News | Cardiovascular Ultrasound | March 15, 2019

Data from two studies evaluate accuracy, efficiency and reproducibility of cardiovascular ultrasound acquisition and calculation via artificial intelligence

Bay Labs Announces New Data on EchoGPS, AutoEF AI Software at ACC.19

March 15, 2019 — Artificial intelligence (AI) company Bay Labs announced the presentation of two studies assessing performance of the company’s deep learning software for cardiovascular imaging. The first evaluated the 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) 68th Annual Scientific Session, March 16-18 in New Orleans.

Innovative Ultrasound Technologies EchoGPS and AutoEF Help Novices Perform Efficient and Accurate Echocardiographic Monitoring in Cancer Patients

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 U.S. Food and Drug Administration (FDA)-cleared or approved for these purposes, the preliminary data assess these products for this potential future use.

Alberta Yen, M.D., Division of Cardiovascular Medicine, Department of Medicine, Stanford University, 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 percent of the studies of sufficient quality to generate an EF measurement, and the root mean square deviation in EF was 4.8 percent between AutoEF and echocardiographers. This suggests 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 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

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 percent measured as mean absolute deviation (MAD). Reproducibility of EF calculations was best for AutoEF (2.94 percent MAD), compared to that of the cardiologists (4.74 percent MAD) and sonographers (6.96 percent MAD), which was calculated by comparing the mean absolute deviation of the three EF measurements from the sonographers, cardiologists and AutoEF. Federico Asch, M.D., FACC, FASE, director of the Echocardiography Core Lab at MedStar Health, 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. Asch will present the study at ACC.19.

For more information: www.baylabs.io

Related Content

News | Artificial Intelligence

November 24, 2021 — Radiologists can now register their practices to take part in the next-generation American College ...

Time November 24, 2021
arrow
News | Magnetic Resonance Imaging (MRI)

November 24, 2021 — Royal Philips announced new AI-enabled innovations in MR imaging launching at the Radiological ...

Time November 24, 2021
arrow
News | Coronavirus (COVID-19)

November 24, 2021 — Significant decreases in CT imaging for cancer persisted even after the peak of the COVID-19 ...

Time November 24, 2021
arrow
News | Lung Imaging

November 23, 2021 — Median Technologies announces new outstanding performance of its lung cancer screening (LCS) CADx1 ...

Time November 23, 2021
arrow
News | Artificial Intelligence

November 23, 2021 — Laurel Bridge Software Inc., a provider of imaging software solutions that enables health systems to ...

Time November 23, 2021
arrow
News | Artificial Intelligence

November 23, 2021 — The results of a unique two-tiered brain tumor AI challenge were announced today by the Radiological ...

Time November 23, 2021
arrow
News | Artificial Intelligence

November 23, 2021 — Royal Philips announced a collaboration with U.S.-based MedChat to integrate MedChat’s live chat and ...

Time November 23, 2021
arrow
News | Magnetic Resonance Imaging (MRI)

November 23, 2021 — Researchers at Yale University analyzing specialized MRI exams found significant changes in the ...

Time November 23, 2021
arrow
News | Interventional Radiology

November 23, 2021 — A minimally invasive ablation procedure offers long-term relief for patients who experience chronic ...

Time November 23, 2021
arrow
News | Information Technology

November 22, 2021 — While still a relatively young enterprise in the radiology IT space, Within Health already has some ...

Time November 22, 2021
arrow
Subscribe Now