Avicenna.AI signs a distribution agreement with Sectra for neurovascular AI solutions

Avicenna.AI's partner - Sectra logo

La Ciotat, France– April 27, 2022 – Medical imaging AI specialist Avicenna.AI today announced a signed distribution agreement with Sectra, an international medical imaging IT and cybersecurity company. The agreement will see Avicenna’s AI solutions for neurovascular pathologies offered through the Sectra Amplifier Marketplace, a platform for contracting, purchasing, and servicing of AI applications validated and verified for use at point of care.

Avicenna’s CINA solutions use deep learning to identify, detect and quantify life-threatening pathologies from CT medical images. The FDA-cleared and CE-Marked tools are seamlessly integrated within the clinical workflow, automatically triggering and reporting algorithm results through the systems already used by radiologists.

Stroke is a leading cause of long-term disability and the second leading cause of death, with one death every 6 seconds worldwide. There are two main types of strokes: ischemic stroke, caused by a blood clot (LVO), and hemorrhagic stroke, caused by an intracranial hemorrhage (ICH).

The AI tools to be included in Sectra’s Amplifier Marketplace include CINA-ICH, CINA-LVO, and CINA-ASPECTS.

  • CINA-ICH uses deep learning to identify suspected intracranial hemorrhage and prioritizes those cases in the worklist, dramatically reducing turnaround time for head trauma and stroke patients. (US/EU)
  • CINA-LVO is a triage tool for rapid automatic LVO detection and real-time triage and prioritization, accelerating clinical workflow and helping stroke teams in their diagnosis. (US/EU)
  • CINA-ASPECTS is an AI-based automatic quantification tool that enables faster, more consistent, and more precise interpretation for the assessment of acute ischemic stroke. (available for European Union only)

Using a combination of deep learning and machine learning technologies, the CINA solutions automatically detect and prioritize acute ICH and LVO cases within seconds, and assess them for severity, seamlessly alerting radiologists within their existing systems and workflow.

“To help healthcare providers get on the AI adoption journey, we have created the Sectra Amplifier Marketplace. We aim to facilitate easier access and usage of AI applications in medical imaging. This distribution agreement is an example of that. With Avicenna.AI tools deeply embedded in the Sectra diagnostic workspace, we provide our radiologists with enhanced diagnostic confidence for stroke cases,” says Nynke Breimer, Global Product Manager AI Radiology, Sectra.”

“We provide best-in-class AI triage tools that enable fast detection of the leading causes of stroke, leading to more efficient patient management,” said Cyril Di Grandi, co-founder, and CEO of Avicenna.AI. “We are glad to collaborate with a global partner such as Sectra. Through this agreement, we can help more clinicians to facilitate stroke decision making, ensuring a prompt therapeutic response and ultimately improving patient outcomes.”

About Avicenna.AI
Founded in 2018, Avicenna.AI develops medical imaging AI solutions for highly prevalent pathologies. The company uses artificial intelligence and deep learning to optimize many of a radiologist’s manual tasks. Its CINA products leverage deep learning algorithms to identify acute abnormalities and support emergency room triage. Avicenna.AI is co-founded by Cyril Di Grandi, who previously co-founded and successfully sold Olea Medical, and Dr. Peter Chang, a radiologist and internationally recognized expert in AI and deep learning.

About Sectra
With 30 years of innovation and more than 2,000 installations around the globe, Sectra is a leading imaging IT provider for health systems worldwide. Sectra offers a complete enterprise solution comprised of imaging modules (radiology, cardiology, pathology, orthopedics, and ophthalmology), and a robust VNA. Over the last nine consecutive years, Sectra has been awarded Best in KLAS for the highest customer satisfaction. For more information, visit Sectra website.

Arterys & Avicenna.AI Join Forces on AI Stroke Detection

Logo Arterys

San Francisco, CA & Paris, FRANCE – December 1, 2020 – Medical imaging AI specialist Avicenna.AI today announced that its FDA-cleared CINA Head triage AI solution will be offered by cloud-based imaging platform Arterys. Supporting the treatment of stroke and neurovascular emergencies, CINA Head detects intracranial hemorrhages (ICHs) and large vessel occlusions (LVOs) from CT-scan imaging.

Arterys has fully integrated the CINA Head AI algorithms and will make them available via its FDA-cleared MICA platform to its installed base in the coming weeks. The platform’s cloud SaaS model enables CINA Head’s workflow-enhancing algorithms to be delivered instantly to any of Arterys’ existing customers.

“Given CMS’s recent NTAP reimbursement decision regarding AI-powered LVO detection, we believe that AI is finally ready to transform clinical practice, and we’re excited to make Avicenna’s AI innovations available to doctors around the world,” said John Axerio-Cilies, Arterys co-founder and CEO. “We’ve tuned the experience for seamless workflow integration—towards the ends of efficient and effective analysis of non-contrast head CTs and CTAs and accurate detection of intracranial hemorrhages and LVO strokes.”

Using a combination of deep learning and machine learning technologies, CINA Head automatically detects and prioritizes acute ICH and LVO cases within 20 seconds, seamlessly alerting the radiologist within their existing systems and workflow.

“Arterys’s platform is the most agile in this new internet-based era of medical AI,” said Cyril Di Grandi, co-founder and CEO of Avicenna.AI. “They help companies like us go from code-to-clinic in weeks not years, giving radiologists the ability to instantly enhance their workflows with our AI – whenever they want to.”

About Arterys

Arterys is the market leader and the world’s first internet platform for medical imaging. Its objective is to transform healthcare by transforming radiology. The Arterys platform is 100% web-based, AI-powered, and FDA-cleared, unlocking simple clinical solutions. For more information on Arterys, please Arterys website.

Stroke Detection AI Tool Approved for Medicare NTAP

Triage tool for CT Scan

Paris, FRANCE – November 24th, 2020 – Medical imaging AI specialist Avicenna.AI today announced that its CINA Head software qualifies for the new technology add-on payment (NTAP) recently approved by the Centers for Medicare & Medicaid Services (CMS).

The NTAP is available for radiological computer-assisted triage and notification software systems like CINA Head, which analyzes computed tomography angiogram (CTA) images and urgently notifies clinical team members when a suspected large vessel occlusion (LVO) has been identified to reduce time to treatment.

Dr Peter Chang, radiologist and co-founder of Avicenna.AI, said, “AI-enabled CT stroke triage, specifically with LVO detection, is the first deep learning tool to receive the CMS NTAP designation and is eligible for up to $1040 of reimbursement. CMS only grants NTAP designation to promote the adoption of new technology that provides substantial clinical improvement over standard of care. This will help accelerate the innovation and adoption of deep learning technology by aligning the incentives of both software developers and clinical providers.”

In addition to LVO detection, Avicenna’s FDA-approved CINA Head triage AI solution for neurovascular emergencies also detects intracranial hemorrhages (ICHs). Using a combination of deep learning and machine learning technologies, CINA Head automatically detects and prioritizes acute ICH and LVO cases from CT-scan imaging within 20 seconds, seamlessly alerting the radiologist within their existing systems and workflow.

Avicenna.ai: NTAP information for customers

Avicenna.AI believes that there is a strong argument that the CINA Head triage solution is substantially similar to the applicant technology and that the same ICD-10-PCS code may be used to describe the use of CINA Head.

Avicenna.AI Partners With Canon Medical on AI Stroke Detection

Logo Canon

Paris, FRANCE – November 10, 2020 – Medical imaging AI specialist Avicenna.AI today announced a partnership with Canon Medical Systems Corporation to deliver a fully integrated stroke detection AI solution for Canon’s Automation Platform.

With this partnership, Avicenna provides Canon with its FDA-approved CINA Head triage AI solution for neurovascular emergencies, which detects two of the leading causes of stroke – intracranial hemorrhage (ICH) and large vessel occlusion (LVO) – from CT-scan imaging.

Using a combination of deep learning and machine learning technologies, CINA Head automatically detects and prioritizes acute ICH and LVO cases within 20 seconds, seamlessly alerting the radiologist within their existing systems and workflow.

CINA’s ICH detection capability was validated using data from 814 cases conducted at more than 250 imaging centers across the United States, with 96% accuracy, 91.4% sensitivity and 97.5% specificity. The product’s LVO detection capability was validated based on 476 cases, with 97.7% accuracy 97.9% sensitivity and 97.6% specificity.

Toshiki Kato, General Manager of Healthcare IT, Canon Medical Systems, said, “Our partnership with Avicenna demonstrates our commitment to providing our customers with AI solutions that help them to improve the quality of care. With its proven ability to help radiologists to identify pathologies quickly, but also to highlight those that require the most urgent care, CINA Head is exactly the kind of innovative solution that we want to offer clinicians.”

Cyril Di Grandi, co-founder and CEO, Avicenna.AI, said, “Our partnership with a medical imaging modality leader like Canon Medical Systems is a testament to the excellent accuracy, sensitivity and specificity data that has been gathered during our validation phase. We look forward to working with our new partner to bring the benefits of our AI-powered stroke triage solution to the emergency room and beyond.”

About Canon Medical Systems Corporation
Canon Medical offers a full range of diagnostic medical imaging solutions including CT, X-Ray, Ultrasound, Vascular and MR, as well as a full suite of Healthcare IT solutions, across the globe. In line with our continued Made for Life philosophy, patients are at the heart of everything we do. Our mission is to provide medical professionals with solutions that support their efforts in contributing to the health and wellbeing of patients worldwide. Our goal is to deliver optimum health opportunities for patients through uncompromised performance, comfort and safety features.

At Canon Medical, we work hand in hand with our partners – our medical, academic and research community. We build relationships based on transparency, trust and respect. Together as one, we strive to create industry-leading solutions that deliver an enriched quality of life. For more information, visit the Canon Medical website: https://global.medical.canon.

The Steps to Build a Robust Clinical Application

Doctor explaining ct scan

Validation is a crucial step before a medical device (MD) is put on the market.

We can easily think that validating an artificial intelligence triage solution for radiology is an easy path: Yes/No.  There is a single pathology: it doesn’t require a lot of work!

However, it is a step with heavy responsibility. Whether it is to comfort us, as software developers, or to obtain certifications, such as FDA clearance or CE mark. Therefore, it’s an important regulatory requirement.

The goal is to prove to the medical community, and to the competent authorities, that our product is accurate, reliable, performant (perform as intended), fast, and above all: carries no risk for the patient!

This proof is made through statistical results, rigorously chosen, reliable, robust, and appropriate. Which, we obtain through validation studies by testing our algorithms on a sample of real-world medical images from the clinic. Furthermore, this sample is supposed to represent the targeted population. Of course, before we start the actual validation process, we need to know our MD at our fingertips – to get the facts.

3 important preliminary steps must be carried out:

  • Carry out bibliographical research:

This is a very long and tedious part, which aims to define the state of the art relative to the device we want to validate. The points that will be addressed are:

Search and define similar/predicate devices:

a) Know their limitations/dysfunctions (to exclude them from our products). Or, if we observe the same limitations in our products during the validation phase, this will give us something to discuss.

b) Understand statistical performances of the competitors: to set a benchmark for the performance and effectiveness we wish to achieve with our device or even surpass them.

– Appreciate the incidence, prevalence, target, and at-risk populations (age, gender). In short, to know the epidemiology of the pathology targeted by our MD.

– This library will allow us to set up a solid validation protocol adapted to the MD to be validated by choosing the right methodologies and adequate and appropriate statistical methods.

  • Write the validation protocol (the Study Summary) according to the point quoted just before.

  • Constitute the database:

This step is also very important, and sometimes the longest. As we know, we will not be able to constitute a database representative of the whole targeted population. Nevertheless, given the bibliography we have made, we can target some characteristics to get as close as possible to it.

Indeed, if we know that 90% of the pathology we are targeting affects an Asian male population of 70 years old and over, it’s clear that we are not going to look for data on Caucasian women under the age of 30. It seems obvious, but way less when we are under pressure to finalize the validation.

Another important point is to collect clinical data acquired in a large number of clinical sites (multi-center data), to cover as many as possible different populations and acquisition protocols.

Also, we need to set the performance we want to achieve. This can be the result of the bibliographic research we have done on similar devices. So, we can already decide not to perform less well than the competitor. Common sense will also push us to say that we do not want, for example, sensitivity and specificity lower than 90%. Lastly, this is also an FDA request for sorting MDs.

These requirements will allow us to calculate the minimum sample size of our database: sample size calculation. This size is indicative only, it is a minimum value. It will inevitably increase according to the criteria we wish to explore during the validation.

The performance is first calculated on the totality of the data, but also stratified ones:

1) Scanner makes:

We will try to obtain data from all the major scanner manufacturers. For each scanner, we want to have as many models as possible, with all the detectors rows available, etc.

Therefore, the more subgroups to be explored (validated), the sample size for each group will have to be increased to have significant statistics.

Hence, the minimum sample size that we have calculated can increase quickly.

2) In addition, we will explore the acquisition protocol recommended in current clinical practice:

We will collect the data trying to get as close as possible to it. Such as aiming at the recommended slice thickness, radiation doses (kVp, mAs, etc.), and more.

3) We NEED to have equivalent portions in terms of sex (depending on the pathology and target population): ~50% for each group

4) Age too.

Always refer to the target population. Of course, we can have small samples in young populations (<20 years old) as long as we can justify that the targeted pathology does not affect this population.

5) …. And more parameters.

6) Also, this is where it gets more complex:

If you are aiming for an FDA clearance, you should know that at least 50% of the data must be in the US. This could be not an easy task, especially if you want to apply IN ADDITION to all the selection criteria described just before.

7) Finally, the location of the pathology must be taken into account.

For example, the large vessel occlusion in the anterior circulation affects the M1 MCA, proximal M2 MCA, distal M2 MCA, and ICA. So, the data with positive LVO must be equally distributed in these groups, for a stratification that holds the road. BUT, as we have done our homework (i.e bibliography), we know that the LVO affects the MCA more than the ICA, so we can justify a smaller sample for the latter category.

Here, I have only spoken of the preliminary parts.

Once, all these steps are done we start the actual validation.

The main difficulty of this part is to find radiologists specialized by type of pathology to carry out the validation. Whether it is a quantitative or qualitative validation or just to establish the ground truth.

For the FDA, the operators must be US board-certified radiologists (for our indication of use). In addition, to establish the ground truth, a minimum of 3 physicians must be involved.

The validation process can be long, as it depends on the availability of the physicians, and the huge amount of clinical data they will assess.

When the statistical results are obtained, we validate (or not) the performance of our medical device.

If the statistics are not good (lower than the limit we set beforehand); if processing errors appear during the validation; if bugs, display, or calculation problems are found; then, the validation stops, and the software goes back into development!

Who still says that validating a triage software is easy?

At Avicenna.AI we are fortunate to have an experienced team, having already carried out such validations, in a changing and increasingly demanding environment.

This has allowed us to quickly put in place rigorous validation studies, required to obtain the FDA for our LVO (large vessel occlusion) and ICH (intracranial hemorrhage) triage software.

This expertise is also used for the construction/training of our algorithms. And, has allowed us to obtain applications with high performance and effectiveness, robust for safe and accurate use. which is for us the key to the acceptance of AI algorithms in clinical routine.

Do you want to know how we validate our products? Access our youtube channel and watch Angela Ayobi, our Clinical Affairs Engineer goes through every step of the validation process.

Why triage for Avicenna.AI?

Peter Chang Interview

We all recognize that artificial intelligence plays an increasingly prominent role in the medical field today. Each day more and more software applications are being developed to support physicians and facilitate their work. So what makes Avicenna stand out from the rest? 

To begin, a key focus of the Avicenna portfolio today lies in a specific area of healthcare focused on emergency triage. This strategic vision is motivated largely by understanding the intersection of what AI technology is capable of doing today and what physicians are most likely to find useful. By carefully designing applications that enhance the physician workflow, we are acknowledging that at least the in immediate future, AI will play a very focused role in augmenting patient care without replacing autonomous human decision making. 

Having made this observation, one natural and powerful role AI can play is to assist human physicians in the triage of patient care. In this framework, AI software is used to identify patients with positive, urgent findings that need to be addressed promptly. All findings and diagnoses made by the AI system are ultimately reviewed by a human physician before final treatment decisions are made. The synergy afforded by this system between the human physician and AI will ultimately lead to improved patient care and outcomes.

Our solution does not stop there. In the triage marketplace, there remains significant room for improvement. One of the key features missing in all currently available commercial solutions is the ability to perform a simple comparison between a historic examination, that is, the ability to not just detect hemorrhage, but to characterize whether the hemorrhage has gotten bigger or smaller. In most academic practices, the vast majority of all exams are follow-up studies and so the presence of a finding is usually not innately an emergency on its own; instead; it is the presence or absence of change that physicians care most about. 

Based on this discussion, the natural next evolution of AI software is a type of algorithm whereby humans no longer need to participate in the interpretation process. This new type of application is certainly in my opinion the next most important, most exciting opportunity for AI. The idea here is to develop very sensitive algorithms, capable of detecting any anomaly present on the screen. Such a tool, if calibrated properly, may be used to screen all patients before human interpretation; depending on algorithm confidence, one may imagine that a subset of normal patients may not need to be evaluated at all by a human physician. Freeing the attention of busy physicians to now focus on those select few patients that need it the most, this system may realize what the economist Jean Fourastié once claimed as “the machine that leads man to specialize in the human”.

Dr. Peter Chang – Director at UCI Center for AI in Diagnostic Medicine

Daniel Chow’s Interview

Daniel Chow Interview

Daniel Chow is a UCI Health radiologist who specializes in neuroradiology and diagnostic radiology. After obtaining his Medical degree at David Geffen School of Medicine at UCLA  he works in the department of radiology of Columbia University Medical Center. Today he is the co-Director of Center for Artificial Intelligence in Diagnostic Medicine, Radiological Sciences. During the ISC (International Stroke Conference) 2020 seminar we had the opportunity to realize an interview with Daniel Chow and discuss about AI in daily routine of clinical practice. 

What do you think AI will  bring to your clinical practice?

The question for me would be what the AI is going to bring us in the short term, and then in the medium and long term. In the short term, I think AI is going to help me as a radiologist to minimize the tedious day to day tasks. We’ve already been doing simple forms of machine learning, for example, that I pull up an MRI study I have my automate hanging protocol and my application will have the correct template up for me. But, what do I find tedious or what can I diagnose in a split second and not even think about? For example, I think of things I would have to do if I had a tumor to measure or if I have a trauma patient with the hemorrhage and I’m following the hemorrhage that I’m looking at. What is the size and doing those quantifiable metrics, would help you right now.

Very often when you follow patients and you have to visit a lesion , the work is long and tedious. These are very easy things that AI could solve in the short term.

And again, this is clinical routine, I am not talking about any fancy reconstruction where we are more in long term. At that point we will get to all the really fun things that we hope AI will do. Such as combining EMR or combining pathology, doing actual diagnosis or prognostication of prediction.

In our generation, in short term,  AI is going to be fully in our workplace. We’re going to be using it and it’s going to make us more accurate, more objective and more reproducible.

When you talk about short term, do you think the actual product are seamlessly integrated in the workflow ?

Currently, no, at least for everything I’ve had to see so far. I have a joke that if I have to click more than two buttons, I’m not going to use it. Often I have to push the study to a better station, open it and initialize it. In a recent setting that works better, and the day-to-day framework where I deal with 50 to 80 cases a day, we don’t really have the time.

Right now, the integration is lacking. But, for viability I’m looking for a tool that involves minimal clicks or means.  However, we are going in the right direction, several companies such as Avicenna understood, they provide a reliable and easy-to-use solution.

Which point do you evaluate in AI product ?

It’s hard to say because it depends on the AI  product and what it’s used for. For example, if it’s a triage tool, a screening tool, I’m going to go evaluate that based on how many false positives there are, how many false negatives they turn on time. I don’t want to be called for every single false positive that’s taking up too much time. And for that, no, I wouldn’t mind having false negatives because I’m going looking to the risk but I would want to minimize the false positives. 

For a measurement tool I would try to look at how well does the mask from the AI tool measure compare to my own measurement. Therefore it depends on the kind of tool, it’s the ultimate thing. A lot of people are not asking is is actually going to improve outcomes. 

What is the benefit to overall survival ? 

 “ The goal is to help patients be.”

There is no difference in overall survival. Let’s say I have a tool that makes me faster, a tool to measure better. If there is no improvement in survival or disability or outcomes, then it really is only a convenience. The idea is really to improve that because ultimately the goal is to help patients be.

Avicenna.AI secures FDA clearance for its CINA neurovascular imaging Tool

Brain CT scan

CINA supports emergency room triage for intracranial hemorrhages and large vessel occlusions.

Paris, FRANCE – July 14, 2020 – Medical imaging AI specialist Avicenna.AI today announced it has received 510(k) clearance from the US Food and Drug Administration (FDA) for its CINA triage AI solution for neurovascular emergencies. The FDA’s decision covers CINA’s automatic detection capabilities for both intracranial hemorrhage (ICH) and large vessel occlusion (LVO) from CT-scan imaging.

Stroke is a leading cause of death in the US, with more than 795,000 strokes resulting in more than 100,000 deaths each year. It is estimated that up to a third of the most common type of stroke are caused by LVO, when a clot blocks the circulation of the blood in the brain. Around 1 in 10 strokes are thought to be caused by ICH, or bleeding that occurs inside the skull. Using a combination of deep learning and machine learning technologies, CINA automatically detects and prioritizes acute ICH and LVO cases within 20 seconds, seamlessly alerting the radiologist within their existing systems and workflow.

Dr. Peter Chang, radiologist and co-founder of Avicenna.AI, said, “When dealing with a stroke, time is of the essence and being able to prioritize effectively is critical to saving lives and improving outcomes. Not only does CINA helps radiologists to identify pathologies quickly, but also to highlight those that require the most urgent care.”

CINA’s ICH detection capability was validated using data from 814 cases conducted at more than 250 imaging centers across the United States, with 96% accuracy, 91.4% sensitivity, and 97.5% specificity. The product’s LVO detection capability was validated based on 476 cases, with 97.7% accuracy 97.9% sensitivity, and 97.6% specificity.

Cyril Di Grandi, co-founder, and CEO of Avicenna.AI, said, “We’re excited to have received FDA clearance for CINA and we are looking forward to working with emergency departments and stroke centers across the United States to help improve detection, decision-making and patient outcomes. As a triage AI tool that identifies multiple pathologies, we believe that CINA delivers more value than AI tools or algorithms that only target a single condition.”

CINA is the first in a family of AI tools for emergency radiology being developed by  Avicenna.AI. Subsequent products spanning the trauma and vascular fields are expected to be unveiled in the next 12 months.