Report
Teaser, summary, work performed and final results
Periodic Reporting for period 1 - ANIMATION (Automatic Neurovascular Image Analysis and Quantification)
Teaser
Stroke is the third most common cause of death in developed countries, exceeded only by coronary heart disease and cancer. Annually, 15 million people worldwide suffer a stroke. Of these, 5 million die (one person every 9 minutes) and another 5 million are left permanently...
Summary
Stroke is the third most common cause of death in developed countries, exceeded only by coronary heart disease and cancer. Annually, 15 million people worldwide suffer a stroke. Of these, 5 million die (one person every 9 minutes) and another 5 million are left permanently disabled, placing a large burden on family and community. Strokes are caused by a disturbance of blood supply to the brain; either the blood supply is blocked (ischemic stroke) or a blood vessel within the brain ruptures (haemorrhagic stroke). Medical imaging, such as CT, is of utmost importance for early diagnosis and choice of treatment. Moreover, advanced and faster decision making will substantially improve the quality of life of patients that suffered an acute stroke. Additionally, new treatment options for patients with acute stroke are expected to be developed. For clinicians, this will lead to a more complication decision-making process regarding the choice of treatment, meaning they need to be better informed. Computational support can almost instantly provide the required information accurately, illustrating the need for those computational decision support systems.
Nico-Lab BV, a spin-off company from the Academic Medical Centre (AMC) University of Amsterdam, aims to introduce a novel analysis toolbox which contains algorithms to automatically manage, quantify and analyse radiological images of acute stroke patients. This toolbox has the potential to strongly boost the image analysis field and minimise the burden of strokes. It consists of tailored algorithms to automatically analyse and quantify different aspects of neurovascular medical images
Nico-Lab BV is an ambitious and technologically advanced company. Its mission is to support clinicians worldwide in decision making to determine the choice of treatment for their acute strokes patients. Nico-lab will do so by providing innovative high performance algorithms that automatically analyse and quantify radiological images. Additionally, Nico-Lab can help researchers in trials with management of radiological images (cloud storage, online analysis, etc). Now, the next step is to take the toolbox from clinical trials into clinical practice, where the toolbox will support neuro-radiologists, neurologists, and neuro- surgeons in their decision making. In this SME phase I project Nico-Lab will therefore investigate the commercial feasibility of its valuable toolbox.
Work performed
Nico-lab has developped, tested, validated and implemented software into a Tool Suite for the Automated Quantitative Neurovascular Images Analysis. The most important elements of this suite are:
1. The establishment of final infaction volume after a stroke;
2. Quantification of the Subarachnoidal hemorrhage ;
3. Quantitave analysis of the Thrombus perviousness (how much blood can pass the blood clot in the vessel);
4. Analysis of the A-ASPECTS;
5. Automated collateral scores, measuring the development of collateral vessels that are formed around a clogged blood vessel.
All methods are validated in clinical trials and many of them have recently been published in a.o. Journal of Medical Imaging, Journal of Neurosurgical Intervention, American Jounal of Neuroradiology (AJNR) and Journal of Neuroradiology.
In the meantime Nico-lab offers its services to clinical trial centers for the analysis of the collected images from CT-scan and MRI. This results in revenues is also an opportunity to use this environment for the further development of software. Nico-lab is aiming for the world-wide roll-out of the software, which will be implemented in standard protocols to be used by physicians as a decision support system in the diagnosis and treatment of stroke (either ischaemnic or hemorrhagic).
Final results
By the year 2020 about 50 million people will suffer a stroke. That is disastrous from a medical point of view, but also from a societal perspective as the costs are extremely high and it places a large burden on families.
When a patient suffers from an ischemic stroke the “traditional†treatment is to dissolve the blood clot with medication (thrombolytic agents), however, thrombolysis only has a limited chance of success for patients with large strokes. A new treatment, Intra Arterial Treatment (IAT), is becoming more common. This new treatment will only benefit specific patients, dependent on the location of the blood-clot and the maximum infarct volume. Thus, there is a need for patient stratification. To do so, clinicians require accurate information about bleeding or infarct volumes and locations. As every second counts with stroke patients, time is of the essence. Computational support can almost instantly provide the required information accurately. This can support a clinician in making the right treatment choice. This can improve patient quality of life, by reducing the adverse side effects of a wrong treatment choice. Making the wrong decision however, when no benefits of treatment are expected, would result in unnecessary costs. For intra-arterial mechanical extraction, this would be a waste of €25k per treatment. Our algorithms can help prevent these kinds of mistakes.