Holt Dudley Byron, El-Bokl Amr, Stromberg Daniel, Taylor Michael D
Department of Pediatrics, University of Texas at Austin Dell Medical School, Austin, Texas.
Texas Center for Pediatric and Congenital Heart Disease, Dell Children's Medical Center, Austin, Texas.
J Soc Cardiovasc Angiogr Interv. 2025 Mar 18;4(3Part B):102567. doi: 10.1016/j.jscai.2025.102567. eCollection 2025 Mar.
Artificial intelligence has promising impact on patients with congenital heart disease, a vulnerable population with life-long health care needs and, often, a substantially higher risk of death than the general population. This review explores the role artificial intelligence has had on cardiac imaging, electrophysiology, interventional procedures, and intensive care monitoring as it relates to children and adults with congenital heart disease. Machine learning and deep learning algorithms have enhanced not only imaging segmentation and processing but also diagnostic accuracy namely reducing interobserver variability. This has a meaningful impact in complex congenital heart disease improving anatomic diagnosis, assessment of cardiac function, and predicting long-term outcomes. Image processing has benefited procedural planning for interventional cardiology, allowing for a higher quality and density of information to be extracted from the same imaging modalities. In electrophysiology, deep learning models have enhanced the diagnostic potential of electrocardiograms, detecting subtle yet meaningful variation in signals that enable early diagnosis of cardiac dysfunction, risk stratification of mortality, and more accurate diagnosis and prediction of arrhythmias. In the congenital heart disease population, this has the potential for meaningful prolongation of life. Postoperative care in the cardiac intensive care unit is a data-rich environment that is often overwhelming. Detection of subtle data trends in this environment for early detection of morbidity is a ripe avenue for artificial intelligence algorithms to be used. Examples like early detection of catheter-induced thrombosis have already been published. Despite their great promise, artificial intelligence algorithms are still limited by hurdles such as data standardization, algorithm validation, drift, and explainability.
人工智能对先天性心脏病患者具有深远影响，这一弱势群体有着终身的医疗需求，且通常比普通人群面临更高的死亡风险。本综述探讨了人工智能在与先天性心脏病儿童和成人相关的心脏成像、电生理学、介入手术及重症监护监测方面所发挥的作用。机器学习和深度学习算法不仅提高了成像分割与处理能力，还提升了诊断准确性，即减少了观察者间的差异。这对复杂先天性心脏病具有重要意义，有助于改善解剖诊断、心脏功能评估及预测长期预后。图像处理有利于介入心脏病学的手术规划，能从相同成像模式中提取更高质量和密度的信息。在电生理学方面，深度学习模型增强了心电图的诊断潜力，可检测出信号中细微但有意义的变化，从而实现心脏功能障碍的早期诊断、死亡风险分层以及心律失常的更准确诊断和预测。对于先天性心脏病患者群体而言，这有可能显著延长寿命。心脏重症监护病房的术后护理是一个数据丰富但往往令人应接不暇的环境。在这种环境中检测细微的数据趋势以早期发现发病情况，是人工智能算法得以应用的成熟途径。诸如早期检测导管诱导血栓形成等例子已见诸报道。尽管人工智能算法前景广阔，但仍受到数据标准化、算法验证、漂移和可解释性等障碍的限制。
