Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

Cho, Younghoon; Kwon, Joon‐myoung; Kim, Kyung‐Hee; Medina‐Inojosa, José R.; Jeon, Ki‐Hyun; Cho, Soohyun; Lee, Soo-Youn; Park, Jinsik; Oh, Byung‐Hee

doi:10.1038/s41598-020-77599-6

Cited by 88 publications

(67 citation statements)

References 27 publications

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“…The most important aspect of deep learning is its ability to extract features and develop an algorithm using various types of data, such as images, 2D data, and waveforms. In previous studies, Attia and colleagues and our study group developed a deep learning‐based model to screen for heart failure, arrhythmia, valvular heart disease, left ventricular hypertrophy, and anemia (Attia, Friedman, et al., 2019; Attia, Kapa, et al., 2019; Attia, Noseworthy, et al., 2019; Cho et al., 2020; Galloway et al., 2019; Jo et al., 2020; Kwon, Cho, et al., 2020; Kwon, Kim, et al., 2020; Kwon, Lee, et al., 2020). In recent studies, Attia and colleagues showed that hyperkalemia and hypokalemia could be detected using ECG based on a deep learning model (Galloway et al., 2019).…”

Section: Discussionmentioning

confidence: 99%

“…The second hypothesis is that limb 6‐lead ECG already had information on precordial 6‐lead ECG. In previous studies, Cho et al have already developed a model to generate precordial 6‐lead ECG from limb 6‐lead ECG (Cho et al., 2020). In some tasks, information about the specific disease of precordial 6‐lead is already reflected in limb 6‐lead ECG.…”

Section: Discussionmentioning

confidence: 99%

“…It is not easy to make diagnostic tools based on conventional statistical methods using such subtle ECG changes. Deep learning has previously been used in the medical field to identify lesions and is currently used to analyze ECGs to diagnose heart failure, valvular heart disease, anemia, and coronary artery disease (Attia, Friedman, et al., 2019; Attia, Kapa, et al., 2019; Attia, Noseworthy, et al., 2019; Cho et al., 2020; Galloway et al., 2019; Jo et al., 2020; Kwon, Cho, et al., 2020; Kwon, Kim, et al., 2020; Kwon, Lee, et al., 2020). Recent studies have shown that deep learning models can detect dyskalemia using ECG (Galloway et al., 2019; Lin et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Kwon

Jung²,

Kim

et al. 2021

Noninvasive Electrocardiol

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Introduction:The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study. Methods and Results: This retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study. A DLM was developed using 83,449 ECGs of 48,356 patients; the internal validation included 12,091 ECGs of 12,091 patients.We conducted an external validation with 31,693 ECGs of 31,693 patients from another hospital, and the result was electrolyte imbalance detection. During internal, the area under the receiving operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.945, 0.866, 0.944, 0.885, 0.905, and 0.901, respectively. The values during external validation of the AUC of hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.873, 0.857, 0.839, 0.856, 0.831, and 0.813 respectively. The DLM helped to visualize the important ECG region for detecting each electrolyte imbalance, and it showed how the P wave, QRS complex, or T wave differs in importance in detecting each electrolyte imbalance. Conclusion:The proposed DLM demonstrated high performance in detecting electrolyte imbalance. These results suggest that a DLM can be used for detecting and monitoring electrolyte imbalance using ECG on a daily basis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Kwon

Jung²,

Kim

et al. 2021

Noninvasive Electrocardiol

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, a number of ECG-based AI algorithms have shown great performance in detecting cardiac conditions, such as LVSD [ 117 , 118 ], HCM [ 119 ] and MI [ 120 ]. The performance of these AI algorithms is typically better than risk scores currently used in routine practice, e.g., the CHA 2 DS 2 -VASc score.…”

Section: Artificial Intelligencementioning

confidence: 99%

Smart Wearables for Cardiac Monitoring—Real-World Use beyond Atrial Fibrillation

Duncker

Ding

Etheridge

et al. 2021

Sensors

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The possibilities and implementation of wearable cardiac monitoring beyond atrial fibrillation are increasing continuously. This review focuses on the real-world use and evolution of these devices for other arrhythmias, cardiovascular diseases and some of their risk factors beyond atrial fibrillation. The management of nonatrial fibrillation arrhythmias represents a broad field of wearable technologies in cardiology using Holter, event recorder, electrocardiogram (ECG) patches, wristbands and textiles. Implementation in other patient cohorts, such as ST-elevation myocardial infarction (STEMI), heart failure or sleep apnea, is feasible and expanding. In addition to appropriate accuracy, clinical studies must address the validation of clinical pathways including the appropriate device and clinical decisions resulting from the surrogate assessed.

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“…The application of this technology in numerous healthcare and medical fields acting as a decision assistance tool will provide aid from diagnosis to prognosis [6]. For example, detecting a myocardial infarction using a six lead ECG with a validated deep learning bases AI algorithm [7]. The application of these technologies is to minimize the loss caused by this pandemic.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 and Artificial Intelligence: the pandemic pacifier

Banerjee¹,

Robinson²,

Kashyap³

et al. 2020

Nepal J Epidemiology

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COVID-19 remains a threat to the entire world. In an attempt to curb its spread and facilitate its treatment, the technological tool that is Artificial Intelligence (AI) is being researched as a potential alternative to conventional methods. Industrial Revolution 4.0 marks the dawn to the combination of digital, physical and biological systems, by application of digital skills such as Blockchain, Internet of things, Artificial Intelligence and Big data. AI tools in SARS-COV-2 pandemic are highly competitive to human performance, such as rapid screening and diagnosis of the disease, surveilling the efficacy of the treatment, keeping record and depicting active cases and mortality, inventions of medications and vaccines, relieving the workload of healthcare workers and extinguishing the spread of the disease. Contact tracing platforms like Aarogya Setu App, implemented by the Government of India, Australian Government's COVID Safe app, Trace Together- a Bluetooth-based contact tracing app developed in Singapore; based on syndromic mapping/surveillance technology. Artificial intelligence will become a mainstay in both the diagnosis and treatment of COVID-19 as well as similar pandemics in future. The application and system development will be challenging; the accuracy and rapidity of its use far outweigh this drawback. The current global technological leaders have proven that the retro modification of current data systems and applications have been indispensable in the war on COVID-19, thus permanently securing their development and application in future.

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Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

Cited by 88 publications

References 27 publications

Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Smart Wearables for Cardiac Monitoring—Real-World Use beyond Atrial Fibrillation

COVID-19 and Artificial Intelligence: the pandemic pacifier

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