{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T23:21:34Z","timestamp":1794352894870,"version":"4.9.0"},"reference-count":23,"publisher":"BMJ","issue":"2","license":[{"start":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T00:00:00Z","timestamp":1734393600000},"content-version":"unspecified","delay-in-days":16,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006396","name":"Alexion","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006396","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Alexion, AstraZeneca"},{"DOI":"10.13039\/100004319","name":"Pfizer","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004319","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NIH NHLBI"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Open Heart"],"accepted":{"date-parts":[[2024,11,12]]},"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Cardiac amyloidosis (CA) is an underdiagnosed, progressive and lethal disease. Machine learning applied to common measurements derived from routine echocardiogram studies can inform suspicion of CA.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>Our objectives were to test a random forest (RF) model in detecting CA.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We used 3603 echocardiogram studies from 636 patients at Cedars-Sinai Medical Center to train an RF model to predict CA from echocardiographic parameters. 231 patients with CA were compared with 405 control patients with negative pyrophosphate scans or clinical diagnosis of hypertrophic cardiomyopathy. 19 common echocardiographic measurements from echocardiogram reports were used as input into the RF model. Data was split by patient into a training data set of 2882 studies from 486 patients and a test data set of 721 studies from 150 patients. The performance of the model was evaluated by area under the receiver operative curve (AUC), sensitivity, specificity and positive predictive value (PPV) on the test data set.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The RF model identified CA with an AUC of 0.84, sensitivity of 0.82, specificity of 0.73 and PPV of 0.76. Some echocardiographic measurements had high missingness, suggesting gaps in measurement in routine clinical practice. Features that were large contributors to the model included mitral A-wave velocity, global longitudinal strain (GLS), left ventricle posterior wall diameter end diastolic (LVPWd) and left atrial area.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Machine learning on echocardiographic parameters can detect patients with CA with accuracy. Our model identified several features that were major contributors towards identifying CA including GLS, mitral A peak velocity and LVPWd. Further study is needed to evaluate its external validity and application in clinical settings.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1136\/openhrt-2024-002884","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T14:23:44Z","timestamp":1734963824000},"page":"e002884","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":11,"title":["Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements"],"prefix":"10.1136","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8969-2757","authenticated-orcid":false,"given":"Rachel Si-Wen","family":"Chang","sequence":"first","affiliation":[{"name":"UCSF, San Francisco, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"I-min","family":"Chiu","sequence":"additional","affiliation":[{"name":"Cedars-Sinai Medical Center, Los Angeles, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phillip","family":"Tacon","sequence":"additional","affiliation":[{"name":"Cedars-Sinai Medical Center, Los Angeles, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Abiragi","sequence":"additional","affiliation":[{"name":"Cedars-Sinai Medical Center, Los Angeles, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Louie","family":"Cao","sequence":"additional","affiliation":[{"name":"Cedars-Sinai Medical Center, Los Angeles, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gloria","family":"Hong","sequence":"additional","affiliation":[{"name":"Cedars-Sinai Medical Center, Los Angeles, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Le","sequence":"additional","affiliation":[{"name":"Cedars-Sinai Medical Center, Los Angeles, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Zou","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chathuri","family":"Daluwatte","sequence":"additional","affiliation":[{"name":"Alexion Pharmaceuticals, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piero","family":"Ricchiuto","sequence":"additional","affiliation":[{"name":"Alexion Pharmaceuticals, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Cedars-Sinai Medical Center, Los Angeles, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2024,12,18]]},"reference":[{"key":"2025081710215658000_11.2.e002884.1","doi-asserted-by":"publisher","DOI":"10.1038\/nrneph.2013.171"},{"key":"2025081710215658000_11.2.e002884.2","doi-asserted-by":"publisher","DOI":"10.1161\/circulationaha.118.038169"},{"key":"2025081710215658000_11.2.e002884.3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2016.06.033"},{"key":"2025081710215658000_11.2.e002884.4","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehv338"},{"key":"2025081710215658000_11.2.e002884.5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jchf.2013.11.004"},{"key":"2025081710215658000_11.2.e002884.6","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1001\/jamacardio.2016.2839","article-title":"Multicenter Study of Planar Technetium 99m Pyrophosphate Cardiac Imaging: Predicting Survival for Patients With ATTR Cardiac Amyloidosis","volume":"1","author":"Castano","year":"2016","journal-title":"JAMA Cardiol"},{"key":"2025081710215658000_11.2.e002884.7","doi-asserted-by":"publisher","DOI":"10.1016\/0002-9343(83)90443-6"},{"key":"2025081710215658000_11.2.e002884.8","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1716793"},{"key":"2025081710215658000_11.2.e002884.9","doi-asserted-by":"crossref","first-page":"59","DOI":"10.14797\/mdcvj.1050","article-title":"Cardiac Amyloidosis Treatment","volume":"18","author":"Stern","year":"2022","journal-title":"Methodist Debakey Cardiovasc J"},{"key":"2025081710215658000_11.2.e002884.10","doi-asserted-by":"publisher","DOI":"10.1161\/cir.0000000000000792"},{"key":"2025081710215658000_11.2.e002884.11","doi-asserted-by":"publisher","DOI":"10.1016\/j.echo.2009.11.016"},{"key":"2025081710215658000_11.2.e002884.12","doi-asserted-by":"crossref","first-page":"e20","DOI":"10.1016\/S2589-7500(20)30267-3","article-title":"Diagnosis and risk stratification in hypertrophic cardiomyopathy using machine learning wall thickness measurement: a comparison with human test-retest performance","volume":"3","author":"Augusto","year":"2021","journal-title":"Lancet Digit Health"},{"key":"2025081710215658000_11.2.e002884.13","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 New Biol"},{"key":"2025081710215658000_11.2.e002884.14","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","article-title":"Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning","volume":"2","author":"Poplin","year":"2018","journal-title":"Nat Biomed Eng"},{"key":"2025081710215658000_11.2.e002884.15","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":"2025081710215658000_11.2.e002884.16","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":"2025081710215658000_11.2.e002884.17","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1001\/jamacardio.2020.7184","article-title":"Association of Global Longitudinal Strain With Clinical Status and Mortality in Patients With Chronic Heart Failure","volume":"6","author":"Tr\u00f6bs","year":"2021","journal-title":"JAMA Cardiol"},{"key":"2025081710215658000_11.2.e002884.18","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/s12471-019-1299-1","article-title":"Cardiac amyloidosis: the need for early diagnosis","volume":"27","author":"Oerlemans","year":"2019","journal-title":"Neth Heart J"},{"key":"2025081710215658000_11.2.e002884.19","doi-asserted-by":"crossref","DOI":"10.1136\/openhrt-2018-000881","article-title":"Role of cardiovascular imaging for the diagnosis and prognosis of cardiac amyloidosis","volume":"5","author":"Agha","year":"2018","journal-title":"Open Heart"},{"key":"2025081710215658000_11.2.e002884.20","first-page":"48","article-title":"Prognostic value of global longitudinal strain in heart failure subjects: A recent prototype","volume":"22","author":"Ashish","year":"2019","journal-title":"Int J Cardiol Heart Vasc"},{"key":"2025081710215658000_11.2.e002884.21","article-title":"ASNC\/AHA\/ASE\/EANM\/HFSA\/ISA\/SCMR\/SNMMI Expert Consensus Recommendations for Multimodality Imaging in Cardiac Amyloidosis: Part 1 of 2-Evidence Base and Standardized Methods of Imaging","volume":"14","author":"Dorbala","year":"2021","journal-title":"Circ Cardiovasc Imaging"},{"key":"2025081710215658000_11.2.e002884.22","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1016\/j.amjmed.2020.11.031","article-title":"Cardiac Amyloidosis for the Primary Care Provider: A Practical Review to Promote Earlier Recognition of Disease","volume":"134","author":"Zhang","year":"2021","journal-title":"Am J Med"},{"key":"2025081710215658000_11.2.e002884.23","doi-asserted-by":"crossref","first-page":"B99","DOI":"10.1093\/eurheartjsupp\/suad082","article-title":"Treatment of amyloidosis: present and future","volume":"25","author":"Mallus","year":"2023","journal-title":"Eur Heart J Suppl"}],"container-title":["Open Heart"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1136\/openhrt-2024-002884","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,17]],"date-time":"2025-08-17T17:22:06Z","timestamp":1755451326000},"score":1,"resource":{"primary":{"URL":"https:\/\/openheart.bmj.com\/lookup\/doi\/10.1136\/openhrt-2024-002884"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":23,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,12,18]]},"published-print":{"date-parts":[[2024,12]]}},"alternative-id":["10.1136\/openhrt-2024-002884"],"URL":"https:\/\/doi.org\/10.1136\/openhrt-2024-002884","relation":{},"ISSN":["2053-3624"],"issn-type":[{"value":"2053-3624","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12]]}}}