Community Research and Development Information Service - CORDIS

H2020

Neuromapper Report Summary

Project ID: 775070

Periodic Reporting for period 1 - Neuromapper (Cloud collaborative open platform for advanced brain analytics)

Reporting period: 2017-05-01 to 2017-10-31

Summary of the context and overall objectives of the project

Understanding the brain anatomy is one of the biggest challenges in science due to the difficulty to observe it in vivo and its enormous complexity. Neuroimaging is the only available tool for non-invasive in vivo quantification of brain pathology and injury. Despite the advances in computer infrastructure, data transmission and imaging techniques cracking the brain requires a collective effort.
The standard way to exchange medical information is still through devices such as CDs or USB drives that do not offer compliance with privacy regulations. Existing systems have serious interoperability issues when importing and visualizing data from different scanners due to the incompatibility between vendors or software providers. In addition to this, analysis of brain images still relies on a specialist’s educated evaluation. Much of the valuable information contained on brain images is severely underused due to the lack of advanced quantification and image visualization tools that can help researchers better identify biomarkers and study large datasets.
This greatly affects the success and reproducibility of research efforts in neuroscience, which hinders not only our understanding of the brain but also our capabilities to develop new treatments for brain disorders, as well as improving the accuracy and speed of diagnosis.

As of 2011, an estimated 164.8 million people are affected by brain disorders in Europe, almost 38% of the EU population. They have a huge impact on the EU economy, with an estimated cost of €798 billion each year. The estimated spending on brain research in Europe surpasses €4 billion annually, where 79% comes from the pharmaceutical industry (€3.3 billion) and the rest from the public sector.

The objective of Neuromapper is to create a new gold standard for collaborative research on neuroscience by providing an environment for secure data sharing in the cloud as well advanced data management, analytics and visualization tools. The platform will be open to external researchers so that they can develop new image processing tools and share them with the community. As the community grows and the database of brain images reaches a critical mass, Mint Labs will be able to implement big data and machine learning techniques. This will allow the development of a decision support system (DSS) able to automate the diagnosis process and find new markers of brain disorders leading to early diagnosis and the development of new treatments.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The market of Neuromapper was analyzed both quantitatively and qualitatively to better understand the market, its size, the relevant countries. Competitors were examined to ensure that the solution offered various competitive advantages over the competitors. The market study allowed us to know the potential barriers and our potential market size.

A market penetration plan and communication strategy were drafted according to the results of the market research to maximize the use of the product and reach the desired market size. The very detailed communication plan that was created is a step-by-step guide to reach the target growth, sales, and profits as it optimizes all marketing and sales activities of the company. Moreover, an experienced and senior business development professional was hired to support and enhance the efforts. Potential partners were also examined as it might lead to further growth for the sales.

Moreover, the regulatory and intellectual property rights related requirements and concerns were examined. Following the results of the regulatory analysis, the company has started the processes to be compliant with FDA regulations and be ISO 13485 certified. Regarding the IP rights of the Neuromapper, we will be applying to patents and follow all necessary regulations for IP protection and for the protection of our system.

The technical analysis on the feasibility of the product examined different imaging techniques and AI techniques to come up with a product development plan and detailed which technologies will be used and how to achieve the results.

Lastly, the resources, financials, and HR were studied. This allowed us to grasp the personnel & expertise required to create the decision support system in addition to the potential sales and profits of the future with the successful end of the project with the accurate decision support system. The previous estimates of the company had to be updated as the decision support system allows for much higher growth and revenue with the wide-ranged applicability and use of the product.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

Our aim is to disrupt the current state-of-the-art and create an AI-based decision support system system for all neurological diseases. Our decision support system will be the first in the world fully capable of handling different cases, diseases, imaging modalities, etc. as the system will be responsive to neuroimages from different scanner vendors, scans with different parameters, all stages of the diseases, and many other variables.

Neuromapper will be an all-in-one solution. It will automate the analysis process of patients’ data and enable advanced analysis of heterogeneous data. This will allow for a more efficient and precise monitoring of treatment effectiveness, reducing labour costs and time. But more importantly, it will detect at sooner stages the failure of the trial, reducing costs greatly.
Our decision support system will help doctors to identify new biomarkers and to make better diagnosis at early stages, reducing the error rate and enabling them to provide better treatments to their patients, in many cases preventing live threatening conditions. It will also be able to predict the likeliness of developing a disease based on genetics, lifestyle and environmental factors. As a result, it will be easier to find patients that match all the requirements for their recruitment on clinical trials, which will bolster the success of the trials.

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