This London-based medtech startup uses Machine learning to detect cancer, raises €20M

Kheiron Medical Technologies, a London-based medtech startup which uses Machine Learning to help radiologists detect early signs of cancer has raised $22 million (€ 20 million) in a Series A funding round. The investment was led by European V.C. firm Atomico, with participation from Greycroft, Connect Ventures, Hoxton Ventures, and Exor Seeds.

The company is planning to use the funding to scale the clinical validation of Mia further by conducting a series of tests, confirming its effectiveness and safety and roll-outs in Europe, the United States, and Asia. As part of this investment, Atomico Principal Irina Haivas will join the board.

Irina Haivas, a Principal at Atomico who is joining Kheiron’s board, said, 

We invested in Kheiron because we believe they have one of the best Machine Learning teams in the world but also because they have such a deep understanding of radiology and the clinical validation required to usher in this new era of cancer diagnosis and care.

Organisation with the best mind!

For the uninitiated, Kheiron Medical was founded in 2016 by Dr. Peter Kecskemethy and Tobias Rijken with the sole focus of supporting the work of breast radiologists with Machine Learning software.

It combines deep learning methods, radiology, and data science expertise to enable an entirely new level of diagnostics that are designed to detect cancers and improve patient health.

The team consists of some of the best minds in technology and radiology, including deep learning experts and engineers from the universities of Oxford, Cambridge, Edinburgh, McGill, KTH, University College London (UCL) and Imperial College, and leading clinical academics and radiologists from across Europe.

Mia

Kheiron Medical’s first product is Mia, a breast cancer screening solution that supports radiologists in reducing errors in breast cancer detection.

Also called Mammography Intelligent Assessment (MIA) it is designed to analyze standard FFDM mammograms in a breast cancer screening setting. It is the first product developed by a U.K. company to receive the C.E. mark for deep learning software in radiology.

Voted as Best New Radiology Software 2019

Furthermore, the software has demonstrated state-of-the-art performance in a multi-center clinical study and was voted Best New Radiology Software 2019 by leading radiology publisher AuntMinnie. It also has European regulatory approval to act as an independent second reader in double reading programs by making the same decision that screening radiologists are required to make.

Commenting on the investment, Kheiron CTO and co-founder Tobias Rijken say: 

“Clinical rigor is at the heart of everything we do. It is one thing to create an algorithm, and entirely different to make it useful in clinical practice when patient lives are at stake. The key to helping radiologists diagnose breast cancer more accurately for the benefit of women everywhere was finding investors who understood how to validate and scale products for global impact safely. The team at Atomico has a wealth of experience doing this at companies such as Skype, Uber, Spotify, and Virgin and we could not be more pleased to be working with them on this important problem.”

Trained with true screening data!

According to the company, Mia was trained with a variety of data sets consisting of extensive and diverse clinical samples that are presentative of true screening populations in the U.K. The company is currently working closely with the U.K. National Health Service (NHS) supported by government funding to deploy across extensive testbeds and pilot sites both in England and Scotland.

Funding from E.U. Horizon 2020

Kheiron has also received funding from the E.U. Horizon 2020 research and innovation program to scale across the E.U., including rolling out pilot sites in Europe in 2019. As per the company’s CEO, this is a breakthrough in AI (Artificial Intelligence) diagnostics.

At present, there are numerous startups raising funding to use AI and Machine Learning to help identify new treatments and diagnoses for diseases as well.

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