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Saleh G, Sularz A, Liu CH, Lo Russo GV, Adi MZ, Attia Z, Friedman P, Gulati R, Alkhouli M. Artificial Intelligence Electrocardiogram-Derived Heart Age Predicts Long-Term Mortality After Transcatheter Aortic Valve Replacement. JACC. ADVANCES 2024; 3:101171. [PMID: 39372454 PMCID: PMC11450920 DOI: 10.1016/j.jacadv.2024.101171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/08/2024]
Affiliation(s)
- Ghasaq Saleh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Agata Sularz
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Chia-Hao Liu
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Gerardo V. Lo Russo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Mahmoud Zhour Adi
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Zachi Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Rajiv Gulati
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Mohamad Alkhouli
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
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Barros A, German Mesner I, Nguyen NR, Moorman JR. Age prediction from 12-lead electrocardiograms using deep learning: a comparison of four models on a contemporary, freely available dataset. Physiol Meas 2024; 45:08NT01. [PMID: 39048099 PMCID: PMC11334242 DOI: 10.1088/1361-6579/ad6746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 06/05/2024] [Accepted: 07/24/2024] [Indexed: 07/27/2024]
Abstract
Objective.The 12-lead electrocardiogram (ECG) is routine in clinical use and deep learning approaches have been shown to have the identify features not immediately apparent to human interpreters including age and sex. Several models have been published but no direct comparisons exist.Approach.We implemented three previously published models and one unpublished model to predict age and sex from a 12-lead ECG and then compared their performance on an open-access data set.Main results.All models converged and were evaluated on the holdout set. The best preforming age prediction model had a hold-out set mean absolute error of 8.06 years. The best preforming sex prediction model had a hold-out set area under the receiver operating curve of 0.92.Significance.We compared performance of four models on an open-access dataset.
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Affiliation(s)
- Andrew Barros
- Center for Advanced Medical Analytics (CAMA), School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Ian German Mesner
- Center for Advanced Medical Analytics (CAMA), School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - N Rich Nguyen
- Center for Advanced Medical Analytics (CAMA), School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States of America
| | - J Randall Moorman
- Center for Advanced Medical Analytics (CAMA), School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Division of Cardiovascular Medicine, Department of Medicine, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
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Barros A, German-Mesner I, Rich Nguyen N, Moorman JR. Age Prediction From 12-lead Electrocardiograms Using Deep Learning: A Comparison of Four Models on a Contemporary, Freely Available Dataset. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.02.24302201. [PMID: 38352374 PMCID: PMC10862990 DOI: 10.1101/2024.02.02.24302201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/31/2024]
Abstract
Objective The 12-lead electrocardiogram (ECG) is routine in clinical use and deep learning approaches have been shown to have the identify features not immediately apparent to human interpreters including age and sex. Several models have been published but no direct comparisons exist. Approach We implemented three previously published models and one unpublished model to predict age and sex from a 12-lead ECG and then compared their performance on an open-access data set. Main results All models converged and were evaluated on the holdout set. The best preforming age prediction model had a hold-out set mean absolute error of 8.06 years. The best preforming sex prediction model had a hold-out set area under the receiver operating curve of 0.92. Significance We compared performance of four models on an open-access dataset.
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Al Moussawy M, Lakkis ZS, Ansari ZA, Cherukuri AR, Abou-Daya KI. The transformative potential of artificial intelligence in solid organ transplantation. FRONTIERS IN TRANSPLANTATION 2024; 3:1361491. [PMID: 38993779 PMCID: PMC11235281 DOI: 10.3389/frtra.2024.1361491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 03/01/2024] [Indexed: 07/13/2024]
Abstract
Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.
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Affiliation(s)
- Mouhamad Al Moussawy
- Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States
| | - Zoe S Lakkis
- Health Sciences Research Training Program, University of Pittsburgh, Pittsburgh, PA, United States
| | - Zuhayr A Ansari
- Health Sciences Research Training Program, University of Pittsburgh, Pittsburgh, PA, United States
| | - Aravind R Cherukuri
- Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States
| | - Khodor I Abou-Daya
- Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States
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Chen C, Chen Z, Luo W, Xu Y, Yang S, Yang G, Chen X, Chi X, Xie N, Zeng Z. Ethical perspective on AI hazards to humans: A review. Medicine (Baltimore) 2023; 102:e36163. [PMID: 38050218 PMCID: PMC10695628 DOI: 10.1097/md.0000000000036163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 10/26/2023] [Indexed: 12/06/2023] Open
Abstract
This article explores the potential ethical hazards of artificial intelligence (AI) on society from an ethical perspective. We introduce the development and application of AI, emphasizing its potential benefits and possible negative impacts. We particularly examine the application of AI in the medical field and related ethical and legal issues, and analyze potential hazards that may exist in other areas of application, such as autonomous driving, finance, and security. Finally, we offer recommendations to help policymakers, technology companies, and society as a whole address the potential hazards of AI. These recommendations include strengthening regulation and supervision of AI, increasing public understanding and awareness of AI, and actively exploring how to use the advantages of AI to achieve a more just, equal, and sustainable social development. Only by actively exploring the advantages of AI while avoiding its negative impacts can we better respond to future challenges.
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Affiliation(s)
- Changye Chen
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Ziyu Chen
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Wenyu Luo
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
- The School of Public Health, Guilin Medical University, Gui Lin, Guangxi, China
| | - Ying Xu
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Sixia Yang
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Guozhao Yang
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Xuhong Chen
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Xiaoxia Chi
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Ni Xie
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
| | - Zhuoying Zeng
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China
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