Owkin Shares New AI-Severity Score for COVID-19 integrating CT Images Published to Nature Communications

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Paris, France, January 27th 2021

COVID-19 vaccine distribution has begun across the globe, while many countries are still struggling with the rampant rise of infections. Owkin, a FrenchAmerican startup pioneering AI and Federated Learning in medical research, has been focusing its COVID-19 research efforts on aspects of the pandemic that still require much public health attention, despite the arrival of an effective vaccine. Efforts to support frontline health systems as they devote their resources to the influx of COVID-19 related hospitalizations have resulted in the AI-Severity Score, published in Nature Communications this week.

This machine learning model, trained on multimodal data sets that include CT scans of the lungs (a routine procedure upon admission), is plug and play and able to predict the severity of a patient’s disease prognosis with a performance that surpasses all other currently published score benchmarks. The use of these scores supports hospital resource management and planning, a sometimes overlooked function that, when managed well, saves lives. This research was made possible through a consortium, called ScanCovIA, made up of Institut Gustave Roussy, Kremlin-Bicêtre APHP, Owkin, and Digital Vision Center of CentraleSupélec and INRIA.

Additionally, Owkin has been developing other machine learning models to discover more coronavirus epitopes that are most likely to be effective in future vaccines As the virus continues to mutate, we don’t yet know how long the current vaccines will remain efficacious or if, like the flu, they will require annual or semi-annual development. Furthermore, it may be possible to develop vaccines for genes within the virus’s DNA that are more stable, and less likely to mutate. Epitope prediction can speed vaccine development by narrowing the field of epitopes to test in the lab, and it can diversify our defences against the virus’s future mutations. Furthermore, these models can be deployed outside vaccine research; they can also be used in oncology research.

The ultimate aim of machine learning for epitope discovery is to have a better understanding of the immune response—these features of the model have their place across the spectrum of precision medicine research.

About Owkin

The French-American startup, which was co-founded in 2016 by Dr. Thomas Clozel, a clinical research doctor and former assistant professor in clinical haematology and Gilles Wainrib, Ph.D., a pioneer in the field of artificial intelligence in biology, has raised $70 million in venture capital. Owkin connects several of the largest medical research centres and pharmaceutical companies in Europe and the U.S. within a federated research ecosystem. Owkin has developed four key components to building this ecosystem: Owkin Loop (the network), Owkin Connect (the technology infrastructure), Owkin Studio (the AI software tool) and Owkin Lab (the expertise). Owkin Connect is a privacy-preserving, traceable, secure technology that allows the company to connect with research centres in the Owkin Loop network.

Using Owkin Connect’s federated learning approach, the data do not move, only algorithms travel. This enables insights from the data to be collectively shared while guaranteeing privacy for patients and compliance with data ownership. In October 2019, Owkin published in Nature Medicine its breakthrough analysis of tumour biology using an interpretable deep-learning model, called MesoNet. In February 2020, Hepatology published Owkin’s novel deep learning models to predict survival after hepatocellular carcinoma resection from histology slides. Most recently, in May 2020, following a winning entry to the data challenge organized last October by the Société Française de Radiologie et d’imagerie médicale (SFR), Owkin published its methodology to automatically measure muscular area from CT scans to assess sarcopenia in Diagnostic and Interventional Imaging. In August 2020, Owkin published its novel genomic analysis tool (HE2RNA) in Nature Communications.

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