Document Type
Article
Publication Date
9-21-2026
Abstract
Introduction: Point-of-care ultrasonography (POCUS) enables clinicians to obtain critical diagnostic information at the bedside, especially in resource-limited settings. This information may include 2D cardiac quantitative data, although measuring the data manually can be time-consuming and subject to user experience. Artificial intelligence (AI) can potentially automate this quantification. This study assessed the interpretation of key cardiac measurements on POCUS images by an AI-enabled device (AISAP Cardio V1.0).
Methods: This retrospective diagnostic accuracy study included 200 POCUS cases from four hospitals (two in Israel and two in the United States). Each case was independently interpreted by three cardiologists and the device for seven measurements [left ventricular (LV) ejection fraction, inferior vena cava (IVC) maximal diameter, left atrial (LA) area, right atrial (RA) area, LV end-diastolic diameter, right ventricular (RV) fractional area change, and aortic root diameter]. The endpoints were the root mean square error (RMSE) of the device compared with the average cardiologist measurement (LV ejection fraction and IVC maximal diameter were primary endpoints; the other measurements were secondary endpoints). Predefined passing criteria were based on the upper bounds of the RMSE 95% confidence intervals (CIs). The intercardiologist RMSE was also calculated for reference.
Results: The device achieved the passing criteria for six of the seven measurements. Although not achieving the passing criterion for RV fractional area change, it still achieved a better RMSE than the intercardiologist RMSE. The RMSE was 6.20% [95% confidence interval (CI): 5.57-6.83; intercardiologist RMSE of 8.23%] for LV ejection fraction, 0.25 cm (95% CI: 0.20-0.29; 0.36 cm) for IVC maximal diameter, 2.39 cm2 (95% CI: 1.96-2.82; 4.39 cm2) for LA area, 2.11 cm2 (95% CI: 1.75-2.47; 3.49 cm2) for RA area, 5.06 mm (95% CI: 4.58-5.55; 4.67 mm) for LV end-diastolic diameter, 10.17% (95% CI: 9.01-11.33; 14.12%) for RV fractional area change, and 0.19 cm (95% CI: 0.16-0.21; 0.24 cm) for aortic root diameter.
Discussion: The device accurately calculated these cardiac measurements especially when benchmarked against intercardiologist variability. Its use could assist clinicians who utilize POCUS and better enable their clinical decision-making.
Recommended Citation
Mercaldo, Sarah F.; Bizzo, Bernardo C.; Sadore, Tsion; Halle, Madeleine A.; MacDonald, Ashley L.; Newbury-Chaet, Isabella; L'Italien, Eric; Schultz, Alex S.; Tam, Victor; Hegde, Sheila M.; Mangion, Judy R.; Mehrotra, Praveen; Zhao, Qiong; Wu, Justina; and Hillis, James M., "Automated Artificial Intelligence-Enabled Measurement of Cardiac Structures on Point-of-Care Ultrasonography: A Prospective Multicenter Validation Study" (2026). Division of Cardiology Faculty Papers. Paper 207.
https://jdc.jefferson.edu/cardiologyfp/207
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Language
English
PubMed ID
42835029
Comments
This article is the author's final published version in Frontiers in Cardiovascular Medicine, Volume 13, 2026, Article number 1891962.
The published version is available at https://doi.org/10.3389/fcvm.2026.1891962. Copyright © 2026 Mercaldo, Bizzo, Sadore, Halle, MacDonald, Newbury-Chaet, L’Italien, Schultz, Tam, Hegde, Mangion, Mehrotra, Zhao, Wu and Hillis.