Diagnosing Atrial Septal Defect from Electrocardiogram with Deep Learning

Mori, Hiroki; Inai, Kei; Sugiyama, Hisashi; Muragakı, Yoshihiro

doi:10.1007/s00246-021-02622-0

Cited by 26 publications

(18 citation statements)

References 27 publications

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“…Machine learning-based systems have been useful in differentiating pathological versus physiological hypertrophic remodeling of the heart [ 59 ]. Deep learning models have demonstrated superiority in terms of sensitivity and specificity up to 76% and 88%, respectively, in diagnosing atrial septal defect (ASD) compared with pediatric cardiologists who demonstrated sensitivity and specificity of 53% ± 0.04 and 67% ± 0.10 respectively [ 35 ]. Automation, AI, and machine learning are game-changing as complementary tools to physicians and in areas with limited expert medical personnel or cardiologists [ 60 ].…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

Sethi

Patel²,

Kaka³

et al. 2022

JCM

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The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. AI modalities have expanded feet to the specialty of pediatric cardiology as well. We conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002–2022. We found that the use of neural networks and machine learning has significantly improved the diagnostic value of cardiac magnetic resonance imaging, echocardiograms, computer tomography scans, and electrocardiographs, thus augmenting the clinicians’ diagnostic accuracy of pediatric heart diseases. The use of AI-based prediction algorithms in pediatric cardiac surgeries improves postoperative outcomes and prognosis to a great extent. Risk stratification and the prediction of treatment outcomes are feasible using the key clinical findings of each CHD with appropriate computational algorithms. Notably, AI can revolutionize prenatal prediction as well as the diagnosis of CHD using the EMR (electronic medical records) data on maternal risk factors. The use of AI in the diagnostics, risk stratification, and management of CHD in the near future is a promising possibility with current advancements in machine learning and neural networks. However, the challenges posed by the dearth of appropriate algorithms and their nascent nature, limited physician training, fear of over-mechanization, and apprehension of missing the ‘human touch’ limit the acceptability. Still, AI proposes to aid the clinician tomorrow with precision cardiology, paving a way for extremely efficient human-error-free health care.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

Sethi

Patel²,

Kaka³

et al. 2022

JCM

View full text Add to dashboard Cite

show abstract

“…al. [24] established progressed diagnostic accuracy found out by incorporating a proposed deep learning model, comprising a convolutional neural network (CNN) and long short-term memory (LSTM), along side ECGs [25] for figuring out ASD. They used a deep learning model comprising a CNN and LTSMs [26].…”

Section: Deep Learning For the Recognition Of Asdmentioning

confidence: 99%

A review on the impact of deep learning in the identification of atrial septal defect and a comparative study on the algorithms employed in the imaging modalities

2022

pnr

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Among the congenital coronary heart diseases, atrial septal defect constitutes the 1/3 most common type. In many cases, the affected person stays asymptomatic for the duration of the youth even having big shunts. Methodologies that may be hired for figuring out the defects are : echocardiogram , chest X-ray, electrocardiogram, cardiac catheterization, MRI, CT scan, phonocardiogram . Deep learning may be correctly utilised for the automatic estimation of the illness from the test result. The purpose of this review paper is to offer an perception into ASD, the strategies for figuring out it and the application of deep learning models for distinguishing the illness. The paper opinions diverse algorithms used for identity of defects also points out the restrictions of every algorithm.

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“…Although an ECG may aid in diagnosis, it is difficult to detect specific problems. Hiroki Mori et al exhibited enhanced diagnosis accuracy for Atrial Septal Defect by combining electrocardiograms [23] with a suggested deep learning model that included a convolutional neural network (CNN) and long shortterm memory (LSTM) [22]. They made use of We employed a CNN and LTSMs-based deep learning model [24].…”

Section: Deep Learning For the Recognition Of Asdmentioning

confidence: 99%

A review on identification of atrial septal defect using deep learning

P¹,

Rahiman²,

Abraham³

et al. 2022

MJAERT

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The third most prevalent kind of congenital cardiac disease is atrial septal defects (ASD). Even with extensive shunts, the majority of individuals remain asymptomatic throughout their infancy. Echocardiogram, Chest X-ray, Electrocardiogram (ECG), Cardiac catheterization, MRI, and CT scan may all be used to detect the abnormality. Deep learning can be employed for automated estimation of the defect from the test result. The goal of this review paper is first to provide an insight into ASD, the methods for diagnosis, the application of deep learning models for distinguishing the defect, defect detection accuracy and algorithm parameters.

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Diagnosing Atrial Septal Defect from Electrocardiogram with Deep Learning

Cited by 26 publications

References 27 publications

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

A review on the impact of deep learning in the identification of atrial septal defect and a comparative study on the algorithms employed in the imaging modalities

A review on identification of atrial septal defect using deep learning

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