Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms

Hirota, Naomi; Suzuki, Shinya; Motogi, Jun; Nakai, Hiroshi; Matsuzawa, Wataru; Takayanagi, T; Umemoto, Takuya; Hyodo, Akira; Satoh, Keiichi; Arita, Takuto; Yagi, Nobuyuki; Otsuka, Takayuki; Yamashita, Takeshi

doi:10.1016/j.ijcha.2023.101172

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…One of the inclusion criteria was ECG sinus rhythm, so the conclusions cannot be extrapolated to patients with arrhythmias. Important patient data such as cardiac morphology, comorbidities including frailty syndrome, or the effects of medication were not included in the model [16].…”

Section: Resultsmentioning

confidence: 99%

“…Valvular diseases [11] Left ventricular hypertrophy [12] Onset of atrial fibrillation [13,14] Risk of sudden cardiac death [15] Biological age of the patient [16] Concentration of sex hormones [17] Heart transplant rejection [18] Cardiovascular complications of liver transplantation [19] Effects of cardiac resynchronization therapy [20] Pulmonary embolism [21] Identity verification [22]…”

Section: Subject Of Interest Reference Numbermentioning

confidence: 99%

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What Else Can AI See in a Digital ECG?

Rechciński

2023

JPM

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The electrocardiogram (ECG), considered by some diagnosticians of cardiovascular diseases to be a slightly anachronistic tool, has acquired a completely new face and importance thanks to its three modern features: the digital form of recording, its very frequent use, and the possibility of processing thousands of records by artificial intelligence (AI). In this review of the literature on this subject from the first 3 months of 2023, the use of many types of software for extracting new information from the ECG is described. These include, among others, natural language processing, backpropagation neural network and convolutional neural network. AI tools of this type allow physicians to achieve high accuracy not only in ECG-based predictions of the patient’s age or sex but also of the abnormal structure of heart valves, abnormal electrical activity of the atria, distorted immune response after transplantation, good response to resynchronization therapy and an increased risk of sudden cardiac death. The attractiveness of the presented results lies in the simplicity of the examination by the staff, relatively low costs and even the possibility of performing the examination remotely. The twelve studies presented here are just a fraction of the novelties that the current year will bring.

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Section: Resultsmentioning

confidence: 99%

Section: Subject Of Interest Reference Numbermentioning

confidence: 99%

What Else Can AI See in a Digital ECG?

Rechciński

2023

JPM

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“…The CNN model in the present study was constructed based on the model by Attia et al [14] , [15] , [18] The detail architecture of the CNN model is shown in supplementary Fig. 2 .…”

Section: Methodsmentioning

confidence: 99%

“…Recently, we have created a digital ECG dataset connected to our single-center cohort database and have reported several conventional machine learning models [11] , [12] , [13] as well as CNN models [14] , [15] for predicting heart age, atrial fibrillation, cardiovascular events, and mortality. Leveraging this database, our objective was to develop a CNN model using ECGs for the detection of AD, with the goal of enhancing the AD screening strategy.…”

Section: Introductionmentioning

confidence: 99%

Identifying patients with acute aortic dissection using an electrocardiogram with convolutional neural network

Arita,

Suzuki,

Motogi

et al. 2024

IJC Heart & Vasculature

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“…Artificial intelligence has found its usefulness in ECG for the following purposes: diagnosis of pulmonary thromboembolism [ 95 ], prediction of sudden death and cardiovascular events [ 97 , 98 ], prediction of fatal events after cardiac resynchronization [ 99 ], prediction of paroxysmal atrial fibrillation [ 37 ], detection of ventricular hypertrophy [ 100 ], risk prediction in liver transplantation [ 101 ], detection of ventricular dysfunction [ 103 ], and prediction of recurrence after paroxysmal atrial fibrillation ablation [ 38 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Stamate,

Piraianu,

Ciobotaru

et al. 2024

Diagnostics

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Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

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Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms

Cited by 7 publications

References 27 publications

What Else Can AI See in a Digital ECG?

What Else Can AI See in a Digital ECG?

Identifying patients with acute aortic dissection using an electrocardiogram with convolutional neural network

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

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