Diagnostic Performance of General Practitioners in Carotid Plaque Detection Using AI-Enhanced Point-of-Care Ultrasound After Systematic Training.

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Abstract

While point-of-care ultrasound (POCUS) has been integrated into daily practice by general practitioners (GPs) in some countries, there is a paucity of literature documenting its use by Chinese GPs. Additionally, artificial intelligence (AI)-assisted diagnosis is increasingly applied to imaging equipment. This study aims to explore the diagnostic performance of GPs in carotid plaque detection using AI-enhanced POCUS after systematic training. This diagnostic accuracy trial was conducted in Shanghai, where 7 GPs received systematic training then recruited patients with high atherosclerotic cardiovascular disease risk during outpatient visits for free carotid plaque screening. We comprehensively evaluated their diagnostic performance using 2 approaches: per-patient (ie, assessed whether patients had any carotid plaque) and per-vessel (ie, determined plaque presence in any carotid vessel). Evaluation metrics included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the <i>κ</i> statistic for agreement. A total of 169 patients (mean age 69.6 years [SD 5.76]; 37.9% male) were included in this study. At the per-patient level, sensitivity was 0.87 (95% CI, 0.80-0.95); specificity was 0.91 (95% CI, 0.86-0.97); PPV was 0.89 (95% CI, 0.82-0.96); NPV was 0.89 (95% CI, 0.83-0.96); and the <i>κ</i> value was 0.78 (95% CI, 0.69-0.88), indicating high agreement with the gold standard. The diagnostic performance evaluated at the per-vessel level was comparable to that at the per-patient level. Our findings indicate that, after systematic training, GPs can effectively perform carotid plaque screening using AI-enhanced POCUS within a clinical workflow in high-risk elderly populations.

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