CENTOGENE Accelerates Rare Disease Diagnosis with new Artificial Intelligence Solution

In a whitepaper published today, CENTOGENE revealed the progress it is making with its in-house artificial intelligence (AI) program to accelerate rare disease patient diagnosis. The whitepaper demonstrates how the Company’s latest AI initiative – a variant prioritization tool – has not only accelerated the diagnostics process but also outperforms other tools with regards to sensitivity and specificity for flagging ‘pathogenic’ and ‘likely pathogenic’ variants.

With what is believed to be the world’s largest curated data repository, which includes epidemiologic, phenotypic and heterogenetic anonymized data of more than 420,000 patients and >7.3 million variants, CENTOGENE is ideally positioned to leverage its ‘big data’ for AI-driven accelerated diagnosis.

“Big data is the key enabler of artificial intelligence since AI systems need enormous data sets to train algorithms. The better and more comprehensive the data, the higher the predictive power and accuracy of results from artificial intelligence,” commented Dr. Volkmar Weckesser, CENTOGENE Chief Information Officer. “CENTOGENE finds itself in this enviable position with our data repository CentoMD® - our ‘big data’. We have demonstrated that by combining what we believe to be the world’s largest database of genetic information with an AI-based variant prioritization solution, we outperform other tools available and ultimately accelerate the diagnosis of rare disease patients.”

Carsten Ullrich, Director of Artificial Intelligence, added: “CENTOGENE’s diagnostic and pharmaceutical solutions rely on the extensive knowledge and insights held in our data repository. Artificial intelligence enables us to find relationships faster, draw more exact conclusions about relationships in the data and discover patterns that cannot be found with traditional methods.”

In the diagnosis of rare disease patients, variant prioritization is a vital step in discovering causal variants in order to identify disease-causing mutations. Variant prioritization accelerates and simplifies variant interpretation because the results enable the interpretation of variants of unknown significance. Prioritization scores enable the diagnosis of a patient and, indeed, rare disease diagnosis relies heavily on variant prioritization scores in order to determine which variants are likely to affect the function of genes.

Read the whitepaper