NICO.LAB at ISC 2021

By | News

Our cloud-based solution enables physicians to provide every stroke patient with the right treatment in time.

With Medicare Reimbursement, ICD-10-PCS code, we can help your hospital to improve patient outcome while reducing hospital costs

StrokeViewer helps to reduce undetected LVOs

  • Artificial intelligence detects occlusions up to distal M2 on CTA
  • User immediately notified of findings via App/email
  • Automated PDF reports

“I think it works brilliantly as a screening tool to alert the team that there is high suspicion of LVO. The communication gets everyone coordinated in a parallel fashion...”

Dr. Albert Yoo, MD

Medical Director at Texas Stroke Institute

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    StrokeViewer Features

    Mobile miniPACS

    This mobile miniPACS is an extension to the familiar hospital PACS , enabling stroke experts to access analyzed patient scans within minutes, anywhere on their device.

    Seamless image sharing

    Rapid image exchange allows stroke experts to forward patient scans to the nearest intervention center with the click of a button, physicians can then plan for patient arrival.

    Certified Diagnostic Viewer

    The certified diagnostic viewer is a unique feature to StrokeViewer. This allows physicians to diagnose patients from their mobile device.

    3D Treatment Planning

    This feature is included in StrokeViewer LVO. It helps neurologists and radiologists visualize the brain vasculature and blood clot.

    Interventional neuroradiologists in particular have reported their use of the 3D Treatment Planning feature for selecting the micro catheters to use prior to patient arrival.

    “It can be really helpful in detecting distal occlusions, because the 3D Treatment Planning can make an occlusion more obvious especially when a vessel goes into another slice or in a different direction. It prevents you from mistaking an occlusion for a loop in the vessel”

    Dr. Bart Emmer, Ph.D.

    Interventional Neuroradiologist, Amsterdam UMC, Netherlands

    New Paper Quantifying the Health and Cost Effects of Faster EVT

    By | News

    Currently 20% of LVOs are initially undetected1 and 40% of patients are treated too late2.

    At NICO.LAB we are working hard to ensure we help hospitals to reduce this % and streamline the workflow to reduce door to groin puncture time. It is extremely rewarding to read a paper with powerful numbers that actually quantifies the impact it has on a patient when just one minute is saved.

    The main conclusion from the paper is that EVT administered 1 minute faster results in overall healthcare cost savings on average of €309 and 1.3 days of disability free life in the Netherlands. Those numbers for an hour faster treatment translate to €18, 540 euro and 80 days on average.

    Click here to read the full paper from the MR CLEAN Registry titled Quantified health and cost effects of faster endovascular treatment for large vessel ischemic stroke patients in the Netherlands3



    Henk van Voorst,Wolfgang G Kunz,Lucie A van den Berg,Manon Kappelhof,Floor M E Pinckaers,Mayank Goyal,Myriam G M Hunink,Bart Emmer,Maxim Johan Heymen Laurence Mulder,Diederik W J Dippel,J M Coutinho,Henk A Marquering,Hieronymus D Boogaarts,Aad van der Lugt,Wim H van Zwam,Yvo B W E M Roos,Erik Buskens,Marcel G W Dijkgraaf,Charles B L M Majoie



    Journal of NeuroInterventional Surgery



    BMJ Publishing Group Ltd.



    Jan 21, 2021


    1: B. Fasen, R. Kwee et al. AJNR 2020
    2: Kunz, W. G., et al. “Lifetime quality of life and cost consequences of treatment delays in endovascular thrombectomy for stroke.” BMJ 10 (2019)
    3: van Voorst H, Kunz WG, van den Berg LA, et al Quantified health and cost effects of faster endovascular treatment for large vessel ischemic stroke patients in the NetherlandsJournal of NeuroInterventional Surgery Published Online First: 21 January 2021. doi: 10.1136/neurintsurg-2020-017017

    Generating brain CT images using Disentangled Variational Autoencoders

    By | News

    Can we generate CT slices of a brain? Or interpolate between two different brains? Or… if we represent the brain image as a set of numbers, can we change brain size, rotation, anatomy just by tweaking these numbers?

    I asked these questions when I first read the paper about Disentangled Variational Autoencoders (β-VAE).

    According to the paper, you can encode an image into a small numerical vector in a way that each vector’s variable will be responsible for one independent and interpretable visual feature. For faces, it can be skin color, age, gender, or image saturation. What these features will be for brain images? Let’s find out.

    Elena, one of our Machine Learning Engineers, shares a paper based on her recent research looking into whether neural networks can understand brain anatomy, maybe just a tiny bit?! Elena tries to answer this question using Disentangled Variational Autoencoder trained on brain CT slices.

    Request the full text below and a downloadable version will be sent via email.

      10 Questions With….. Dr. Michael Hill

      By | Expert Series, News, Uncategorized

      Dr. Michael Hill is the Director of the Stroke Unit for the Calgary Stroke Program, Alberta Health Services. He is also a Professor at The University of Calgary for the departments of Radiology, Clinical Neurosciences, Community Health Sciences and Medicine. Dr. Hill has been involved in several stroke studies in recent years including the reputable ESCAPE and ESCAPE-NA1 trials. During this interview Michael shares with us an understanding the potential impact of the ESCAPE-NA1 results.

      How did you get to where you are in your career today?

      The course of my career I suppose has been relatively opportunistic. I’ve had a chance to follow opportunities that I saw or were put in front of me to go forward. For example I started training in internal medicine and then switched over to neurology, obtaining certification in both. When I was starting out in neurology, the NINDS tPA trial was published, which was the first thrombolytic trial for stroke that clearly had a positive outcome. It outlined a way forward for the treatment of stroke when really there hadn’t been a clear path yet in acute therapy for stroke.  Thus, the combination of internal medicine and neurology, and hence specializing in vascular neurology seemed like a good marriage for me. Then the evolution of endovascular therapy began. We ran the ESCAPE trial from Calgary and I was involved in a series of trials which showed the effectiveness of EVT for the worst kinds of stroke, large vessel occlusions. So our involvement has really taken off since then. 

      Congratulations on the recent breakthrough with the ESCAPE-NA1 trial. How does it feel?

      That’s an interesting one too because that also evolved in a similarly opportunistic way, by following the choices that were available at the time. The collaboration between us at University of Calgary and the group at the Toronto Western Hospital & University of Toronto, where the compound, nerinetide (NA1) was developed, has been a very fruitful and enjoyable one. It is a very exciting time, we have shown there is a way forward for cytoprotection in stroke which is a novel concept in itself. We are going to need more research to really solidify our findings and explore more details such as which patients would be eligible for this sort of treatment and at which stage it should be administered but overall I think there will be essentially another arrow in the quiver for treating stroke.

      Can you give me an understanding of the potential impact of NA-1? 

      Well, excitotoxicity is a term that describes a whole series of biochemical reactions which ultimately lead to cell death. In the ESCAPE-NA1 trial we demonstrated the efficacy of the compound called nerinetide or NA1 in treating a very specific group of stroke patients by manipulating this biochemical process. Excitotoxicity is particularly relevant in the brain and spinal cord and there are many other neurological diseases in which excitotoxicity occurs. If nerinetide (NA1) can be applied to prevent cell death in multiple disorders this would have a huge impact. But in this recent trial we targeted a very small subset of stroke patients so we are not talking all strokes here, but these things have to start somewhere. Even if nerinetide (NA1) is only applicable to this type of stroke this will still be a great breakthrough as it is such a common disease.

      What are the tangible results or impacts that could be felt by a patient treated this way? 

      Well first of all, to be clear, we found an interaction effect between nerinetide and alteplase which meant that nerinetide only seemed to work on those that did not receive alteplase as well. Within this group, that didn’t receive alteplase, we saw quite a large effect size of 9.5% absolute benefit in independent outcome; so it essentially means there is one extra person in ten walking out of a hospital functionally independent instead of being severely disabled or dead. This is quite a large effect size, approximately half the effect of endovascular therapy. To give you some perspective, I’ve been involved in stroke for over 20 years and when I began, only 2 out of 10 patients with an LVO would be functionally independent after their stroke and maybe 3 or 4 out of 10 would die. With endovascular therapy this really increased it to 5 out of 10 being able to go home functionally independent and only 1 would die. Now we are talking about 6 out of 10 being functionally independent with EVT plus nerinetide.

      We are making incremental progress which is fantastic and certainly with endovascular therapy, it was such a large leap after the ESCAPE trial and MR CLEAN alike we really were visibly able to see the way things were happening on the stroke inpatient service. Fewer people staying for long periods in hospitals and being paralysed, more people going home on the 3rd day instead of staying for several weeks. It is very gratifying for sure. 

      It must be incredibly gratifying. I was wondering if there have been any pivotal moments in the ESAPE-NA1 trial?

      When you design a trial like this you are definitely standing on the shoulders of giants.  We were definitely beneficiaries of failed experiments in the past. I mean you learn something everytime right?

      I think there were a few key things that were done. Firstly actually doing the experiment on primates initially. Lots of ethical issues come up with this and you have to be very careful but doing this was a really important demonstration that it was possible. Then we worked really hard in the trial to make sure we were duplicating that same model but in humans. And I think we were very successful in doing so just based on the study metrics. Duplicating the preclinical model was probably the most important scientific thing we’ve done. This was how the evolution of highly effective endovascular therapy happened. Being involved with both EVT and ESCAPE-NA1 has been terrific, and they followed one from the other.

      Where would the administration of NA1 work in the structure of the current workflow?So that’s important and an example of another feature of trial design. In any trial, you need to integrate whatever you are testing within the current paradigm of care. We gave nerinetide (NA1) at any time after randomisation up until closure of the groin so it was not a linear paradigm. It went in parallel with the treatment. This means it could have been given at many different stages – just before emergency treatment, in the angio-suite, while the thrombus was being extracted or even just after reperfusion. In hindsight, we now think that the earlier you give it, the better the result, so as we go forward with future studies looking at this compound we are really going to try and give it early. 

      How does nerinetide (NA1) work in terms of protecting or restoring the brain cells?

      It’s a rational drug-design peptide. A little protein made up of 20 amino acids. A short segment of amino acids are taken from Tat protein, which is an envelope protein from HIV virus. Tat protein plays an important role in allowing the entire protein to enter a cell, crossing through the blood brain barrier so it gets into neurons and across a lipid membrane without any problem. We see that pharmacologically once it’s been intravenously administered, it is out of the patient’s circulation within 10 minutes and into the tissue. 

      The remaining amino acids form a peptide that interferes with a protein-protein interaction inside the neuron between the cytosolic surface of the NMDA receptor and a protein cascade which is part of the negative aspects of excitotoxicity which would ultimately normally result in excess production of nitrous oxide, which is fatal to a cell. They target the excitotoxic pathway downstream from where it has been targeted before. It’s always been targeted on the extracellular surface of various aspects of the receptor whereas this was the first time it has been targeted inside the cell. It’s never worked well extracellularly because it’s always interfered with normal functions of the receptor.

      You’ve already touched upon what this could lead to in terms of other neurological areas but what are the next steps of opening the door for new leads in stroke research?

      Excitotoxicity is a pathological mechanism important in trauma, MS, Alzheimer’s disease and many other neurological diseases. Could you improve outcomes of concussion or a car accident if you can receive this drug to protect neurons immediately after the event? We just don’t know yet, and we will need more research to be done.

      Although, I’m not saying this idly, there are some animal model data to say the drug is useful in traumatic injury, for example when looking at rats but it hasn’t been studied in humans. This is all speculative so we will have to research and see what the future holds.

      How exciting, you must be incredibly excited yourself, what has been the most significant breakthrough for you to date?

      Well, what I’ve focused on the most in the past 10 years is therapeutics. Some trials have been successful and some have failed but it’s definitely the most recent two for me that have been the most influential. Especially the ESCAPE trial which has had the most impact.  It is now five years since that was done and of course this was not just my research, there is also the MR CLEAN group, the Australian, British, Swiss, French and Spanish groups that all showed how effective endovascular therapy is. Now we’ve got to get on with it and really implement EVT globally. This has completely changed the face of acute stroke management and how people are triaged including how hospitals are organised. 

      I do think we’ve got a really interesting result with ESCAPE-NA1 so if that also evolved to become a big success in stroke and expands beyond then that may be even bigger, i don’t know.

      Can you recall a time in your career to date when you’ve felt most challenged?

      I can think of a couple of different occasions. There are many challenges in research because it is a highly entrepreneurial process. You have to come up with an idea, write a grant, be successful (which only occurs 15-20% of the time), receive the money, hire and organise a team, execute the project and produce results and then there is no guarantee the results are going to be what you hope to see! If you’re successful the first time you then have a much greater chance of doing it again because success allows you to follow on from the things you’ve done before so it’s very much like running a business in terms of getting stuff done. 

      The biggest challenge I find is making sure you can continue to support all your team. If you are the one in charge, receiving all the money coming in, you must make sure you’ve got funds to conduct the research and support the people involved especially when they are risking their time, effort and employment. It is all that kind of stuff that has stopped me from sleeping at night. 

      Congratulations to you and your team as I can imagine it’s been years of extremely hard work!


      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.

      NICO.LAB’s 2020 Highlights

      By | News

      2020 has been an exciting year of growth and success at NICO.LAB. As the year draws to a close we want to reflect on some highlights and milestones we have reached during an unusual and challenging year.


      StrokeViewer LVO received FDA Clearance

      In late November, after many months of hard work from the NICO.LAB team, we received FDA approval. We look forward to our next  phase of growth as we enter and expand through the US market, improving the lives of more stroke patients.

      A New Visual Identity

      Our new visual identity was introduced alongside FDA clearance in November. The refreshed look will help us to convey our mission, to empower physicians to provide patients with the right treatment, in time. 

      Proven Effectiveness of StrokeViewer

      Recent studies have proven a reduction in time-to-treatment and improved patient outcome with the use of StrokeViewer. It is very gratifying to see how our work at NICO.LAB aligns with our mission and translates into a proven positive impact for society.

      First Live Stream from NICO.LAB

      In early December, our Live Stream between Interventional Neuroradiologist Dr. Bart Emmer and our Chief Technology Officer Renan Sales Barros focused on ‘How Cloud-based AI Solutions in Stroke Care’. It marked the beginning of an exciting online series. Keep your eyes peeled in the new year as we plan to dive deeper into the different biomarkers used to diagnose stroke which will include the latest advancements in research and hearing more from the experts.

      Being a Part of the ICOVAI Consortium

      The consortium formed in response to the urgent need to quickly diagnose and prioritise patients in the early months of the pandemic in March. ICOVAI aimed to triage COVID-19 patients and speed up diagnosis with powerful AI. During difficult times it was great to see the collaboration of science, research and technology experts to come together and expertise to tackle the pandemic.

      International expansion

      We are proud to be on the ground in Australia, UK, Germany, US and the Netherlands. It is our ambition to provide the best stroke treatment to as many as we can reach. With our cloud-based solution we can scale at a fast rate, and implement in hospitals across the world

      A Growing Team!

      The NICO.LAB team continues to grow and diversify. We now finish 2020 with double the number of Full Time Employees than last year. With employees of 13 different nationalities, we are a passionate and international team! We have unified and kept spirits high in line with NICO.LAB’s values. An achievement in itself considering the difficult circumstances of remote working and little face-to-face contact. 

      Techleap Rise Program

      Back in May we were selected as one of the highest potentials in the Netherlands, to join several other inspiring scale ups on the program. It is designed to help scale ups grow faster internationally. Through workshops and training days we connected with many talented entrepreneurs which helped us all learn and grow together.

      All of us at NICO.LAB would like to thank our colleagues in the healthcare sector and all that have worked so incredibly hard battling the COVID-19 crisis. We hope 2021 will be a better year for all.


      Merry Christmas and a Happy New Year!

      Merel Boers, CEO and co-founder of NICO.LAB

      I am very proud of the team at NICO.LAB who have worked hard and achieved a huge amount under difficult circumstances. I want to thank all those that have supported NICO.LAB along the way. It is very exciting to think about where we could be this time next year!

      Merel BoersCEO and co-founder of NICO.LAB

      How Cloud-based AI Solutions are Transforming Stroke Care

      By | News

      Renan Sales Barros

      Chief Technology Officer at NICO.LAB

      Dr. Bart Emmer

      Interventional Neuroradiologist at AMC

      On Monday 7 December we Live Streamed an expert discussion between Interventional Neuroradiologist Dr. Bart Emmer and NICO.LAB’s Chief Technology Officer Renan Sales Barros on ‘How Cloud-based AI Solutions are Transforming Stroke Care’. If you didn’t manage to join us on the day there is the opportunity to watch some highlights below.

      Over the one hour they covered a wide range of topics including the different bottlenecks in the current stroke workflow, how AI helps to minimise delays and improve diagnosis, and finally why cloud-based solutions are the way to go.

      Catch up below and we hope to see you at one of our webinars next year!

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      Dr. Emmer and Renan discuss the different challenges and bottlenecks in the current stroke workflow, without the help of artificial intelligence.

      AI solutions can be very effective to save time and improve diagnosis. Radiologists use the various algorithms to go through the different stages of the workflow. Find out more in the video.

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      The AI Solution also helps the interventional team to plan the endovascular thrombectomy, prior to patient arrival.

      FDA approved

      NICO.LAB ready to revolutionize U.S. stroke care following FDA clearance

      By | News, Press Release

      The U.S. Food and Drug Administration (FDA) has cleared StrokeViewer LVO, NICO.LAB’s artificial intelligence powered solution in stroke care. StrokeViewer enables physicians to provide every stroke patient with the right treatment, as fast as possible. Two million brain cells die every minute until blood flow is restored (Saver, 2006), starting treatment earlier can make the difference between recovery or life-long disability.

      StrokeViewer LVOThis week the FDA approved a 510k application (FDA K200873) for StrokeViewer LVO, an artificial intelligence algorithm for fast triaging of stroke patients. StrokeViewer LVO detects image characteristics associated with a Large Vessel Occlusion (LVO) and alerts physicians. The FDA application was supported by a multi center clinical study where the performance of the algorithm was retrospectively evaluated in 384 patients from multiple US stroke centers. An expert panel assessed the data to identify LVOs (ICA, M1 and M2) and comparison with the algorithm showed results that exceeded the performance goal.

      StrokeViewer was developed by the Dutch medtech company NICO.LAB. The cloud-based solution uses artificial intelligence to support physicians in the emergency stroke setting. The FDA-cleared LVO algorithm is embedded in a cloud-based system that sends a notification to the medical specialists involved just minutes after a stroke patient arrives in the hospital. The physicians are able to use their smartphones, in the hospital but also at home, to inspect the CT images in a web viewer and diagnose a stroke.

      Merel Boers CEO & Founder“Medical specialists are under enormous pressure to make fast decisions day and night, but it’s not easy. Complicated assessment and interhospital communication sadly make life difficult for physicians motivated to treat stroke victims effectively” – said Merel Boers, CEO and co-founder of NICO.LAB. “With our first FDA clearance we are now able to show U.S. physicians how impactful the combination of human and artificial intelligence is. And yes, more will follow as we are fully committed to unlocking the full healthcare potential for every patient.”

      StrokeViewer is currently in use in Australia and Europe where it has proven to reduce the time from hospital arrival to start treatment for patients with acute stroke, leading to reduced patient disability in the short-term and more clot removal treatments performed.

      Stroke is a leading cause for serious long-term disability in the United States. The economic burden of stroke exceeds an astonishing $100 billion per annum (Girotra, Lekoubou et al., 2020). Stroke incidence and associated costs are rising drastically due to an ageing population and increasing unhealthy lifestyle. NICO.LAB is dedicated to playing a crucial role in reducing these stroke related costs and improving quality of life of patients by combining human and artificial intelligence to revolutionize emergency stroke care.

      FDA approvalStrokeViewer consists of a comprehensive set of tools to support the entire stroke workflow. StrokeViewer LVO is the first with FDA approval. Local availability of StrokeViewer functionality is subject to applicable CE marking, TGA and FDA approval (actual status on

      Saver, 2006 – “Time is brain—quantified.” Stroke 37.1 (2006): 263-266
      FDA K200873 – StrokeViewer LVO is approved as HALO under FDA submission number K200873
      Girotra, Lekoubou et al. 2020 – “A contemporary and comprehensive analysis of the costs of stroke in the United States” Journal of Neurological Sciences, volume 410, 116643

      10 Questions With….. Ludo Beenen

      By | Expert Series, News

      Ludo works at the Academic Medical Centre in Amsterdam in the Emergency Department as a Trauma and Emergency Radiologist. We interviewed Ludo during the COVID-19 lockdown when he was working from home a lot more. Ludo told us he feels the stroke workflow will be very different following COVID-19 compared to how things were done prior. He informed us stroke care and all emergency care has been more complicated due to the worry of contamination and spread of infection.

      So firstly Ludo, how did you come to be an emergency radiologist?

      A little bit of chance and a bit of luck. I am a broad oriented physician meaning I’m interested in a variety of things. Radiology concerns a lot of physiological systems in the body which appeals to me. When my predecessor left the emergency department I jumped at the opportunity to become an emergency radiologist! What I love about my job is that you always know there is room for improvement and things are always evolving. If you look at stroke for example, CT perfusion was discovered, then the success of endovascular therapy followed. We then had to work out how to efficiently transfer patients to intervention centers with all the important information, and this is where Nico.lab has come in. I have been very impressed with the performance of StrokeViewer and I really believe it is doing better than the others because it encompasses all aspects of emergency stroke care, from start to finish.

      That is great to hear, what’s it like being at the heart of an emergency department?

      Our Prime Minister recently said during the covid outbreak “you should accept that you will make 100% of your decision based on only 50% information that you have and you should be able to cope with that problem”. I think this is a great way to look at it, even in the emergency department. It can be difficult to accept this and people often want to take their time to make a clear diagnosis, avoiding mistakes. But my state of mind is that it’s important to not see it as a problem but a challenge, and do your utmost best to make the best decision from the 50% of information you start with. Everyone knows if you only have 50% of information, sometimes you’re wrong and coping with that in the emergency department is often the main issue for emergency radiologists. You have to put the different bits of information together like a puzzle and come to a conclusion as best you can. I enjoy that challenge the most, in a timely fashion. It will be interesting to see how artificial intelligence performs. Diagnosing a stroke patient is difficult. I am an expert so most of the time I can see where the problem is but certainly for juniors they can’t identify the problem as quickly so in that aspect artificial intelligence is very important and has the potential to take the pressure off.

      When have you felt most challenged at work?

      Well what is happening now is people have recognized more and more how useful the CT scanner is. When I first joined the ED we only had one sliding gantry CT scanner at the emergency department. But now we have two scanners covering three shock rooms. This means we can have three acute patients arriving simultaneously to the ED when a lot of hospitals would only have capacity for one. Basically triple the pressure which can be the biggest challenge when you have to make treatment decisions and give a fast and accurate diagnosis. But this is also what I enjoy the most about my job! 

      From your professional perspective, can you explain some of the challenges and gaps in the emergency department?

      The challenge is of course from an operational point of view. Everyone has to be very strict and everyone involved wants to do things their own way which can result in delay. People generally think when they are busy, they are doing valuable work but that is not always true given the circumstances! It is a skill of knowing what is necessary for the patient before things have even happened, and ensuring you stick to it and do nothing unnecessary. 

      The second part is the infrastructure. If there is a lot of data that has to be calculated from the scanner this can take time. We now accept this and have to wait a minute or so but we know how valuable time is. A faster calculation of data would be really helpful. Maybe that is also where artificial intelligence solutions would work and a part of that is you calculate them and then it has to be transferred to the PACS system. That is something that is not working as fast compared to what I want. I know the moment it is scanned and calculated but i can’t see it. I still have to wait some minutes. People are not always aware of how much damage is done every second in the emergency department. When you are 15 minutes slower there can be huge consequences and that is something we should share. So I would say the main challenges are time and sharing the information.

      In the current stroke workflow when a stroke patient comes into the ED how do you go about making your decisions?

      This is really a team effort, and we regularly discuss optimization of the workflow with all the stakeholders. When a stroke patient comes in, I try to be as clear as possible, and want other members of the team to also work as efficiently as possible, without wasting time. Non-contributing tests should be avoided. It’s a matter of making a sequence of binary decisions in a strict manner, with always the possibility to adapt the workflow to the circumstances.

      Is it a stroke: yes – what do I need to know? Is it hemorrhagic or not? Is another diagnosis possible? What about brain perfusion? And are there filling defects visible on the CT Angiography? Are the radiological findings and clinical findings comparable? What type of therapy should be started, intravenous or endovascular? This should be performed in a timely fashion: Time = Brain!

      In your opinion where do you think AI could be most useful for you and how could it be implemented into the radiology department? 

      Although it wouldn’t be typically thought of as AI, I think the calculation of penumbra and core is very useful, but still needs some further studies. It’s great that support from AI calculations is now accepted. In my opinion, if you want to implement a new AI powered tool into a healthcare system it needs to require very little effort from the hospital’s end. It needs to be something that is easy to implement, easy to use and easy to interpret. The moment it is presented to physicians with very little effort then they can accept that they have to know what it is all about!

      Can you see how AI can have a helpful role in the stroke workflow?

      Yes, there are certainly a few ways in which I think AI benefits the workflow. The most obvious role is the recognition of clots which for me is not the most difficult challenge when I can correlate it with the perfusion calculation. But some of my colleagues can have some difficulties in finding the clot so there is definitely an advantage in that. 

      On a broad scale we have quite a few parameters which is great when trying to diagnose patients. The question we really want to know the answer to is what will the patient outcome be for a particular treatment. I know this means taking into account many parameters. Some interventionists are reluctant to treat older patients for example over 80 years old. But I believe for every individual it is different. Some 85 year olds may have at least another 15 years of valuable life. So this can be a moral problem. Hopefully in time when enough data is available we will be able to predict the probability of a beneficial outcome for a particular treatment.

      What is your experience of AI in the stroke workflow and do you see it adding value in your opinion?

      One of the most important things StrokeViewer does for me is the transfer of information of patients between hospitals and radiologists which can save so much time. When a patient is on its way to you, knowing all the information the primary center has found means there is no risk of wasting time repeating procedures. This has been of great value for us when trying to diagnose and deliver the best care. The system we had in place before meant that for half of the cases we were unsure whether CTA had been performed or we were unable to see the images so time would be wasted. Whereas now we always have the information beforehand and can make a quicker decision whether it is worthwhile the patient going directly to the endovascular suite. In that respect AI is great support for clot detection.

      Are you excited about the potential impact of AI and the future?

      Yes I think there are interesting ways in which AI can support several processes and have a positive impact, which is exciting to see. I also do have my reservations but it’s not a fear. Personally I think it is illogical to think AI will take over and humans will just be the support to AI. The human brain is very different compared to AI even though AI is the product of humans. I think the human brain functions very differently therefore AI will help support and strengthen us. 


      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….. Bram van Ginneken

      By | Expert Series, News

      Bram is a Professor of Medical Image Analysis at Radboud University Medical Center and chairs the Diagnostic Image Analysis Group. He is co-founder of Thirona, a high-tech company focusing on the development of automated medical image analysis. Bram obtained his PhD on Computer-Aided Diagnosis in Chest Radiography and went on to develop software for tuberculosis and lung screening which is now used in many countries across the world.

      How would you describe what you do for a living and what impact does it have?

      What I typically say to people is that my team develops computer software that analyses medical images to do the same kind of thing that doctors do. At the moment, doctors are spending a large part of their job just looking at images and saying what’s wrong with the patients. They often have to search for tiny lesions like a needle in the haystack and since we don’t have enough doctors and healthcare is extremely expensive, developing this software can have a huge impact. Automating the scanning of images we can reduce costs and also make the job more enjoyable for doctors.

      How did you come to what you do?

      While I was studying Physics at university I saw an advertisement for a PhD position which was about writing a computer program to take a chest x-ray and predict if someone has tuberculosis. I was not planning to do a PhD but this was a very practical project and really appealed to me. I ended up becoming a professor and leading a big research group. Developing computer algorithms that do something really intelligent doesn’t have to be in healthcare but I ended up in that area and it led me to producing tuberculosis and also lung cancer screening software which is now in active use everyday all over the world.

      Why do you think there is hesitancy when it comes to AI specifically in healthcare?

      In my experience, doctors are open to using these kinds of tools and if they work well they are very happy to use it. If for example a license runs out and the software is no longer they will immediately call and insist it must work again. But if the software doesn’t work so well they are very quick to say it is unacceptable. But I think this is inevitable, when a new product comes on the market the very first version is usually not fantastic. The earlier doctors that have tried it are likely to say it is useless but then in time a much better product quickly becomes available and it is then taken for granted.

      Machine learning was the big buzz 10 years ago but what’s your vision for the future?

      I think the next big thing is actually implementing software that works. We are definitely at the very beginning. Medicine is not a sector where things move very quickly because it is heavily regulated when talking about patients and lives. So I hope the next big thing is having successful, well working software across the healthcare system but this will take longer than we think. I also hope this will keep healthcare affordable.

      I think the end goal should be to make high quality healthcare accessible to more people all over the world, which can only be achieved if we keep it affordable. I think AI can help enormously with that. If you imagine our society without computers then that is the biggest difference when you think about 50 years ago. I think 50 years from now these computers will be doing the kind of tasks we are doing now. The question is then what is left for humans but of course humans should be in charge of things and should not go out of work.

      If we are able to automate the simplest of tasks will this have a huge impact on healthcare systems in terms of freeing up physicians time and reducing costs?

      Yes I think most research being carried out focuses on improving the quality of healthcare and a very small part is about reducing costs. If you look at what could have a big impact in society then I believe reducing costs has a greater impact than improving healthcare. If we didn’t research how to improve healthcare that would be stupid but there is so much emphasis and prestige around it and much less research looks at reducing costs.

      In some cases improving healthcare leads to reduced costs for example if you automate what doctors do today then you save costs because computers can do things much faster and have a lower salary but I think a lot of the improvements in healthcare actually increase costs tremendously. For example super expensive cancer drugs improve healthcare a little, for a small percentage of the population, but at a huge cost. So I personally think improving healthcare ultimately will not always reduce costs but increase them. But if you are willing to save lives you will be willing to pay a high price for that.

      Do you think that within the healthcare sector the implementation of AI should or could be faster?

      Well it really depends, some tasks are simpler and can be implemented faster but others are more complicated and therefore take longer. I think right now we should aim to implement the simple things and then hopefully society will accept that we need to develop these algorithms and there will be funding to sort out the more difficult cases. A lot can be done but it is still a lot of work and time to develop these systems and it’s a big investment one has to make. It is not that implementation is technically difficult, like some radiologists may think because their IT system has said ‘our PACS system is not compatible’. Implementation is easy for example with StrokeViewer which has been cleverly designed to be easily implemented around the current workflow and PACS system. So technically it can often be done, but there must be an incentive, demand and a funding scheme so that it is worthwhile for companies to develop this software for radiology departments to buy.

      Would you be able to comment on general AI and narrow AI?

      So at the moment we only have narrow AI. They are often seen as separate things but in reality there is a continuum between narrow and general AI. Eventually we will get collections of narrow AI solutions which will make up general AI, for example finding all different relevant diseases on a chest x-ray. So for us, as software developers, it will become easier and easier. Even now I can see that what I am able to do with a student in 3 months used to take us 3 years. We used to train on data sets of 100 images and are now training on data sets of 100,000 images. Facebook is training networks with 2 billion images. Things are definitely moving and developing, it is very exciting.

      There has been more and more focus on prevention and early detection rather than cure, if we continue to work with narrow AI will we be able to detect multiple diseases someone may face at one time?

      Well preventing someone from getting a disease is the most important but that is more to do with lifestyle and is outside the realm of what I’m doing with image analysis. My work is early detection which I think is a completely different thing. But yes, AI products developing on the market today are for the most common abnormalities because there’s a market for it. I hope in the future we will have implemented AI for the more common diseases and can develop software for more rare diseases. In some areas of early detection we will have better scans although CT scans is a fully developed technology. It has not improved for the last 15 years, they are so good they measure almost every photon of radiation you give to the patient. To get a better scan you have to give the patient more radiation which is harmful so we won’t do that. In the future, yes I think things will be totally transformed by AI. We will be able to see diseases earlier if we want to, but then we have to decide what we want to do with that. For patients, people get very scared if they hear they have early stage cancer but we all have cancer, we just don’t detect it until is something that could be serious. In the future if we get better detection mechanisms we have to deal with that and I think it’s going to be very difficult for people to handle. If there are more possibilities of detecting disease early, do you really want to know that? Is it always necessary to know that?

      Can you think of which living person you admire most and why?

      Very difficult question, one of the big nice things working in science is you see and interact with very clever people. In many ways more clever than I am which is very inspiring. I am using deep learning to analyse medical images. But I didn’t invent it, those that did are so clever and I think it is so fantastic what they have developed. There are a lot of people I admire in the field.

      Finally, are you excited about the future of AI within medical imaging?

      What I find exciting and also scary is the development of the last couple of years. We now have these AI systems that can actually create images. So for example, a simple application is a very low dose image and you improve the quality of the image. There are also systems where you take an X-ray and predict a 3D scan and it actually looks very realistic. We know this from deep fake images, you may have come across this online when someone is made to look like they are talking and it’s actually completely computer generated and looks really realistic.

      I think this has great applications in medical imaging analysis. We will be able to generate complete 3D scans from very little data but it’s also very scary. We won’t know whether a computer has made up certain structures in an image or not and whether we can actually trust the data that the computer generates. I think at the moment that is one of the most exciting areas in deep learning and image analysis.


      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….. Fabien Scalzo

      By | Expert Series, News

      Fabien Scalzo is an Assistant Professor at UCLA and Director of the AI in Imaging and Neuroscience Lab there. He develops Machine Learning and Computer Vision algorithms to better our understanding of neurological disorders particularly Stroke and traumatic brain injuries (TBI). He does this using brain mapping of imaging and biosignals.

      So Fabien, how did you come to do what you do today?

      I was trained in computer vision and machine learning at the University of Liege in Belgium and carried out research to build new algorithms for visual learning. I became fascinated by how we perform these visual tasks as humans and interested to learn about the learning mechanisms in the brain. This was a big inspiration for my PhD work and one of the reasons I later joined   the department of neurology and neurosurgery at UCLA. This was 10 years ago, when I realised that machine learning, although had its limitations, could really make a difference for clinical care. 

      Why do you think AI has proven so transformative to medical imaging and why is this particularly the case in something like stroke?

      With stroke, it wasn’t even clear 10 years ago that imaging would be helpful in guiding decisions and there was a lot of controversy regarding the value of it. With machine learning, we let the computer figure out the patterns that really matter by retrospectively analysing the outcome of hundreds of patients. It reduces the manual information and the prior assumptions that we need to input and because we make better assessment of the patient, thanks to the algorithm, the patient may have access to the best treatment option; the one with the highest likelihood of good outcome. Machine learning streamlines automation and has an ability to extract information that we would not be able to comprehend or even visualize, making it accessible to clinicians.

      What do you think about the hesitation to AI in the medical field? Where do you think it comes from?

      One part of this is tradition. The medical field has operated in a certain way for so many years; very successfully in many cases. There is also a hierarchy amongst doctors and bringing on board a machine that is part of the decision process can be met with scepticism and in some cases it’s valid. There is a need for understanding what that machine is doing, I don’t mean being able to code it but get a grasp of the structure and the rationale behind its diagnostic output. 

      What do you think needs to be done to reduce this?

      The clinicians need to be able to give trust to the machine learning model which comes with having a good general understanding of it. Also regulation through the FDA is becoming even more important because they are the only organisation that the clinician can trust to adopt and regulate these tools. I think we need to customise special tracks for approving machine learning tools. These are very different from anything we’ve worked with before so even though it may fall within the category of a medical device there is another dimension to it. I see far too often clinicians taking for granted the ability of machine learning to always give the perfect answer and then if it gives a wrong prediction they immediately lose their trust in it. There is always some uncertainty and that’s ok as long as we are aware of it. I try to tell people it’s not perfect, it has its own weaknesses but as long as you know when that happens and what to look for, it is still useful.

      Do you think there are issues with transferability from different technical perspectives?

      Yes totally. There was a very big hurdle because we have two very different worlds trying to come together. On one side we have the clinical world which measures the value of the technology by how much it improves the care of the patient and the workflow. On the other side, the machine learning scientists normally measure usefulness  by how well the model performs with certain types of accuracy metrics. Even though these metrics may work well in the experimental setting it may have zero impact when transferred into clinical care. This is something I’m working on, trying to evaluate the machine learning not only with respect to the accuracy of the models but with respect to the clinical value in terms of decisions and workflow.

      Do you think in the next 5 years we are going to see big changes in the merge of these two worlds?

      Yes machine learning is very young and novel and will continually improve. At some point, machine learning will be at least on par with the best neurologists in the world for diagnosis so in that sense it’s going to be very interesting to see how it develops. At the moment all the models we have are extremely specialized for specific tasks but I think eventually models will develop to be more versatile and general so a neurologist will be able to see things that previously would have been missed. And some of that is not part of machine learning, it is common sense so eventually we want to bring common sense to the machine!

      Is the best of AI currently in medical imaging or is it to any extent behind because of the difficulty with patient data and accessing it?

      Today if you compare the magnitude of data in medical imaging to marketing/advertisement, we have extremely small data sets and they are for very heterogeneous diseases, where every patient is different. There are a lot of issues regarding privacy which need to be addressed. We are behind because we rely on data coming from a medical provider, such as a hospital, but across the world they do not have the infrastructure to deal with large amounts of data in an efficient way so that slows things down.

      It is great that there are so many new companies like Nico.lab that build around existing infrastructure in the hospital, which makes it easily accessible to all physicians and renders the process of using data in a transparent and secure way which is what we need right now. 

      If patients were more comfortable with sharing their data for research would this have a positive impact on AI?

      Yes millions of people will benefit from sharing your data so it’s increasing awareness of that and working out an infrastructure that will make people feel comfortable about sharing it. But I think it will be a gradual cultural shift. 15 years ago Google was using our data and there was a big backlash but now most people don’t mind about that to some degree. It is part of the world we live in, as long as it is recognised and we know how it is used in a positive way toward betterment of society I think it is acceptabe. But some would disagree and that is also ok! One should have the right to not share their data. 

      On a personal level, what is the most exciting aspect of your work?

      Well two things. You can use machine learning to do finance and make a lot of money but you can also use it to save lives and that is what excites me the most. To be more precise I am very interested in stroke because it is a disease that is extremely hard to understand – some patients may return home with almost no side effects and others may not survive. It is phenomenal that soon we will be able to predict accurately the outcome of a patient for a given treatment course. This works because machine learning can incorporate data from so many patients with different treatment options so eventually the model will be able to make simulations of the likely outcomes for different treatments including the different benefits versus risks. It will be truly personalised medicine!

      As well as prediction, all the different elements of AI streamlining and speeding up care will improve healthcare systems as a whole but do you think that AI will also save money?

      Yes, AI will make the whole process from reading images to treatment much more efficient therefore reduce time and make work easier and more pleasurable for neuroradiologists which will of course reduce costs. It seems obvious to me but i guess it depends on how versatile the model is. If it is only for stroke, you need another one for cancer and screening brain injuries then you will be surrounded by AI. Versatility and some AI is a good direction in the future where we will have a model not just for stroke. But we are not there yet, we are in different compartments, but eventually I hope we can bring it together.

      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.

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