Bay Labs Announces New Data that Evaluates Accuracy, Efficiency and Reproducibility of EchoGPS™ and AutoEF AI Software
Results Presented at the American College of Cardiology ACC.19 68th Annual Scientific Session
SAN FRANCISCO – March 13, 2019 – Bay Labs, a medical technology company at the forefront of applying artificial intelligence (AI) to cardiovascular imaging, today announced the presentation of two studies assessing performance of the company’s deep learning software. The first evaluated the company’s software when used by medical professionals with no prior ultrasound experience to acquire diagnostic-quality echocardiograms, and the second evaluated the fully automated calculation of Ejection Fraction (EF) with accuracy and increased reproducibility. Results from these studies will be presented at the American College of Cardiology ACC.19 68th Annual Scientific Session.
Relevant ACC.19 presentations are scheduled this week in the Cutting Edge Echocardiography and Non-Invasive Imaging: Echo 1 sessions held in Poster Hall F:
“Innovative Ultrasound Technologies EchoGPS and AutoEF Help Novices Perform Efficient and Accurate Echocardiographic Monitoring in Cancer Patients” (March 16, Session 1128-355, 10–10:45 am) Presented by Alberta Yen, M.D., Division of Cardiovascular Medicine, Department of Medicine, Stanford University
An ongoing prospective study conducted at Stanford University is assessing the use of deep learning software to aid in cardiac function monitoring in cancer patients undergoing treatment with potentially cardiotoxic therapies. Bay Labs’ EchoGPS™ and AutoEF software are being used in the study to aid in the acquisition of limited views of a standard echocardiogram by providing users with no prior ultrasound experience real-time guidance to obtain cardiac views, and to automatically calculate a left ventricular EF. Cardiac function monitoring for cardiotoxicity caused by cancer treatments is recommended for at-risk patients, however such screening remains underutilized. While these tools are not yet FDA cleared or approved for these purposes, the preliminary data assess these products for this potential future use.
Dr. Yen will present preliminary study results demonstrating a strong potential use of EchoGPS and AutoEF in a busy cancer clinic for cardiac function monitoring. To date, 37 patients have undergone echocardiograms performed by novices including oncologists and nurse practitioners in the oncology clinic with EchoGPS and minimal supervision, with 100 patients planned in this prospective study. The AutoEF software deemed 76% of the studies of sufficient quality to generate an EF measurement, and the root mean square deviation in EF was 4.8% between AutoEF and echocardiographers, suggesting that the AutoEF measurements may be accurate when calculated from studies gathered using EchoGPS.
“Results from our study suggest that future use of these technologies could enable clinicians to provide expanded access to cardiac monitoring in cancer patients,” said Dr. Yen. “Machine learning-based technology shows promise to expand access to screening echocardiography without overburdening echocardiography labs.”
“Accuracy and Reproducibility of a Novel Artificial Intelligence Deep Learning-Based Algorithm For Automated Calculation of Ejection Fraction in Echocardiography” (March 17, Session 1023-05, 9:45–10:30 am) Presented by Dr. Federico M. Asch, MD, FACC, FASE, Director of the Echocardiography Core Lab, MedStar Health
This study aimed to test the accuracy and reproducibility of an investigational update to Bay Labs’ AutoEF software for automated calculation of EF based on deep learning technology. Although EF is the single most clinically relevant parameter reported in echocardiography, high variability between readers limits its reliability.
Three expert cardiologists assessed EF of 99 patients that had imaging done as part of their routine evaluation and their assessments were compared to the output of Bay Labs’ deep learning algorithm for automated calculation of EF (AutoEF). Cardiologists analyzed biplane tracings performed by three sonographers and AutoEF made its prediction from the clips selected by the sonographers. Accuracy between the investigational software and the average cardiologist prediction was 5.97% measured as mean absolute deviation (MAD). Reproducibility of EF calculations was best for AutoEF (2.94% MAD), compared to that of the cardiologists (4.74% MAD) and sonographers (6.96% MAD), which was calculated by comparing the mean absolute deviation of the three EF measurements from the sonographers, cardiologists, and AutoEF. Dr. Asch concluded that automated calculation of EF using the investigational deep learning algorithm is accurate compared to expert cardiologists and that future use of these algorithms may improve accuracy and reproducibility.
“The data presented at the ACC.19 sessions suggest that deep learning technologies may enable medical professionals to gain new skills and apply them to obtain accurate results,” said Charles Cadieu, co-founder and CEO of Bay Labs. “We believe that that our unique solutions, once cleared by FDA, may help to expand the use and accuracy of high-quality echocardiography imaging.”
With several projects under confidential development, the company invites interested parties to connect directly for a deeper look into the future of AI technologies for ultrasound. To connect to the Bay Labs team, visit www.baylabs.io/.
About Bay Labs
Bay Labs is a San Francisco-based privately held company focused on increasing quality, value and access to medical imaging by combining deep learning and ultrasound. Founded in 2013, Bay Labs applies artificial intelligence to cardiovascular imaging, and its deep learning technology is designed to help medical professionals of all skill levels perform and interpret high-quality echocardiography to ultimately benefit their patients. Bay Labs is funded by Khosla Ventures, Data Collective, and other leading venture capital firms. For more information about Bay Labs, visit www.baylabs.io/.
