Personalize immunotherapy

Ellogon.AI assists medical experts in selecting the right patient for cancer immunotherapy. We do this by fast and accurate quantification of relevant biomarkers by use of a breakthrough deep learning approach, developed together with the Netherlands Cancer Institute (NKI). Our CE certified solution EIDOSTM is fully integrated into existing medical treatment protocols.

Our Unique Features

Artificial Intelligence

In the next decade of personalized medicine in cancer and pathology, AI will play a pivotal role in translating “super-human” complex patterns into actionable insights. To achieve this, the recipe is clear: big data, big AI expertise all combined with in depth medical expertise. But what does “big” even mean? For instance, is it realistic to expect a pathologist to identify millions of cells in just a single gigantic H&E slide to train the AI algorithm, let alone repeat that for thousands of slides? This unrealistic level of “big data”, is the guidance today’s machine learning algorithms require. Under the leadership of expert pathologists and medical researchers at the world-class Netherlands Cancer Institute, and of expert professors of AI at the University of Amsterdam, Ellogon.AI has the answer. Our proprietary and groundbreaking Exclusive Deep Learning Technology does not just blindly copy the pathologist but extrapolates. By exploiting sparse-shot and self-supervised neural dynamics during the extremely high-dimensional optimization, Ellogon’s Exclusive Deep Learning recognizes millions of cells and “super-human” patterns in raw H&E and PD-L1 stained histopathology images; it quantifies immunogram biomarkers including Tumor Infiltrating Lymphocytes (TIL’s), PD-L1 inhibition checkpoints, and Tumor Mutation Burden (TMB); it delivers personalized response to immunotherapy of which the patient will benefit from treatment; validated thoroughly in multi-center medical data and cohors worldwide; quality assured to meet CE-certified standards of excellence and quality according to current regulation standards.

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Seamless integration

We have designed our solution in such a way that our customers do not need to acquire or install any hard- and/or software. Our solution uses a GDPR compliant cloud architecture through which digitized images of tissue can be uploaded and analyzed by our breakthrough deep learning solution EIDOSTM. Through a personalized secure web-portal, quantified biomarkers like TIL’s, PD-L1 and Tumor Foreignness, will be made available. A so-called ‘heatmap’ containing lymphocytes and segmentation of tissue areas like stroma and tumor, can be used by medical experts to verify the outcome of EIDOSTM.

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About us

The Ellogon.AI team shares a single passion: contribute to improving the outcome of cancer. To realize this we have put together a team with all the right ingredients. A proven track record in building organizations in healthcare up to realization of exits. Cutting edge knowledge and expertise in applying AI in medical imaging. And last but not least, senior expertiser in immunotherapy.

The core Ellogon.AI team is strengthened by two crucial partnerships. In 2020 we closed a partnership with the NKI, currently ranked #6 worldwide in cancer research. This partnership not only gives us access to required data for training and testing our AI solution, but also in-depth knowledge on immunotherapy and how to fit our solution within clinical practice. Our second partnership is with the UvA, within the top 5% globally Universities for Artificial Intelligence and home of the prestigious institution of the Innovation Center for Artificial Intelligence (ICAI). Together with the UvA and NKI we have developed a breakthrough AI approach able to deal with the enormous amount of data related to pathology slides and resulting in very high accuracy of patient selection for immunotherapy.

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Artificial Intelligence will be of great value in the pathology workflow by optimizing the selection process of patients who will possibly benefit from immunotherapy

Hugo Horlings, pathologist at NKI

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