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Nakayama M, Goto S, Sakano T, Goto S. Detection of the Relationship between the Multi-Dimensional Data Sets of Serially Measured Blood Pressure and the Future Risk of Death in Healthy Elderly Japanese Population. J Atheroscler Thromb 2023; 30:1002-1009. [PMID: 36273901 PMCID: PMC10406660 DOI: 10.5551/jat.63798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023] Open
Abstract
AIMS Whether the multi-dimensional data of serially measured blood pressure contains information for predicting the future risk of death in elderly individuals in nursing homes is unclear. METHODS Of the elderly individuals staying in a nursing home, 19,740 and 40,055 individuals with serially measured blood pressure from day 1 to 365 (for AI-long) and 1 to 90 (for AI-short) along with the death information at day 366 to 730 and 91-365 were included. The neural network-based artificial intelligence (AI) was applied to find the relationship between BP time-series and the future risks of death in both populations. RESULTS AI-long found a significant relationship between the serially measured BP from day 1 to day 365 days and the risk of death occurring 366-730 days with c-statistics of 0.57 (95% CI: 0.51-0.63). AI-short also found a significant relationship between the serially measured BP from day 1 to day 90 and the rate of death occurring 91-365 days with c-statistics of 0.58 (95%CI: 0.52-0.63). CONCLUSION Our results suggest that neural network-based AI could find the hidden subtle relationship between multi-dimensional data of serially measured BP and the future risk of death in apparently healthy elderly Japanese individuals under nursing care.
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Affiliation(s)
- Masamitsu Nakayama
- Department of Medicine (Cardiology), Tokai University School of Medicine, Kanagawa, Japan
| | - Shinichi Goto
- Department of Medicine (Cardiology), Tokai University School of Medicine, Kanagawa, Japan
| | | | - Shinya Goto
- Department of Medicine (Cardiology), Tokai University School of Medicine, Kanagawa, Japan
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Alskaf E, Dutta U, Scannell CM, Chiribiri A. Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis. JOURNAL OF MEDICAL ARTIFICIAL INTELLIGENCE 2022; 5:11. [PMID: 36861064 PMCID: PMC7614252 DOI: 10.21037/jmai-22-36] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Background The application of deep learning on medical imaging is growing in prevalence in the recent literature. One of the most studied areas is coronary artery disease (CAD). Imaging of coronary artery anatomy is fundamental, which has led to a high number of publications describing a variety of techniques. The aim of this systematic review is to review the evidence behind the accuracy of deep learning applications in coronary anatomy imaging. Methods The search for the relevant studies, which applied deep learning on coronary anatomy imaging, was performed in a systematic approach on MEDLINE and EMBASE databases, followed by reviewing of abstracts and full texts. The data from the final studies was retrieved using data extraction forms. A meta-analysis was performed on a subgroup of studies, which looked at fractional flow reserve (FFR) prediction. Heterogeneity was tested using tau2, I2 and Q tests. Finally, a risk of bias was performed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) approach. Results A total of 81 studies met the inclusion criteria. The most common imaging modality was coronary computed tomography angiography (CCTA) (58%) and the most common deep learning method was convolutional neural network (CNN) (52%). The majority of studies demonstrated good performance metrics. The most common outputs were focused on coronary artery segmentation, clinical outcome prediction, coronary calcium quantification and FFR prediction, and most studies reported area under the curve (AUC) of ≥80%. The pooled diagnostic odds ratio (DOR) derived from 8 studies looking at FFR prediction using CCTA was 12.5 using the Mantel-Haenszel (MH) method. There was no significant heterogeneity amongst studies according to Q test (P=0.2496). Conclusions Deep learning has been used in many applications on coronary anatomy imaging, most of which are yet to be externally validated and prepared for clinical use. The performance of deep learning, especially CNN models, proved to be powerful and some applications have already translated into medical practice, such as computed tomography (CT)-FFR. These applications have the potential to translate technology into better care of CAD patients.
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Affiliation(s)
- Ebraham Alskaf
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Utkarsh Dutta
- GKT School of Medical Education, King’s College London, London, UK
| | - Cian M. Scannell
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK,Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Amedeo Chiribiri
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
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Sawano S, Kodera S, Sato M, Katsushika S, Sukeda I, Takeuchi H, Shinohara H, Kobayashi A, Takiguchi H, Hirose K, Kamon T, Saito A, Kiriyama H, Miura M, Minatsuki S, Kikuchi H, Higashikuni Y, Takeda N, Fujiu K, Ando J, Akazawa H, Morita H, Komuro I. Age prediction from coronary angiography using a deep neural network: Age as a potential label to extract prognosis-related imaging features. PLoS One 2022; 17:e0276928. [PMID: 36301966 PMCID: PMC9612526 DOI: 10.1371/journal.pone.0276928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 10/16/2022] [Indexed: 12/03/2022] Open
Abstract
Coronary angiography (CAG) is still considered the reference standard for coronary artery assessment, especially in the treatment of acute coronary syndrome (ACS). Although aging causes changes in coronary arteries, the age-related imaging features on CAG and their prognostic relevance have not been fully characterized. We hypothesized that a deep neural network (DNN) model could be trained to estimate vascular age only using CAG and that this age prediction from CAG could show significant associations with clinical outcomes of ACS. A DNN was trained to estimate vascular age using ten separate frames from each of 5,923 CAG videos from 572 patients. It was then tested on 1,437 CAG videos from 144 patients. Subsequently, 298 ACS patients who underwent percutaneous coronary intervention (PCI) were analysed to assess whether predicted age by DNN was associated with clinical outcomes. Age predicted as a continuous variable showed mean absolute error of 4 years with R squared of 0.72 (r = 0.856). Among the ACS patients stratified by predicted age from CAG images before PCI, major adverse cardiovascular events (MACE) were more frequently observed in the older vascular age group than in the younger vascular age group (p = 0.017). Furthermore, after controlling for actual age, gender, peak creatine kinase, and history of heart failure, the older vascular age group independently suffered from more MACE (hazard ratio 2.14, 95% CI 1.07 to 4.29, p = 0.032). The vascular age estimated based on CAG imaging by DNN showed high predictive value. The age predicted from CAG images by DNN could have significant associations with clinical outcomes in patients with ACS.
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Affiliation(s)
- Shinnosuke Sawano
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Satoshi Kodera
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
- * E-mail:
| | - Masataka Sato
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Susumu Katsushika
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Issei Sukeda
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hirotoshi Takeuchi
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroki Shinohara
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Atsushi Kobayashi
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroshi Takiguchi
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Kazutoshi Hirose
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Tatsuya Kamon
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Akihito Saito
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroyuki Kiriyama
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Mizuki Miura
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Shun Minatsuki
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hironobu Kikuchi
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Yasutomi Higashikuni
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Norifumi Takeda
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Katsuhito Fujiu
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
- Department of Advanced Cardiology, The University of Tokyo, Tokyo, Japan
| | - Jiro Ando
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroshi Akazawa
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroyuki Morita
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Issei Komuro
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
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Molenaar MA, Selder JL, Nicolas J, Claessen BE, Mehran R, Bescós JO, Schuuring MJ, Bouma BJ, Verouden NJ, Chamuleau SAJ. Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease. Curr Cardiol Rep 2022; 24:365-376. [PMID: 35347566 PMCID: PMC8979928 DOI: 10.1007/s11886-022-01655-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/03/2022] [Indexed: 12/17/2022]
Abstract
Purpose of Review Artificial intelligence (AI) applications in (interventional) cardiology continue to emerge. This review summarizes the current state and future perspectives of AI for automated imaging analysis in invasive coronary angiography (ICA). Recent Findings Recently, 12 studies on AI for automated imaging analysis In ICA have been published. In these studies, machine learning (ML) models have been developed for frame selection, segmentation, lesion assessment, and functional assessment of coronary flow. These ML models have been developed on monocenter datasets (in range 31–14,509 patients) and showed moderate to good performance. However, only three ML models were externally validated. Summary Given the current pace of AI developments for the analysis of ICA, less-invasive, objective, and automated diagnosis of CAD can be expected in the near future. Further research on this technology in the catheterization laboratory may assist and improve treatment allocation, risk stratification, and cath lab logistics by integrating ICA analysis with other clinical characteristics.
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Affiliation(s)
- Mitchel A Molenaar
- Amsterdam University Medical Centers-Location VU Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands.
- Amsterdam University Medical Centers-Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands.
| | - Jasper L Selder
- Amsterdam University Medical Centers-Location VU Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
| | - Johny Nicolas
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1030, New York, NY, 10029-6574, USA
| | - Bimmer E Claessen
- Amsterdam University Medical Centers-Location VU Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
| | - Roxana Mehran
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1030, New York, NY, 10029-6574, USA
| | | | - Mark J Schuuring
- Amsterdam University Medical Centers-Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
| | - Berto J Bouma
- Amsterdam University Medical Centers-Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
| | - Niels J Verouden
- Amsterdam University Medical Centers-Location VU Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
| | - Steven A J Chamuleau
- Amsterdam University Medical Centers-Location VU Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam University Medical Centers-Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers-Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
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Liu MH, Zhao C, Wang S, Jia H, Yu B. Artificial Intelligence—A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome. Front Cardiovasc Med 2022; 8:782971. [PMID: 35252367 PMCID: PMC8888682 DOI: 10.3389/fcvm.2021.782971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 12/29/2021] [Indexed: 11/19/2022] Open
Abstract
Acute coronary syndrome is the leading cause of cardiac death and has a significant impact on patient prognosis. Early identification and proper management are key to ensuring better outcomes and have improved significantly with the development of various cardiovascular imaging modalities. Recently, the use of artificial intelligence as a method of enhancing the capability of cardiovascular imaging has grown. AI can inform the decision-making process, as it enables existing modalities to perform more efficiently and make more accurate diagnoses. This review demonstrates recent applications of AI in cardiovascular imaging to facilitate better patient care.
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Affiliation(s)
- Ming-hao Liu
- Department of Cardiology, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China
- The Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin, China
| | - Chen Zhao
- Department of Cardiology, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China
- The Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin, China
| | - Shengfang Wang
- Department of Cardiology, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China
- The Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin, China
| | - Haibo Jia
- Department of Cardiology, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China
- The Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin, China
- *Correspondence: Haibo Jia
| | - Bo Yu
- Department of Cardiology, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China
- The Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin, China
- Bo Yu
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