Artificial Intelligence in Pediatric Cardiology: A Scoping Review

Sethi, Yashendra; Patel, Neil; Kaka, Nirja; Desai, Ami; Kaiwan, Oroshay; Sheth, Mili; Sharma, Rupal; Huang, Helen; Chopra, Hitesh; Khandaker, Mayeen Uddin; Lashin, Maha M. A.; Hamd, Zuhal Y.; Emran, Talha Bin

doi:10.3390/jcm11237072

Open AccessReview

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

by

Yashendra Sethi

^1,2

,

Neil Patel

^1,3

,

Nirja Kaka

^1,3

,

Ami Desai

⁴,

Oroshay Kaiwan

^1,5,*

,

Mili Sheth

⁶

,

Rupal Sharma

⁷,

Helen Huang

⁸,

Hitesh Chopra

⁹

,

Mayeen Uddin Khandaker

¹⁰

,

Maha M. A. Lashin

¹¹,

Zuhal Y. Hamd

^12,*

and

Talha Bin Emran

^13,14,*

¹

PearResearch, Dehradun 248001, India

²

Department of Medicine, Government Doon Medical College, Dehradun 248001, India

³

Department of Medicine, GMERS Medical College, Himmatnagar 383001, India

⁴

Department of Medicine, SMIMER Medical College, Surat 395010, India

⁵

Department of Medicine, Northeast Ohio Medical University, Rootstown, OH 44272, USA

⁶

Department of Medicine, GMERS Gandhinagar, Gandhinagar 382012, India

⁷

Department of Medicine, Government Medical College, Nagpur 440003, India

⁸

Faculty of Medicine and Health Science, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland

⁹

Chitkara College of Pharmacy, Chitkara University, Rajpura 140401, India

¹⁰

Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, Bandar Sunway 47500, Malaysia

¹¹

Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. 84428, Riyadh 11671, Saudi Arabia

¹²

Department of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. 84428, Riyadh 11671, Saudi Arabia

¹³

Department of Pharmacy, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh

¹⁴

Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh

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^*

Authors to whom correspondence should be addressed.

J. Clin. Med. 2022, 11(23), 7072; https://doi.org/10.3390/jcm11237072

Submission received: 5 November 2022 / Revised: 22 November 2022 / Accepted: 26 November 2022 / Published: 29 November 2022

(This article belongs to the Special Issue Clinical Research Advances in Congenital Heart Disease)

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Abstract

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.

Keywords: artificial intelligence; pediatric cardiology; pediatric cardiac surgery; machine learning; congenital heart diseases

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MDPI and ACS Style

Sethi, Y.; Patel, N.; Kaka, N.; Desai, A.; Kaiwan, O.; Sheth, M.; Sharma, R.; Huang, H.; Chopra, H.; Khandaker, M.U.; et al. Artificial Intelligence in Pediatric Cardiology: A Scoping Review. J. Clin. Med. 2022, 11, 7072. https://doi.org/10.3390/jcm11237072

AMA Style

Sethi Y, Patel N, Kaka N, Desai A, Kaiwan O, Sheth M, Sharma R, Huang H, Chopra H, Khandaker MU, et al. Artificial Intelligence in Pediatric Cardiology: A Scoping Review. Journal of Clinical Medicine. 2022; 11(23):7072. https://doi.org/10.3390/jcm11237072

Chicago/Turabian Style

Sethi, Yashendra, Neil Patel, Nirja Kaka, Ami Desai, Oroshay Kaiwan, Mili Sheth, Rupal Sharma, Helen Huang, Hitesh Chopra, Mayeen Uddin Khandaker, and et al. 2022. "Artificial Intelligence in Pediatric Cardiology: A Scoping Review" Journal of Clinical Medicine 11, no. 23: 7072. https://doi.org/10.3390/jcm11237072

APA Style

Sethi, Y., Patel, N., Kaka, N., Desai, A., Kaiwan, O., Sheth, M., Sharma, R., Huang, H., Chopra, H., Khandaker, M. U., Lashin, M. M. A., Hamd, Z. Y., & Emran, T. B. (2022). Artificial Intelligence in Pediatric Cardiology: A Scoping Review. Journal of Clinical Medicine, 11(23), 7072. https://doi.org/10.3390/jcm11237072

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Artificial Intelligence in Pediatric Cardiology: A Scoping Review

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