Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients

Built on the foundation of the randomized controlled trial (RCT), Evidence Based Medicine (EBM) is at its best when optimizing outcomes for homogeneous cohorts of patients like those participating in an RCT. Its weakness is a failure to resolve a clinical quandary: patients appear for care individua...

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Autores principales:	Jef Van den Eynde, Cedric Manlhiot, Alexander Van De Bruaene, Gerhard-Paul Diller, Alejandro F. Frangi, Werner Budts, Shelby Kutty
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	artificial intelligence congenital heart disease deep learning evidence-based medicine personalized medicine randomized controlled trial Diseases of the circulatory (Cardiovascular) system RC666-701
Acceso en línea:	https://doaj.org/article/4a91857ed8d5485e89f290738d055af5
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id	oai:doaj.org-article:4a91857ed8d5485e89f290738d055af5
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spelling	oai:doaj.org-article:4a91857ed8d5485e89f290738d055af52021-12-02T08:39:24ZMedicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients2297-055X10.3389/fcvm.2021.798215https://doaj.org/article/4a91857ed8d5485e89f290738d055af52021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fcvm.2021.798215/fullhttps://doaj.org/toc/2297-055XBuilt on the foundation of the randomized controlled trial (RCT), Evidence Based Medicine (EBM) is at its best when optimizing outcomes for homogeneous cohorts of patients like those participating in an RCT. Its weakness is a failure to resolve a clinical quandary: patients appear for care individually, each may differ in important ways from an RCT cohort, and the physician will wonder each time if following EBM will provide best guidance for this unique patient. In an effort to overcome this weakness, and promote higher quality care through a more personalized approach, a new framework has been proposed: Medicine-Based Evidence (MBE). In this approach, big data and deep learning techniques are embraced to interrogate treatment responses among patients in real-world clinical practice. Such statistical models are then integrated with mechanistic disease models to construct a “digital twin,” which serves as the real-time digital counterpart of a patient. MBE is thereby capable of dynamically modeling the effects of various treatment decisions in the context of an individual's specific characteristics. In this article, we discuss how MBE could benefit patients with congenital heart disease, a field where RCTs are difficult to conduct and often fail to provide definitive solutions because of a small number of subjects, their clinical complexity, and heterogeneity. We will also highlight the challenges that must be addressed before MBE can be embraced in clinical practice and its full potential can be realized.Jef Van den EyndeJef Van den EyndeCedric ManlhiotAlexander Van De BruaeneGerhard-Paul DillerAlejandro F. FrangiAlejandro F. FrangiAlejandro F. FrangiWerner BudtsShelby KuttyFrontiers Media S.A.articleartificial intelligencecongenital heart diseasedeep learningevidence-based medicinepersonalized medicinerandomized controlled trialDiseases of the circulatory (Cardiovascular) systemRC666-701ENFrontiers in Cardiovascular Medicine, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence congenital heart disease deep learning evidence-based medicine personalized medicine randomized controlled trial Diseases of the circulatory (Cardiovascular) system RC666-701
spellingShingle	artificial intelligence congenital heart disease deep learning evidence-based medicine personalized medicine randomized controlled trial Diseases of the circulatory (Cardiovascular) system RC666-701 Jef Van den Eynde Jef Van den Eynde Cedric Manlhiot Alexander Van De Bruaene Gerhard-Paul Diller Alejandro F. Frangi Alejandro F. Frangi Alejandro F. Frangi Werner Budts Shelby Kutty Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients
description	Built on the foundation of the randomized controlled trial (RCT), Evidence Based Medicine (EBM) is at its best when optimizing outcomes for homogeneous cohorts of patients like those participating in an RCT. Its weakness is a failure to resolve a clinical quandary: patients appear for care individually, each may differ in important ways from an RCT cohort, and the physician will wonder each time if following EBM will provide best guidance for this unique patient. In an effort to overcome this weakness, and promote higher quality care through a more personalized approach, a new framework has been proposed: Medicine-Based Evidence (MBE). In this approach, big data and deep learning techniques are embraced to interrogate treatment responses among patients in real-world clinical practice. Such statistical models are then integrated with mechanistic disease models to construct a “digital twin,” which serves as the real-time digital counterpart of a patient. MBE is thereby capable of dynamically modeling the effects of various treatment decisions in the context of an individual's specific characteristics. In this article, we discuss how MBE could benefit patients with congenital heart disease, a field where RCTs are difficult to conduct and often fail to provide definitive solutions because of a small number of subjects, their clinical complexity, and heterogeneity. We will also highlight the challenges that must be addressed before MBE can be embraced in clinical practice and its full potential can be realized.
format	article
author	Jef Van den Eynde Jef Van den Eynde Cedric Manlhiot Alexander Van De Bruaene Gerhard-Paul Diller Alejandro F. Frangi Alejandro F. Frangi Alejandro F. Frangi Werner Budts Shelby Kutty
author_facet	Jef Van den Eynde Jef Van den Eynde Cedric Manlhiot Alexander Van De Bruaene Gerhard-Paul Diller Alejandro F. Frangi Alejandro F. Frangi Alejandro F. Frangi Werner Budts Shelby Kutty
author_sort	Jef Van den Eynde
title	Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients
title_short	Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients
title_full	Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients
title_fullStr	Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients
title_full_unstemmed	Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients
title_sort	medicine-based evidence in congenital heart disease: how artificial intelligence can guide treatment decisions for individual patients
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/4a91857ed8d5485e89f290738d055af5
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Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients

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