Value of Artificial Intelligence for Enhancing Suspicion of Cardiac Amyloidosis Using Electrocardiography and Echocardiography: A Narrative Review
Nonspecific symptoms and other diagnostic challenges lead to underdiagnosis of cardiac amyloidosis (CA). Artificial intelligence (AI) could help address these challenges, but a summary of the performance of these tools is lacking. This narrative review of published literature describes the performan...
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
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| Online Access: | https://www.ahajournals.org/doi/10.1161/JAHA.124.036533 |
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| author | Martha Grogan Francisco Lopez‐Jimenez Spencer Guthrie Nisith Kumar Reuben Langevin Isabelle Lousada Ronald Witteles Ajay Royyuru Michael Rosenzweig Sarah Cairns‐Smith David Ouyang |
| author_facet | Martha Grogan Francisco Lopez‐Jimenez Spencer Guthrie Nisith Kumar Reuben Langevin Isabelle Lousada Ronald Witteles Ajay Royyuru Michael Rosenzweig Sarah Cairns‐Smith David Ouyang |
| author_sort | Martha Grogan |
| collection | DOAJ |
| description | Nonspecific symptoms and other diagnostic challenges lead to underdiagnosis of cardiac amyloidosis (CA). Artificial intelligence (AI) could help address these challenges, but a summary of the performance of these tools is lacking. This narrative review of published literature describes the performance of AI tools that use data from ECGs and echocardiography to improve identification of CA and challenges that hinder adoption of these tools. Thirteen studies met inclusion criteria with sample sizes ranging from 50 to 2451 patients. Four studies used ECG data, 8 used echocardiography data, and 1 used both. The CA gold standard was typically defined as a CA diagnosis in an institutional or other database but the requirements for these diagnoses were heterogenous across studies, and many did not distinguish among CA subtypes. AI model development varied considerably, and only 4 studies included external validation. The ability of models to predict CA ranged from 0.71 to 1.00, sensitivity ranged from 16% to 100%, and specificity from 75% to 100%. Only 1 study reported model performance across strata of sex, age, race, and CA type. Persistent challenges to AI adoption include usability, cost, value added, electronic health record/information technology interoperability, patient‐related factors, regulation, and privacy and liability. Published studies on AI for improved identification of CA show favorable performance measures but numerous methodologic and other challenges must be addressed before these tools are more widely adopted. |
| format | Article |
| id | doaj-art-3b959bf8e6344ab6a703eada51419a7b |
| institution | OA Journals |
| issn | 2047-9980 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
| spelling | doaj-art-3b959bf8e6344ab6a703eada51419a7b2025-08-20T01:52:38ZengWileyJournal of the American Heart Association: Cardiovascular and Cerebrovascular Disease2047-99802025-04-0114810.1161/JAHA.124.036533Value of Artificial Intelligence for Enhancing Suspicion of Cardiac Amyloidosis Using Electrocardiography and Echocardiography: A Narrative ReviewMartha Grogan0Francisco Lopez‐Jimenez1Spencer Guthrie2Nisith Kumar3Reuben Langevin4Isabelle Lousada5Ronald Witteles6Ajay Royyuru7Michael Rosenzweig8Sarah Cairns‐Smith9David Ouyang10Department of Cardiovascular Diseases Mayo Clinic Rochester MN USADepartment of Cardiovascular Diseases Mayo Clinic Rochester MN USAAttralus San Francisco CA USAPfizer Inc. New York NY USAAmyloidosis Research Consortium Newton MA USAAmyloidosis Research Consortium Newton MA USAStanford University School of Medicine Stanford CA USAIBM Research Yorktown Heights NY USACity of Hope National Cancer Center Duarte CA USAAmyloidosis Research Consortium Newton MA USACedars‐Sinai Medical Center Los Angeles CA USANonspecific symptoms and other diagnostic challenges lead to underdiagnosis of cardiac amyloidosis (CA). Artificial intelligence (AI) could help address these challenges, but a summary of the performance of these tools is lacking. This narrative review of published literature describes the performance of AI tools that use data from ECGs and echocardiography to improve identification of CA and challenges that hinder adoption of these tools. Thirteen studies met inclusion criteria with sample sizes ranging from 50 to 2451 patients. Four studies used ECG data, 8 used echocardiography data, and 1 used both. The CA gold standard was typically defined as a CA diagnosis in an institutional or other database but the requirements for these diagnoses were heterogenous across studies, and many did not distinguish among CA subtypes. AI model development varied considerably, and only 4 studies included external validation. The ability of models to predict CA ranged from 0.71 to 1.00, sensitivity ranged from 16% to 100%, and specificity from 75% to 100%. Only 1 study reported model performance across strata of sex, age, race, and CA type. Persistent challenges to AI adoption include usability, cost, value added, electronic health record/information technology interoperability, patient‐related factors, regulation, and privacy and liability. Published studies on AI for improved identification of CA show favorable performance measures but numerous methodologic and other challenges must be addressed before these tools are more widely adopted.https://www.ahajournals.org/doi/10.1161/JAHA.124.036533artificial intelligencecardiac amyloidosisechocardiographyelectrocardiography |
| spellingShingle | Martha Grogan Francisco Lopez‐Jimenez Spencer Guthrie Nisith Kumar Reuben Langevin Isabelle Lousada Ronald Witteles Ajay Royyuru Michael Rosenzweig Sarah Cairns‐Smith David Ouyang Value of Artificial Intelligence for Enhancing Suspicion of Cardiac Amyloidosis Using Electrocardiography and Echocardiography: A Narrative Review Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease artificial intelligence cardiac amyloidosis echocardiography electrocardiography |
| title | Value of Artificial Intelligence for Enhancing Suspicion of Cardiac Amyloidosis Using Electrocardiography and Echocardiography: A Narrative Review |
| title_full | Value of Artificial Intelligence for Enhancing Suspicion of Cardiac Amyloidosis Using Electrocardiography and Echocardiography: A Narrative Review |
| title_fullStr | Value of Artificial Intelligence for Enhancing Suspicion of Cardiac Amyloidosis Using Electrocardiography and Echocardiography: A Narrative Review |
| title_full_unstemmed | Value of Artificial Intelligence for Enhancing Suspicion of Cardiac Amyloidosis Using Electrocardiography and Echocardiography: A Narrative Review |
| title_short | Value of Artificial Intelligence for Enhancing Suspicion of Cardiac Amyloidosis Using Electrocardiography and Echocardiography: A Narrative Review |
| title_sort | value of artificial intelligence for enhancing suspicion of cardiac amyloidosis using electrocardiography and echocardiography a narrative review |
| topic | artificial intelligence cardiac amyloidosis echocardiography electrocardiography |
| url | https://www.ahajournals.org/doi/10.1161/JAHA.124.036533 |
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