Key result
The AI-enabled device accurately measured left ventricular ejection fraction on point-of-care ultrasound with a root mean square error of 6.20%, demonstrating better accuracy than the intercardiologist error of 8.23%.
Why the study?
Manual measurement of 2D cardiac quantitative data on point-of-care ultrasonography is time-consuming and dependent on user experience, motivating assessment of AI-enabled automated quantification.
Does an AI-enabled device accurately calculate key cardiac measurements on point-of-care ultrasonography compared to expert cardiologist interpretation in adult patients with suspected or established cardiac pathology?
Cross-Sectional (n=200)
Yes
Does an AI-enabled device accurately calculate key cardiac measurements on point-of-care ultrasonography compared to expert cardiologist interpretation in adult patients with suspected or established cardiac pathology?
Effect estimate: RMSE 6.20% (95% CI 5.57-6.83)
Absolute Event Rate: 6.2% vs 8.23%
An AI-enabled device accurately calculates key cardiac measurements on POCUS with error margins comparable to or better than inter-cardiologist variability, offering a promising tool to enhance bedside clinical decision-making.
Supports AI LVEF quantification on POCUS with RMSE below inter-reader variability; leaves open prospective validation before clinical adoption.
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 cm 2 (95% CI: 1.96–2.82; 4.39 cm 2 ) for LA area, 2.11 cm 2 (95% CI: 1.75–2.47; 3.49 cm 2 ) 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.
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Mercaldo et al. (2026) conducted a cross-sectional in Suspected or established cardiac pathology (n=200). AISAP Cardio V1.0 (AI-enabled device) vs. Average of 3 cardiologists' manual measurements was evaluated on Root mean square error (RMSE) for left ventricular ejection fraction (RMSE 6.20%, 95% CI 5.57-6.83). The AI-enabled device accurately measured left ventricular ejection fraction on point-of-care ultrasound with a root mean square error of 6.20%, demonstrating better accuracy than the intercardiologist error of 8.23%.
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