[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125227-en":3,"doc-seo-125227-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125227,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",7,"Healthcare","Machine Learning to Optimize the Echocardiographic Follow-Up of Aortic Stenosis - Original Research","Machine learning is evaluated as an automated tool to optimize echocardiographic surveillance for patients with mild-to-moderate aortic stenosis. The model was trained, validated, and externally tested to predict progression to severe valvular disease at 1, 2, or 3 years using demographic and echocardiographic data from two independent tertiary-hospital cohorts totaling 3,171 patients. Performance showed strong discrimination and guideline comparisons indicated substantial reductions in unnecessary follow-up examinations.","JA CC: CA RD IOVAS C ULAR I MAG IN G VOL . 1 6 , N O . 6 , 202 3  \nª 202 3 BY T H E A MERICA N C OLLEGE OF C A RDIOLOG Y FOU N D A T I ON  \nP U BLIS HED B Y E L S E VIER  \nORIGINAL RESEARCH  \nMachine Learning to Optimize the Echocardiographic Follow-Up of Aortic Stenosis  \nAntonio Sánchez-Puente, BSC, PHD,a,b,* P. Ignacio Dorado-Díaz, BE, PHD,a,b,* Jesús Sampedro-Gómez, BE,a,b Javier Bermejo, MD, PHD,b,c Pablo Martinez-Legazpi, BE, PHD,d Francisco Fernández-Avilés, MD, PHD,b,c Javier Sánchez-González, BE, PHD,e Candelas Pérez del Villar, MD, PHD,a,b Víctor Vicente-Palacios, BE, PHD,e, y Pedro L. Sanchez, MD, PHDa,b,y  \nBACKGROUND Disease progression in patients with mild-to-moderate aortic stenosis is heterogenous and requires periodic echocardiographic examinations to evaluate severity.  \nOBJECTIVES This study sought to explore the use of machine learning to optimize aortic stenosis echocardiographic surveillance automatically.  \nMETHODS The study investigators trained, validated, and externally applied a machine learning model to predict whether a patient with mild-to-moderate aortic stenosis will develop severe valvular disease at 1, 2, or 3 years. Demographic and echocardiographic patient data to develop the model were obtained from a tertiary hospital consisting of 4,633 echocardiograms from 1,638 consecutive patients. The external cohort was obtained from an independent tertiary hospital, consisting of 4,531 echocardiograms from 1,533 patients. Echocardiographic surveillance timing results were compared with the European and American guidelines echocardiographic follow-up recommendations.  \nRESULTS In internal validation, the model discriminated severe from nonsevere aortic stenosis development with an area under the receiver-operating characteristic curve (AUC-ROC) of 0.90, 0.92, and 0.92 for the 1-, 2-, or 3-year interval, respectively. In external application, the model showed an AUC-ROC of 0 . 85, 0 . 85, and 0 . 85, for the 1-, 2-, or 3-year interval. A simulated application of the model in the external validation cohort resulted in savings of 49% and 13% of unnecessary echocardiographic examinations per year compared with European and American guideline recommendations, respectively.  \nCONCLUSIONS Machine learning provides real-time, automated, personalized timing of next echocardiographic follow-up examination for patients with mild-to-moderate aortic stenosis. Compared with European and American guidelines, the model reduces the number of patient examinations. (J Am Coll Cardiol Img 2023;16:733–744)  \n© 2023 by the American College of Cardiology Foundation.  \nFrom the aCardiology Service, Salamanca University Hospital, Biomedical Research Institute of Salamanca (IBSAL), Department of Medicine, University of Salamanca, Salamanca, Spain; bSpanish Cardiovascular Network (CIBERCV), Carlos III Health Institute, Spain; cCardiology Service, Gregorio Marañón University Hospital, Gregorio Marañón Health Research Institute (IISGM), Faculty of Medicine, Complutense University, Madrid, Spain; dDepartment of Mathematical Physics and Fluids, Faculty of Sciences, National University of Distance Education (UNED) and CIBERCV, Madrid, Spain; and ePhilips Healthcare, Madrid, Spain. *Drs Sánchez-Puente and Dorado-Díaz contributed equally to this paper as co-first authors. yDrs Vicente-Palacios and Sanchez contributed equally to this paper as co-senior authors.  \nThe authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the Author Center.  \nManuscript received April 5, 2022; revised manuscript received November 17, 2022, accepted December 2, 2022 .  \nISSN 1936-878X/$36 .00 [https://doi.org/10.1016/j.jcmg.2022.12.008](https://doi.org/10.1016/j.jcmg.2022.12.008)  \n734  \nSánchez-Puente et al J A C C : C A R D I O V A S C U L A R I M A G I N G , V O L","cbCaisLmcAkWCye9","https://ap.wps.com/l/cbCaisLmcAkWCye9","pdf",1279084,1,12,"English","en",105,"# Background\n# Objectives\n# Methods\n# Results\n# Conclusions","[{\"question\":\"What problem does this study address for aortic stenosis care?\",\"answer\":\"Disease progression in mild-to-moderate aortic stenosis varies across patients, and periodic echocardiography is needed to reassess severity. The study targets the lack of knowledge about an optimal follow-up interval.\"},{\"question\":\"How was the machine learning model developed and tested?\",\"answer\":\"Investigators trained and validated the model using echocardiographic data from a tertiary hospital cohort, then applied it externally using an independent tertiary-hospital cohort. The model predicts development of severe valvular disease at 1, 2, or 3 years.\"},{\"question\":\"What were the main outcomes compared with guideline recommendations?\",\"answer\":\"The model achieved high discrimination performance in both internal validation and external application. A simulated use in the external cohort indicated savings by reducing unnecessary echocardiographic examinations compared with European and American guideline follow-up intervals.\"}]","Machine Learning to Optimize the Echocardiographic Follow-Up of Aortic Stenosis - Original Research | PDF",1785897606,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-to-optimize-the-echocardiographic-follow-up-of-aortic-stenosis-original-research","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-to-optimize-the-echocardiographic-follow-up-of-aortic-stenosis-original-research/125227/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this study address for aortic stenosis care?","Question",{"text":75,"@type":76},"Disease progression in mild-to-moderate aortic stenosis varies across patients, and periodic echocardiography is needed to reassess severity. The study targets the lack of knowledge about an optimal follow-up interval.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the machine learning model developed and tested?",{"text":80,"@type":76},"Investigators trained and validated the model using echocardiographic data from a tertiary hospital cohort, then applied it externally using an independent tertiary-hospital cohort. The model predicts development of severe valvular disease at 1, 2, or 3 years.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the main outcomes compared with guideline recommendations?",{"text":84,"@type":76},"The model achieved high discrimination performance in both internal validation and external application. A simulated use in the external cohort indicated savings by reducing unnecessary echocardiographic examinations compared with European and American guideline follow-up intervals.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":29,"slug":121},8,"Research & Report","research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]