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Robert Lauritzen

Smart Protocol

Denmark
Market: Medicine, Artificial Intelligence
Stage of the project: Prototype or product is ready

Date of last change: 09.03.2020
Min investment
$  150.000
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$ 500.000
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Idea

Using machine learning to automate and adapt brain MRI acquisition

Current Status

Cerebriu's first product has received a CE mark. We are in negotiations with the first commercial implementation of the technology.

Market

Recent data show that the number of scans undertaken is growing for all main modalities (e.g. x-ray, computerized tomography (CT), magnetic resonance imaging (MRI), ultrasound), reaching an estimated average growth rate of 10.5% by 2022. Among these, MRI is a widely used medical technology and is often employed as the preferred imaging tool for disorders of the neurologic conditions, as it can better delineate soft tissue structures than either plain x-rays or CT. In the US, in 2017, there was a total number of 36M MRI scans (17.7M of these taken in hospitals), whereas in the EU, approx. 33M had been taken4. Until 2024, it is expected a worldwide growth rate solely for MRI scanning of 6.6%. Both availability and use are clearly ramping up in most of the developed countries - the United States (US) have one of the highest densities of MRI units, as nearly 38 such units are available per every million of its population, followed closely by Germany and South Korea (34 and 29 units respectively). An estimated 50,000 MRI scanners are being used worldwide, across 16,500 hospitals, increasing by 5,000 MRI units per year.

Problem or Opportunity

Recent data show that the number of scans undertaken is growing for all main modalities (e.g. x-ray, computerized tomography (CT), magnetic resonance imaging (MRI), ultrasound), reaching an estimated average growth rate of 10.5% by 2022. Among these, MRI is a widely used medical technology and is often employed as the preferred imaging tool for disorders of the neurologic conditions, as it can better delineate soft tissue structures than either plain x-rays or CT. In the US, in 2017, there was a total number of 36M MRI scans (17.7M of these taken in hospitals), whereas in the EU, approx. 33M had been taken4. Until 2024, it is expected a worldwide growth rate solely for MRI scanning of 6.6%. Both availability and use are clearly ramping up in most of the developed countries - the United States (US) have one of the highest densities of MRI units, as nearly 38 such units are available per every million of its population, followed closely by Germany and South Korea (34 and 29 units respectively). An estimated 50,000 MRI scanners are being used worldwide, across 16,500 hospitals, increasing by 5,000 MRI units per year.

Importantly, neurological disorders are an important cause of disability and death worldwide. Globally, in 2015, they caused 250M DALYs (disability-adjusted life years), comprising 10.2% of global DALYs, and 9.4M deaths, related to 16.8% of global deaths. Their burden has increased substantially over the past 25 years because of expanding population numbers and ageing, despite substantial decreases in mortality rates from stroke and other brain conditions. In fact, a significant number of patients could be saved if these neurological disorders are diagnosed in a faster and more efficient way, through medical imaging support, which is vital to improve global healthcare outcomes.

Solution (product or service)

Due to the flaws experienced by radiologists, ML in medical imaging is widely recognized as a highly promising solution. CEREBRIU is focused on helping neuroradiology work smarter, since it is where we have a strong footprint and higher competences, as well as the fact that the brain has an unequivocal priority when compared with other body parts. Our novel ML solution – Apollo – detects high priority brain conditions in real-time to adapt MRI protocols and patient triage, becoming the backbone of CEREBRIU solutions.
The key novelty of Apollo is smart protocol. Smart Protocol identify whether there are indications of pathologies in real-time (during image acquisition). These results are fed to a decision tree that based on scan findings, recommends sequences/actions in real time (right sequences, right first time). These analysis are available downstream to be used for visualisation and reporting.

Competitors

There is currently no AI-based solution for brain MRI detection, which has been CE approved and gained any broad and unanimous use in the general clinic. Most of the technologies focus on specific diagnoses (e.g. bleeds) and support for pharma trials and R&D delivering narrow outcomes. Specific vendors cater to certain disease/pathology areas in the field of brain MRIs: Specifically, within dementia, Quantib/NeuroQuant offer solutions that allow segmentation of MRIs for both volumetry and white matter lesion segmentation, while Icometrix offer white matter lesion segmentation related to multiple sclerosis. None of the 100+ AI image analysis providers interviewed by Signify Research14 enter the actual patient examination step, since they do not deliver real-time image analysis besides focusing either on a very specific brain condition or in a general perspective leading to low accuracy rates. This is precisely one of Apollo´s USPs.
Each of the Apollo product features has distinct uniqueness to existing solutions as described hereafter, and combined they provide a unique value proposition with significant productivity and quality improvements in the general radiological clinic (e.g. improved clinical efficiency and scanning time, less time spent by radiologists, increase on patient care experience, higher safety through reduced use of contrast chemicals, support for non-radiologist reading, etc). Unlike competitors focusing on diagnostics during reporting (or needing to generate more data during scanning), we offer workflow efficiency gains by bringing new insights, ensuring clinically relevant accuracy above 95% and letting the radiologists focus on their core expertise. Apollo will be a breakthrough on the AI market for medical imaging as current products do not focus on improving the overall workflow with true real-time decision support. Generally, existing algorithms are too slow and compute power-intensive, as opposed to deep learning-based algorithms, and they focus on the quantification of a few biomarkers, which is useful in a narrow often research-based context, but less effective in the general clinic. This makes Apollo unique and well ahead compared to the available solutions. The key value streams benchmarking is described below:
1) Smart Protocol. Today, there exists no solution to adapt the protocol during scanning and there are no available commercial products that handle automatic determination of brain MRI protocols. Current state-of-the-art includes different protocols for different pathologies (e.g. epilepsy protocol, granuloma protocol etc.) which are manually selected. Solutions like GE MAGiC/Siemens GOBrain that acquire several fixed sequences are in validation phase. However, special sequences like MRI spectroscopy, or time-of-flight need to be manually set. Currently there are no real-time protocol modification tools available. Most of the modifications are done manually by the radiographers together with a consulting radiologist. In the absence of radiologists, the most elaborate protocols are usually followed. Solutions like ones offered by CorTechLabs/Icometrix may be used to determine the underlying pathology but they do not provide any real-time info, and only offer identification of one specific biomarker, i.e., white matter hyper intensities.

2) Image Reporter. There are many existing image viewer solutions and some quantification solutions that can quantify segments and biomarkers in the brain (e.g. neuroQuant, which provides measurements of total brain volumetry, mostly regarding volume/atrophy of brain structures). Existing solutions are, however, too slow to be relevant during scanning but also too specific (focusing on quantification of brain segments) making it necessary to have several independent solutions. While most of the brain MRI solutions like Icometrix, CorTechLabs etc. offer image reporting, they only rely on appending DICOMs (Digital Imaging and Communications in Medicine) with PDF reports, slowing down the process.
The Image Reporter is a real-time image viewer guiding radiologists to focus on findings whereas it prioritizes the most acute ones. This is mainly applied to help the clinicians to immediately see findings in current scans and to convey the recommended sequence, if any, for the radiographer to perform (see Smart Protocol). Apollo offers additional flexibility on navigating through the findings of segmented MRI images generated by deep learning, on the way to identify a vast range of brain conditions in real-time scan. This information is also stored in PACS as overlays or secondary captures supporting radiologist reporting.
3) Triage Advisor. A few systems focus on triage (e.g. AIdoc) for acute cases, but mainly within the CT modality. Commercially, there are no brain MRI based triage reports being used. The closest AI-based triage solution available for brain is by AIdoc for the emergency condition intercranial haemorrhage (focus on emergency/trauma conditions) based on head CT scans. Enlitic provides a triage solution for lung diseases that is based on deep learning in lung imaging such as chest x-ray and chest CT combined with unstructured information such as reports.

Advantages or differentiators

Smart reporter differentiates itself by providing an automated way of deciding the most optimal protocol, adapting the current one in real-time based on the first sequence acquisition. To sum up, it identifies the most relevant sequences and adjusts the protocol on the fly. This helps ensuring the reduction of irrelevant data to be analysed after. If there are no findings (up to 30% of all cases), the minimal data set is taken, reducing the burden during image interpretation and reporting. We detect candidate findings e.g. pathologies based on T2-FLAIR (weighted-Fluid-Attenuated Inversion Recovery) technique, diffusion-weighted imaging (DWI) and susceptibility weighted imaging (SWI).

The Triage Advisor operates in the general clinical environment able to provide input to RIS on prioritization of cases, but also to immediately notify about any acute conditions while the patient is in the scanner (e.g. prioritizing treatment before leaving the site). CEREBRIU offers the first triage solution that is based on brain MRIs. Our triage advisor will address the general radiology clinic along with all the emergency conditions that may be detected on brain MRIs. This is aimed at improving general neuroradiology productivity that none of the existing competitors offer.

Finance

We expect to be break-even during Q4 - 2021

Business model

Subscription model of 10-20,000 usd per MRI scanner.

Money will be spent on

Building sales organisation to establish health economics., expanding R and D, and product expansion.

Offer for investor

will be a part of current raise of 2m USD at 15% dilution of the company.

Team or Management

Risks

1. Access to radiologists since they are key to determine the requirements and get the best fit for use & purpose
2. Lack of awareness from clinicians and radiologists and absence of guidelines for brain-MRI support by using ML
3. New ML-based software developed by competitors focusing on neuroradiology
5. Unpredictable political climate that may difficult the ethical approval of data access

Incubation/Acceleration programs accomplishment

N/A

Won the competition and other awards

4th place MSD challenge MICCAI 2018

Invention/Patent

UK Patent Application No. 1909212.1
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Idea
Current Status
Market
Problem or Opportunity
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Finance
Invested in previous rounds, $
Business model
Money will be spent on
Offer for investor
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Incubation/Acceleration programs accomplishment
Won the competition and other awards
Invention/Patent
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