Artificial Intelligence in Pediatric Cardiology: A Scoping Review

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.

Artificial Intelligence in Cardiology: Why So Many Great Promises and Expectations, but Still a Limited Clinical Impact?

“…Artificial intelligence has been widely applied in the field of cardiovascular imaging, including echocardiography, cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging [ 1 , 2 ].…”

“…Looking at the extremely large amount of literature, as summarized in two recent reviews on applications of Artificial Intelligence in Cardiology, both in the adult and pediatric age groups, published in the Journal of Clinical Medicine [ 1 , 2 ], we can easily affirm that the future of AI in this field should be bright and extremely helpful and fruitful, both for cardiologists and patients. We can then expect that the implementation of AI to different aspects of cardiology discipline will induce great advantages, improvements, and important innovations, which could have a great impact on daily practice.…”

“…We can then expect that the implementation of AI to different aspects of cardiology discipline will induce great advantages, improvements, and important innovations, which could have a great impact on daily practice. All this would include multi-modality imaging improvements, better digital infrastructures, fundamental support to diagnostic, therapeutic, and prognostic techniques and clinical approaches, and to the clinical decision-making process, leading to optimized organizations and procedures [ 1 , 2 ]. Integrating AI into cardiology practice is a change that the profession will embrace.…”

“…Integrating AI into cardiology practice is a change that the profession will embrace. In fact, AI has the potential to support physicians’ knowledge and decisions, towards a precision cardiology and to a more efficient, and hopefully also more efficacious, health care [ 1 , 2 ]. Despite this apparent tremendous potential, the impact of AI in current clinical cardiology practice is still limited [ 1 , 2 ].…”

“…In fact, AI has the potential to support physicians’ knowledge and decisions, towards a precision cardiology and to a more efficient, and hopefully also more efficacious, health care [ 1 , 2 ]. Despite this apparent tremendous potential, the impact of AI in current clinical cardiology practice is still limited [ 1 , 2 ]. In recent years, the Journal of Clinical Medicine has published several contributions in this field, which are the subject of this Editorial and could help to better understand the increased interest of the scientific community in the application of AI in cardiology, but also the difficulties and obstacles to be overcome.…”