{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T03:25:45Z","timestamp":1793157945275,"version":"4.5.0"},"reference-count":30,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T00:00:00Z","timestamp":1749600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCDD"],"abstract":"<jats:p>The aim of this review is to provide an update on contemporary and evolving artificial intelligence (AI) methods and their role in diagnosing and managing cardiac amyloidosis (CA). AI is the broadest term which describes a variety of different techniques that enable computers to mimic human intelligence. It is widely used across different diagnostic tests including electrocardiograms, echocardiography, scintigraphy and cardiac magnetic resonance (CMR). Through a comprehensive search among four databases, we identified several insights into clinical applications, diagnostic modalities and different utilization of AI in CA. The elusive nature of CA, which often makes early diagnosis challenging, can greatly benefit from the integration of AI into the diagnostic process. The variability in diagnostic strategies of CA underscores the need for more AI-focused prospective clinical trials to establish evidence-based guidelines for AI-driven diagnostic pathways. Our review highlights the capabilities of AI, particularly in the diagnosis of cardiac amyloidosis.<\/jats:p>","DOI":"10.3390\/jcdd12060221","type":"journal-article","created":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T08:18:06Z","timestamp":1749629886000},"page":"221","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["The Role of Artificial Intelligence in Cardiac Amyloidosis: A Focus on Diagnosis and Clinical Application"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9180-1704","authenticated-orcid":false,"given":"Roshan","family":"Wardak","sequence":"first","affiliation":[{"name":"Cleveland Clinic Florida, Weston, FL 33331, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Snipelisky","sequence":"additional","affiliation":[{"name":"Cleveland Clinic Florida, Weston, FL 33331, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2726","DOI":"10.1038\/s41467-021-22877-8","article-title":"Artificial Intelligence-Enabled Fully Automated Detection of Cardiac Amyloidosis Using Electrocardiograms and Echocardiograms","volume":"12","author":"Goto","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.jcmg.2023.10.010","article-title":"Current and Evolving Multimodality Cardiac Imaging in Managing Transthyretin Amyloid Cardiomyopathy","volume":"17","author":"Alwan","year":"2024","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1016\/j.mayocp.2020.01.038","article-title":"Artificial Intelligence in Cardiology: Present and Future","volume":"95","author":"Attia","year":"2020","journal-title":"Mayo Clin. Proc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"429","DOI":"10.2459\/JCM.0000000000001103","article-title":"Machine Learning and Network Medicine: A Novel Approach for Precision Medicine and Personalized Therapy in Cardiomyopathies","volume":"22","author":"Infante","year":"2020","journal-title":"J. Cardiovasc. Med."},{"key":"ref_5","first-page":"e63673","article-title":"Cardiac Amyloidosis: A Comprehensive Review of Pathophysiology, Diagnostic Approach, Applications of Artificial Intelligence, and Management Strategies","volume":"16","author":"Ahammed","year":"2024","journal-title":"Cureus"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100998","DOI":"10.1016\/j.jacadv.2024.100998","article-title":"Impact of Case and Control Selection on Training Artificial Intelligence Screening of Cardiac Amyloidosis","volume":"3","author":"Vrudhula","year":"2024","journal-title":"JACC Adv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e005289","DOI":"10.1161\/CIRCOUTCOMES.118.005289","article-title":"Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery","volume":"12","author":"Tison","year":"2019","journal-title":"Circ. Cardiovasc. Qual. Outcomes"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2768","DOI":"10.1016\/j.mayocp.2021.04.023","article-title":"Artificial Intelligence\u2013Enhanced Electrocardiogram for the Early Detection of Cardiac Amyloidosis","volume":"96","author":"Grogan","year":"2021","journal-title":"Mayo Clin. Proc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.cvdhj.2023.03.001","article-title":"Artificial Intelligence\u2013Enabled Classification of Hypertrophic Heart Diseases Using Electrocardiograms","volume":"4","author":"Haimovich","year":"2023","journal-title":"Cardiovasc. Digit. Health J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1161\/CIRCULATIONAHA.121.058696","article-title":"Multinational Federated Learning Approach to Train ECG and Echocardiogram Models for Hypertrophic Cardiomyopathy Detection","volume":"146","author":"Goto","year":"2022","journal-title":"Circulation"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"100612","DOI":"10.1016\/j.jacadv.2023.100612","article-title":"Postdevelopment performance and validation of the artificial intelligence-enhanced electrocardiogram for detection of cardiac amyloidosis","volume":"2","author":"Harmon","year":"2023","journal-title":"JACC Adv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1016\/j.jcmg.2023.05.002","article-title":"Development and Validation of a Prediction Model and Score for Transthyretin Cardiac Amyloidosis Diagnosis","volume":"16","author":"Gaspar","year":"2023","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1161\/CIRCULATIONAHA.118.034338","article-title":"Fully Automated Echocardiogram Interpretation in Clinical Practice","volume":"138","author":"Zhang","year":"2018","journal-title":"Circulation"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.jcmg.2023.09.011","article-title":"Echocardiography-Based Deep Learning Model to Differentiate Constrictive Pericarditis and Restrictive Cardiomyopathy","volume":"17","author":"Chao","year":"2024","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1001\/jamacardio.2021.6059","article-title":"High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning","volume":"7","author":"Duffy","year":"2022","journal-title":"JAMA Cardiol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, J., Chao, C.-J., Jeong, J.J., Farina, J.M., Seri, A.R., Barry, T., Newman, H., Campany, M., Abdou, M., and O\u2019Shea, M. (2023). Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies. J. Imaging, 9.","DOI":"10.3390\/jimaging9020048"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1001\/jamacardio.2021.0185","article-title":"Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use","volume":"6","author":"Narang","year":"2021","journal-title":"JAMA Cardiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e113","DOI":"10.1016\/S2589-7500(24)00249-8","article-title":"Artificial Intelligence-Guided Detection of Under-Recognised Cardiomyopathies on Point-of-Care Cardiac Ultrasonography: A Multicentre Study","volume":"7","author":"Oikonomou","year":"2025","journal-title":"Lancet Digit. Health"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1038\/s41586-020-2145-8","article-title":"Video-Based AI for Beat-To-Beat Assessment of Cardiac Function","volume":"580","author":"Ouyang","year":"2020","journal-title":"Nature"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"S161","DOI":"10.1016\/j.hlc.2024.06.083","article-title":"An Artificial Intelligence-Guided Echocardiography in Primary Care Patients from Rural and Remote Communities: The AGILE-Echo Study","volume":"33","author":"Soh","year":"2024","journal-title":"Heart Lung Circ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1016\/j.jcmg.2023.01.014","article-title":"Deep Learning on Bone Scintigraphy to Detect Abnormal Cardiac Uptake at Risk of Cardiac Amyloidosis","volume":"16","author":"Delbarre","year":"2023","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e251","DOI":"10.1016\/S2589-7500(23)00265-0","article-title":"Diagnosis and Prognosis of Abnormal Cardiac Scintigraphy Uptake Suggestive of Cardiac Amyloidosis Using Artificial Intelligence: A Retrospective, International, Multicentre, Cross-Tracer Development and Validation Study","volume":"6","author":"Spielvogel","year":"2024","journal-title":"Lancet Digit. Health"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s13550-022-00897-9","article-title":"Convolutional Neural Networks for Detection of Transthyretin Amyloidosis in 2D Scintigraphy Images","volume":"12","author":"Halme","year":"2022","journal-title":"EJNMMI Res."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Maggialetti, N., Torrente, A., Lorusso, G., Villanova, I., Ficco, M., Gravina, M., Ferrari, C., Giordano, L., Granata, V., and Rubini, D. (2024). Role of Cardiovascular Magnetic Resonance in Cardiac Amyloidosis: A Narrative Review. J. Pers. Med., 14.","DOI":"10.3390\/jpm14040407"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1186\/s12968-020-00690-4","article-title":"Deep Learning to Diagnose Cardiac Amyloidosis from Cardiovascular Magnetic Resonance","volume":"22","author":"Martini","year":"2020","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Agibetov, A., T\u00f6rnquist, K., Perez-Rodriguez, D., Smistad, E., Oliveira, G.S., Rudd, J., Rueckert, D., J\u00f8rstad, O.K., \u00d8stvik, A., and Blaschke, T.F. (2021). Convolutional Neural Networks for Fully Automated Diagnosis of Cardiac Amyloidosis by Cardiac Magnetic Resonance Imaging. J. Pers. Med., 11.","DOI":"10.3390\/jpm11121268"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Eckstein, J., Moghadasi, N., K\u00f6rperich, H., Weise Vald\u00e9s, E., Sciacca, V., Paluszkiewicz, L., Burchert, W., and Piran, M. (2022). A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function. Diagnostics, 12.","DOI":"10.3390\/diagnostics12112693"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3532","DOI":"10.1038\/s41467-021-23880-9","article-title":"Machine Learning Analyses of Antibody Somatic Mutations Predict Immunoglobulin Light Chain Toxicity","volume":"12","author":"Garofalo","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.jacc.2024.05.003","article-title":"Transforming Cardiovascular Care with Artificial Intelligence: From Discovery to Practice","volume":"84","author":"Khera","year":"2024","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"100109","DOI":"10.1016\/j.glmedi.2024.100109","article-title":"Advancements and Applications of Artificial Intelligence in Cardiology: Current Trends and Future Prospects","volume":"3","author":"Olawade","year":"2024","journal-title":"J. Med. Surg. Public Health"}],"container-title":["Journal of Cardiovascular Development and Disease"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2308-3425\/12\/6\/221\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:50:08Z","timestamp":1760032208000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2308-3425\/12\/6\/221"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,11]]},"references-count":30,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["jcdd12060221"],"URL":"https:\/\/doi.org\/10.3390\/jcdd12060221","relation":{},"ISSN":["2308-3425"],"issn-type":[{"value":"2308-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,11]]}}}