Does an AI model applied to single-view POCUS videos accurately detect hypertrophic cardiomyopathy and transthyretin amyloid cardiomyopathy in patients undergoing cardiac POCUS?
An AI model applied to single-view point-of-care cardiac ultrasound can accurately detect under-recognized hypertrophic and transthyretin amyloid cardiomyopathies, identifying patients at higher mortality risk.
BACKGROUND: Point-of-care ultrasonography (POCUS) enables cardiac imaging at the bedside and in communities but is limited by abbreviated protocols and variation in quality. We aimed to develop and test artificial intelligence (AI) models to screen for under-diagnosed cardiomyopathies from cardiac POCUS. METHODS: In a development set of 290 245 transthoracic echocardiographic videos across the Yale-New Haven Health System (YNHHS), we used augmentation approaches, and a customised loss function weighted for view quality to derive a POCUS-adapted, multi-label, video-based convolutional neural network that discriminates hypertrophic cardiomyopathy and transthyretin amyloid cardiomyopathy from controls without known disease. We evaluated the model across independent, internal, and external, retrospective cohorts of individuals undergoing cardiac POCUS across YNHHS and the Mount Sinai Health System (MSHS) emergency departments (between 2012 and 2024) to prioritise key views and validate the diagnostic and prognostic performance of single-view screening protocols. FINDINGS: Between Nov 1, 2023, and March 28, 2024, we identified 33 127 patients (mean age 58·9 SD 20·5 years, 17 276 52·2% were female, 14 923 45·0% were male, and for 928 2·8% sex was recorded as unknown) at YNHHS and 5624 patients (mean age 56·0 20·5 years, 1953 34·7% were female, 2470 43·9% were male, and for 1201 21·4% sex was recorded as unknown) at MSHS with 78 054 and 13 796 eligible cardiac POCUS videos, respectively. AI deployed to single-view POCUS videos successfully discriminated hypertrophic cardiomyopathy (eg, area under the receiver operating characteristic curve 0·903 95% CI 0·795-0·981 in YNHHS; 0·890 0·839-0·938 in MSHS for apical-4-chamber acquisitions) and transthyretin amyloid cardiomyopathy (0·907 0·874-0·932 in YNHHS; 0·972 0·959-0·983 in MSHS for parasternal acquisitions). In YNHHS, 40 (58%) of 69 hypertrophic cardiomyopathy cases and 22 (46%) of 48 transthyretin amyloid cardiomyopathy cases would have had a positive screen by AI-POCUS at a median of 2·1 (IQR 0·9-4·5) years and 1·9 (0·6-3·5) years before diagnosis. Moreover, among 25 261 participants without known cardiomyopathy followed up over a median of 2·8 (1·2-6·4) years, AI-POCUS probabilities in the highest (vs lowest) quintile for hypertrophic cardiomyopathy and transthyretin amyloid cardiomyopathy conferred a 17% (adjusted hazard ratio 1·17, 95% CI 1·06-1·29; p=0·0022) and 32% (1·39, 1·19-1·46; p<0·0001) higher adjusted mortality risk, respectively. INTERPRETATION: We developed and validated an AI framework that enables scalable, opportunistic screening of under-recognised cardiomyopathies through simple POCUS acquisitions. FUNDING: National Heart, Lung, and Blood Institute, Doris Duke Charitable Foundation, and BridgeBio.
Oikonomou et al. (Wed,) studied this question.
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