in terms of key medical areas to benefit from AI?

It’s such an exciting time, we’re at the tip of what’s possible. There are very many possible applications of AI in medicine.Not only analysis of medical images but  the merging of different types of information can be very impactful and could help deeper understanding greatly.

As a successful woman in the field of AI, you must get asked about gender dynamics a lot, do you feel this question is still useful? 

Absolutely, it’s an incredibly important but sensitive issue. I dislike when people say it’s not a problem or that it’s not a problem for me any more. That is just not true. Ultimately we do not have enough women in science. As a society we are educating women but missing out on benefiting from their talent. Why? It’s such a complicated myriad of things at play (such as PhD and PostDoc timing coinciding with starting families, judgement in the workplace, at home, in the school playground etc) that ultimately work together to prevent society not utilising the full capacity of experts trained in science. 

Diversity is key for benefiting science. Some people say we need to encourage women, I think it would be a start if we didn’t discourage them.

Nico.lab improves clinical practice with innovative and trusted AI. Our product StrokeViewer, is an AI-powered clinical decision support system, offering a complete assessment of relevant imaging biomarkers within 3 minutes. 

10 Questions with….. Wouter de Monyé

By | Expert Series, News

Dr. Wouter de Monyé is the Medical Director of Radiology at Spaarne Gasthuis. Having graduated from the University of Utrecht in 1996, de Monyé went on to carry out research at Leiden University Medical Center and trained as a radiologist at Leiden University Medical Center and Royal Liverpool University Hospital.

Why do you do what you do? 

My motto first and foremost is to make people better. It’s a broad ambition but importantly it’s not just patients, but people, I strive to better. That’s why I love to teach and train within the hospital. I want to help my fellow medical colleagues come to patient diagnoses faster and more efficiently. 

What was your first job?

Funnily enough, cleaning a hospital when I was 15 years old.  My mother was a nurse and set me up waxing those long corridors. As a child I wanted to be a pilot, but this soon developed into the desire to become a flying doctor. You need to become a doctor first and the aviation part comes later. In the process, I discovered that radiology was my calling. I built computers on the side for a bit of extra money during my studies, but soon had to give this up when I started my research at Leiden University. 

What has been the highlight of your career so far? 

Becoming the Medical Director of my department. This has allowed me to influence systems and process and improve the circumstances we work in as doctors. At some point you think you’re doing your job and you’re being a professional, but ultimately it’s the same process time and time again. If you manage to get a helicopter view, you can see structural changes that could benefit not only one person, but a cohort of patients. I find being able to influence such change extremely rewarding. 

What is the most challenging aspect of your job? 

Getting the diagnosis right, particularly in acute situations. As a radiologist, you need to be able to ascertain what’s normal and abnormal and stick to it. But you also need to be able to know when to convey doubt. That’s the tricky part, especially when you don’t have time for a colleague to have a second look. Sometimes doctors put you on the spot – is it this or is it this? You find yourself having to give black or white answers in situations which are often pretty grey. 

Do you have any coping mechanisms for dealing with the stress of your profession?

That’s difficult to answer. I guess realising life can be over in the blink of an eyelid makes you focus on trying to build its most important elements,  like good relationships and friendships. 

Why is timely diagnosis so urgent in stroke?

The challenge to get things right is critical. Stroke is pretty common and its impact can be huge – everlasting, for the rest of a patient’s life. Quick diagnosis is key, but more and more research suggests even in cases with a larger diagnosis time interval, there is still benefit from intra-arterial thrombolysis. So having been proven in the Mr Clean trial that in acute situations this type of treatment option works, I think we’re now working towards expanding this approach to more patients. We’ll be seeing more situations whereby a possible clot has a chance to be treated in this novel way. I think the Mr Clean trial gets more relevant every year. 

Are there any challenges in the current stroke workflow? 

Getting the knowledge to the image, or the image to the knowledge. Doing the actual scans is no longer a problem. These days the CT scanners are able to produce high quality images in a very short amount of time. The challenge is interpretation. Those many images need to be analysed by an expert. Such professionals (ideally a neuroradiologist) are not always on call and a patient’s scan will often have to be interpreted by colleagues with less professional experience. 

Why is improving image sharing so vital? 

A waste of time is a waste of brain. If we can get an image to an expert as quickly as possible, that can be life altering for a patient. If a colleague needs help with interpretation, the current standard way of sharing images is difficult if you’re relying on your old fashioned Pacs system. You receive a call when you’re out and you have to go home, load up your computer, get the images up-  this can easily lose you 15 minutes which is absolutely critical. The shorter the door to needle time, the better the result. With StrokeViewer, I can immediately receive a patient’s CT scan on my mobile phone wherever I am. That saves time by enabling me to interpret the high quality images on the spot. It’s amazing and something I’ve never experienced with the regular Pacs system. Seamless image sharing makes everything so much easier. 

What are your thoughts on the added value of assisting image interpretation via artificial intelligence? 

 Assisting interpretation of CT images via artificial intelligence brings the bottom of quality up. It raises the bar.  If for whatever reason (timing, location etc.) a less experienced radiologist is interpreting the scans (which is common), AI can help him or her perform like an experienced radiologist. That’s something which is really becoming clear from Strokeviewer and is so helpful. As with any relationship, it takes time to build a relationship of complete trust. And similarly with any new technology, AI will need to prove itself in clinical practice. But we are currently seeing real life applications of artificial intelligence and it is amazing how fast my colleagues are beginning to rely and trust in it. It’s definitely going in the right direction.

How would you describe the current relationship between radiology and artificial intelligence? And how do you see this progressing? 

In radiology we’ve had digital images for quite a long time, more than 15 or 20 years. Even in the early stages there were various computer aided detection promises. But it’s only very recently, in the past 3 or 4 years, that deep learning and artificial intelligence, actually make possible what was promised all that time ago. I’ve worked with systems which have tried to do similar things but in a poor way. As with any relationship, it takes time to build a relationship of complete trust. And similarly with any new technology, AI will need to prove itself in clinical practice. But we are currently seeing real life applications of artificial intelligence and it is amazing how fast my colleagues are beginning to rely and trust in it. It’s definitely going in the right direction.

 

Nico.lab improves clinical practice with innovative and trusted AI. Our product StrokeViewer, is an AI-powered clinical decision support system, offering a complete assessment of relevant imaging biomarkers within 3 minutes.