Diagnostic Accuracy of AI-Assisted Focused Cardiac Ultrasound (FOCUS) in Primary Care

<b>Background</b>: Focused cardiac ultrasound (FOCUS) can augment the cardiac exam in primary care but is operator-dependent. We evaluated the diagnostic performance of artificial intelligence-assisted FOCUS (AI-FOCUS) performed by family physicians against cardiologist-performed echocardiography. <b>Methods</b>: This research is a prospective cross-sectional study in primary care; family physicians performed conventional FOCUS and AI-FOCUS, with cardiologist-performed echocardiography within 24 h as the reference standard. The primary outcomes were accuracy, sensitivity/specificity, and agreement (κ). <b>Results</b>: AI-FOCUS achieved 94.33% accuracy (95% CI 93.15-95.35), 89.91% sensitivity, and 96.49% specificity, with excellent agreement compared to cardiologists (κ = 0.88). Among the confirmed abnormalities (32.9% of participants), valvular disease was most frequent (42%), followed by reduced LVEF < 50% (28%) and pericardial effusion (12%). In multivariable analysis, AI-assisted LVEF < 50% (OR = 6.05, <i>p</i> < 0.0001) and valvular abnormalities (OR = 4.05, <i>p</i> < 0.0001) were strong predictors of cardiac pathology. <b>Conclusions</b>: AI-FOCUS performed by trained family physicians showed high diagnostic accuracy and excellent agreement with blinded cardiologist-performed echocardiography for detecting LVEF < 50%, screening-level valvular abnormalities, and pericardial effusion, supporting its use for early detection and triage in primary care. Its ease of use and reproducibility suggest value in settings with limited access to cardiology.

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Journal
Healthcare
Published
2025-10-29
DOI
https://doi.org/10.3390/healthcare13212726
Primary Topic
Ultrasound in Clinical Applications
Type
article
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article

Diagnostic Accuracy of AI-Assisted Focused Cardiac Ultrasound (FOCUS) in Primary Care

Andreea Valentina Iacob, Simona Ruxanda Dragan, Anca Dinu, Nilima Rajpal Kundnani et al.
Healthcare
Ultrasound in Clinical Applications
article

Diagnostic Accuracy of AI-Assisted Focused Cardiac Ultrasound (FOCUS) in Primary Care

Andreea Valentina Iacob, Simona Ruxanda Dragan, Anca Dinu, Nilima Rajpal Kundnani, Abhinav Sharma, Mihai Sorin Iacob
article en

Abstract

<b>Background</b>: Focused cardiac ultrasound (FOCUS) can augment the cardiac exam in primary care but is operator-dependent. We evaluated the diagnostic performance of artificial intelligence-assisted FOCUS (AI-FOCUS) performed by family physicians against cardiologist-performed echocardiography. <b>Methods</b>: This research is a prospective cross-sectional study in primary care; family physicians performed conventional FOCUS and AI-FOCUS, with cardiologist-performed echocardiography within 24 h as the reference standard. The primary outcomes were accuracy, sensitivity/specificity, and agreement (κ). <b>Results</b>: AI-FOCUS achieved 94.33% accuracy (95% CI 93.15-95.35), 89.91% sensitivity, and 96.49% specificity, with excellent agreement compared to cardiologists (κ = 0.88). Among the confirmed abnormalities (32.9% of participants), valvular disease was most frequent (42%), followed by reduced LVEF < 50% (28%) and pericardial effusion (12%). In multivariable analysis, AI-assisted LVEF < 50% (OR = 6.05, <i>p</i> < 0.0001) and valvular abnormalities (OR = 4.05, <i>p</i> < 0.0001) were strong predictors of cardiac pathology. <b>Conclusions</b>: AI-FOCUS performed by trained family physicians showed high diagnostic accuracy and excellent agreement with blinded cardiologist-performed echocardiography for detecting LVEF < 50%, screening-level valvular abnormalities, and pericardial effusion, supporting its use for early detection and triage in primary care. Its ease of use and reproducibility suggest value in settings with limited access to cardiology.

HealthcareVol. 13(21)
Spitalul Clinic Judeţean de Urgenţă "Pius Brînzeu" Timişoara (RO), Victor Babeș University of Medicine and Pharmacy Timișoara (RO), Vasile Goldis Western University of Arad (RO)
Openalex Percentile: Top 64%
Ultrasound in Clinical Applications
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