Circulation. Cardiovascular imaging · 2026
Multimodal Artificial Intelligence for Cardiac Amyloidosis Diagnosis: Integrating Echocardiography With Clinical and Laboratory Data for Improved Detection.
СтатьяНазвание и аннотация: Перевод в очереди
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Slivnick Jeremy A, Lim Sze Chi, Randazzo Michael, Maurer Mathew S, Helmke Stephen, Scherrer-Crosbie Marielle, Vakilpour Azin, Zareba Karolina M, Goyal Akash, Cheng Richard, Nakamatsu Nicole, Kitano Tetsuji, Takeuchi Masaaki, Hotta Viviane Tiemi, Campos Vieira Marcelo Luiz, Elissamburu Pablo, Ronderos Ricardo E, Prado Aldo, Koutroumpakis Efstratios, Deswal Anita, Pursnani Amit, Sarswat Nitasha, Addetia Karima, Cotella Juan, Ruberg Frederick L, Frost Matthew, Fontana Marianna, Lam Su Ping Carolyn, Lang Roberto M, Asch Federico M
Аннотация
BACKGROUND: Cardiac amyloidosis (CA) is an underdiagnosed yet treatable cause of heart failure in which timely diagnosis is essential to initiate life-prolonging therapies. While artificial intelligence (AI)-based tools using transthoracic echocardiography (TTE), electrocardiography, or electronic health records have demonstrated promise for CA detection, most rely on single data sources. We aimed to evaluate whether integrating clinical, laboratory, and TTE biomarkers improves the performance of an existing TTE-based AI model for CA detection.
METHODS: We developed and tested a combined AI echo-clinical model (AI-ECM) incorporating demographics, laboratory biomarkers, and TTE parameters into a previously validated TTE-only AI model (Us2.Ca). Model training and internal validation were performed using the Amyloidosis Imaging International Consortium, a global multiethnic registry comprised of 727 patients with CA and 316 controls, including 202 with suspected transthyretin-CA with negative diagnostic evaluation and 114 patients with biopsy-proven extracardiac light chain amyloidosis without cardiac involvement. Ground truth CA diagnosis was adjudicated per consensus criteria. AI-ECM and Us2.Ca performance was assessed using area under the curve, accuracy, sensitivity, and specificity.
RESULTS: In building the AI-ECM, feature importance analysis showed that having the Us2.Ca prediction scores, relative wall thickness, sex, and estimated glomerular filtration rate contributed most to performance. The AI-ECM demonstrated superior performance (area under the curve, 0.94; accuracy, 90%; sensitivity, 93%; specificity, 85%) compared with the Us2.Ca (area under the curve, 0.89; accuracy, 80%; sensitivity, 76%; specificity, 91%; P=0.006). While the Us2.Ca model classification was indeterminate in 9% of the cases, the AI-ECM allowed classification of all cases. The AI-ECM improved sensitivity for light chain-CA detection and maintained high accuracy across subtypes and control groups.
CONCLUSIONS: A multiparametric AI model integrating basic clinical, laboratory, and TTE data with the deep learning Us2.Ca improved performance for CA detection over Us2.Ca alone. This approach represents a step toward scalable, AI-guided precision diagnostics for CA in diverse populations.
METHODS: We developed and tested a combined AI echo-clinical model (AI-ECM) incorporating demographics, laboratory biomarkers, and TTE parameters into a previously validated TTE-only AI model (Us2.Ca). Model training and internal validation were performed using the Amyloidosis Imaging International Consortium, a global multiethnic registry comprised of 727 patients with CA and 316 controls, including 202 with suspected transthyretin-CA with negative diagnostic evaluation and 114 patients with biopsy-proven extracardiac light chain amyloidosis without cardiac involvement. Ground truth CA diagnosis was adjudicated per consensus criteria. AI-ECM and Us2.Ca performance was assessed using area under the curve, accuracy, sensitivity, and specificity.
RESULTS: In building the AI-ECM, feature importance analysis showed that having the Us2.Ca prediction scores, relative wall thickness, sex, and estimated glomerular filtration rate contributed most to performance. The AI-ECM demonstrated superior performance (area under the curve, 0.94; accuracy, 90%; sensitivity, 93%; specificity, 85%) compared with the Us2.Ca (area under the curve, 0.89; accuracy, 80%; sensitivity, 76%; specificity, 91%; P=0.006). While the Us2.Ca model classification was indeterminate in 9% of the cases, the AI-ECM allowed classification of all cases. The AI-ECM improved sensitivity for light chain-CA detection and maintained high accuracy across subtypes and control groups.
CONCLUSIONS: A multiparametric AI model integrating basic clinical, laboratory, and TTE data with the deep learning Us2.Ca improved performance for CA detection over Us2.Ca alone. This approach represents a step toward scalable, AI-guided precision diagnostics for CA in diverse populations.