Jan. 7, 2020
Singapore, Tuesday 7th January - eko.ai, a fast-growing medtech startup based in Singapore, has raised a $4 million USD round co-led by Sequoia India and Singapore government-linked strategic investor EDBI. Partech, SG Innovate and Startup Health Transformer also participated in the round.
has developed a machine learning platform to automate the slow, manual and error-prone process of measuring and interpreting echocardiograms, or ultrasound images of the heart.
“With this funding, we can further develop our innovative solutions with the ultimate goal of democratizing echocardiography -- the safest and most commonly used tool to image the heart,” said Dr Carolyn Lam, co-founder of eko.ai. “Our ultimate goal is to put heart health screening into everyone’s hands. Cardiovascular disease remains the top cause of death for men and women globally and we’re excited to help address this global health issue in a meaningful way.”
The business was founded in August 2017 by James Hare, a serial entrepreneur, investor and co-founder of eDreams; Dr Carolyn Lam, a senior consultant cardiologist at the National Heart Centre Singapore and professor at Duke-National University of Singapore; and Dr Yoran Hummel, founder and former general manager of the Groningen Imaging Core Laboratory of the University Medical Center Groningen.
“When we first met the founders, we were struck by how passionate they were about putting better tools in the hands of cardiologists and researchers. The combination of Carolyn’s deep domain knowledge, James’ commercial acumen and his experience as a founder is truly unique and compelling,” said Pieter Kemps, Principal, Sequoia Capital (India) Singapore. “eko.ai is going after a big market and aims to make a real positive impact on one of the most important areas in healthcare.”
The funds will be used to grow the company’s development team and accelerate commercial operations in the US and Europe. Potential applications of the eko.ai platform and tools range from expanding the use of echocardiography in clinical care to improving the performance of cardiovascular clinical trials, especially for the early detection and prediction of heart disease.
eko.ai has ongoing commercial and academic research collaborations with multiple partners, including Astrazeneca, Brigham and Women’s Hospital, Samsung Medical Center’s Heart, Vascular and Stroke Institute, and the University of Alberta, with more to be announced soon.
James Hare, CEO and co-founder of eko.ai, said “We’re honored to work with such world-renowned partners. The strong momentum behind our collaborations reflects the growing recognition that the combination of machine learning and echocardiography can be a powerful research tool for improved patient phenotyping and hypothesis generation.”
“We look forward to supporting eko.ai as they expand globally and advance the use of AI in healthcare to improve cardiologists’ detection, accuracy and productivity,” said EDBI’s CEO & President, Chu Swee Yeok. “Nurturing innovative home-grown companies in deep tech industries including healthcare remains a cornerstone of our investment priorities and together with like-minded investors, eko.ai will have a booster shot to transform medical diagnostic imaging and delivery of care to cardiovascular patients.”
The company recently won several high-profile competitions, including Slingshot 2019, an award scheme run by Enterprise Singapore, in which eko.ai beat 2,400 companies across 120 countries to clinch the top prize for best startup. Other awards include the Hello Tomorrow Singapore 2019 award, the Singapore Funded Here award and the Medtech Innovator award from JLABS @ Shanghai.
eko.ai uses machine learning to automate the fight against heart disease. The software tools improve cardiovascular research and the performance of clinical trials using echocardiography, the safest and most common cardiac imaging modality. eko.ai connects institutions and imaging labs around the world on a platform of ready to use automation tools for video classification, segmentation and federated learning across diverse, anonymous patient and disease cohorts.