Artificial Intelligence-Enabled Cardiac Function Estimation from Phone Videos of Echocardiograms

Importance

Mobile phone-recorded echocardiogram videos are commonly used in point-of-care, telemedicine, and resource-limited workflows, but artificial intelligence models for left ventricular ejection fraction (LVEF) estimation have primarily been evaluated on native Digital Imaging and Communications in Medicine (DICOM) videos.

Objective

To evaluate whether previously described artificial intelligence models for LVEF estimation retain performance when applied to mobile phone-recorded echocardiographic videos.

Design

Multicenter model validation study comparing model-estimated LVEF with clinician-reported LVEF.

Setting

Three medical centers: Kaiser Permanente Northern California, Beth Israel Deaconess Medical Center through MIMIC-IV-ECHO, and Cedars-Sinai Medical Center.

Participants

Source studies with clinician-reported LVEF and apical 4-chamber or apical 2-chamber views, yielding 6209 phone-recorded videos from 2648 studies and 2611 patients.

Exposures

Mobile phone recording of native echocardiographic videos and fine-tuning of pretrained models using mobile phone-recorded videos from the Kaiser Permanente Northern California training cohort.

Main Outcomes and Measures

Mean absolute error in ejection fraction percentage points, R² for continuous estimation, and area under the receiver operating characteristic curve for identifying ejection fraction greater than 50%.

Results

The study included 6209 mobile phone-recorded echocardiographic videos from 2648 studies and 2611 patients; the weighted mean age was 68.4 years, and 1031 patients were male (39.5%). Without phone-video fine-tuning, the primary model achieved a mean absolute error of 7.00 percentage points, coefficient of determination of 0.49, and area under the receiver operating characteristic curve of 0.91 on phone-recorded videos; corresponding native DICOM performance was 6.08 percentage points, 0.60, and 0.93, respectively. On the 2396-video fine-tuning evaluation cohort, fine-tuning improved primary model performance to a mean absolute error of 6.96 percentage points, coefficient of determination of 0.61, and area under the receiver operating characteristic curve of 0.93. Fine-tuning the public EchoNet-Dynamic model improved performance from 9.36 percentage points, 0.37, and 0.84 to 7.86 percentage points, 0.50, and 0.89, respectively. Progressive central zoom preprocessing degraded model performance.

Conclusions and Relevance

These findings suggest that artificial intelligence–assisted left ventricular ejection fraction estimation from mobile phone-recorded echocardiograms may be feasible when native image export is unavailable, although prospective evaluation is needed before clinical deployment.

Key Points

Question: Can artificial intelligence models developed for native echocardiographic video formats estimate left ventricular ejection fraction from mobile phone-recorded echocardiogram videos?

Findings: In this multicenter retrospective model-validation study of 6209 phone-recorded echocardiographic videos from 2648 studies, the primary pretrained model achieved a mean absolute error of 7.00 percentage points and area under the curve of 0.91 for identifying ejection fraction greater than 50%; phone-video fine-tuning improved performance and digital zoom degraded performance.

Meaning: Artificial intelligence-assisted ejection fraction estimation from phone-recorded echocardiograms may support point-of-care and telemedicine workflows when native image export is unavailable.