{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T06:01:54Z","timestamp":1789970514568,"version":"4.0.1"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\u20136.83; intercardiologist RMSE of 8.23%] for LV ejection fraction, 0.25\u2005cm (95% CI: 0.20\u20130.29; 0.36\u2005cm) for IVC maximal diameter, 2.39 cm\n                      <jats:sup>2<\/jats:sup>\n                      (95% CI: 1.96\u20132.82; 4.39 cm\n                      <jats:sup>2<\/jats:sup>\n                      ) for LA area, 2.11 cm\n                      <jats:sup>2<\/jats:sup>\n                      (95% CI: 1.75\u20132.47; 3.49 cm\n                      <jats:sup>2<\/jats:sup>\n                      ) for RA area, 5.06\u2005mm (95% CI: 4.58\u20135.55; 4.67\u2005mm) for LV end-diastolic diameter, 10.17% (95% CI: 9.01\u201311.33; 14.12%) for RV fractional area change, and 0.19\u2005cm (95% CI: 0.16\u20130.21; 0.24\u2005cm) for aortic root diameter.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1891962","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T05:35:25Z","timestamp":1789968925000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated artificial intelligence\u2013enabled measurement of cardiac structures on point-of-care ultrasonography: a prospective multicenter validation study"],"prefix":"10.3389","volume":"13","author":[{"given":"Sarah F.","family":"Mercaldo","sequence":"first","affiliation":[{"name":"Digital Clinical Research Organization, Mass General Brigham AI","place":["Boston, MA, United States"]},{"name":"Department of Radiology, Massachusetts General Hospital","place":["Boston, MA, United States"]},{"name":"Harvard Medical School","place":["Boston, MA, United 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