Journal of the National Medical Association · 2026
Addressing disparities in transthyretin amyloid cardiomyopathy: A systematic review of artificial intelligence in the early identification to improve patient outcomes.
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Zhang Zihuang, Lim Natalie, Abi-Rached Joe, Pickard Benjamin, Parkman Aundria, Zha Yitian, Ferdinand Keith C
Аннотация
BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) remains substantially underdiagnosed among Black patient populations. When applied to non-invasive diagnostic and screening techniques, artificial intelligence (AI) can aid in the early detection of subtle patterns indicative of transthyretin amyloid cardiomyopathy (ATTR-CM), potentially before clinical symptoms appear. This systematic review examines the current landscape of AI-based diagnostic tools for ATTR-CM.
METHODS: A comprehensive search was performed in PubMed, Embase, Cochrane Library, Scopus, and Web of Science. The search strategy targeted peer-reviewed, English-only articles published from January 1, 2015, to May 27, 2025, focusing on the application of artificial intelligence to electrocardiograms (ECG), echocardiograms, and cardiac magnetic resonance imaging (CMR) on ATTR-CM specifically. We include the studies that have diagnostic performance measures as their primary outcomes.
RESULTS: 713 studies were identified from databases, and 27 were included. Studies were categorized into three groups based on their primary AI-applied non-invasive diagnostic tools: ECG (n = 16), echocardiogram (n = 8), and CMR (n = 3), either as solo or combined interventions. Performance of AI-enhanced ECG yielded Area Under the Curves (AUCs) ranging from 0.82 to 0.97. AI-integrated echocardiography achieved AUCs between 0.87 and 0.97, while AI-CMR models resulted in AUCs up to 0.92. Multimodal approaches that integrated data from multiple sources also reported AUCs ranging up to 0.97.
CONCLUSION: The integration of AI into ECG, echocardiograms, and CMR have the potential to significantly improve the early detection and classification of ATTR-CM, preventing the progression to end-stage heart failure. Accessible AI-based screening tools could address health disparities by enabling earlier identification and treatment initiation for Black populations who face disproportionate disease burden of ATTR-CM and diagnostic delays.
METHODS: A comprehensive search was performed in PubMed, Embase, Cochrane Library, Scopus, and Web of Science. The search strategy targeted peer-reviewed, English-only articles published from January 1, 2015, to May 27, 2025, focusing on the application of artificial intelligence to electrocardiograms (ECG), echocardiograms, and cardiac magnetic resonance imaging (CMR) on ATTR-CM specifically. We include the studies that have diagnostic performance measures as their primary outcomes.
RESULTS: 713 studies were identified from databases, and 27 were included. Studies were categorized into three groups based on their primary AI-applied non-invasive diagnostic tools: ECG (n = 16), echocardiogram (n = 8), and CMR (n = 3), either as solo or combined interventions. Performance of AI-enhanced ECG yielded Area Under the Curves (AUCs) ranging from 0.82 to 0.97. AI-integrated echocardiography achieved AUCs between 0.87 and 0.97, while AI-CMR models resulted in AUCs up to 0.92. Multimodal approaches that integrated data from multiple sources also reported AUCs ranging up to 0.97.
CONCLUSION: The integration of AI into ECG, echocardiograms, and CMR have the potential to significantly improve the early detection and classification of ATTR-CM, preventing the progression to end-stage heart failure. Accessible AI-based screening tools could address health disparities by enabling earlier identification and treatment initiation for Black populations who face disproportionate disease burden of ATTR-CM and diagnostic delays.