Dawood Iyas, Alhussein Samahir Taha, Wadi Wefag Yahya Adam, Abdalgadir Rana Abdalgadir Yousif, Mohammed Sarah Siddig Ibrahim, Ahmed Elaf Hamza Makkawi
Faculty of Medicine, Omdurman Islamic University, Khartoum, Sudan.
College of Medicine, Elrazi University, Khartoum, Sudan.
Ann Pediatr Cardiol. 2025 Jan-Feb;18(1):42-48. doi: 10.4103/apc.apc_236_24. Epub 2025 Jul 14.
Viral myocarditis is the inflammation of heart myocytes resulting from viral infection. Incidence in the pediatric population could reach 2 per 100,000 per year, and COVID-19 infection is a significant risk factor, which increases the possibility of having an infection by 40 times. Early detection results in catching the disease early and consequently improves outcomes. Clinical presentation of viral myocarditis in children could vary from mild prodromal symptoms to severe heart failure. Clinical examination, electrocardiogram, and chest X-ray may give clues for physiological and structural signs usually associated with the disease. However, they are inconclusive as they lack both accuracy and specificity. Biomarkers used to track the disease usually lack sensitivity and specificity. Cardiac magnetic resonance (CMR) is the imaging of choice to diagnose viral myocarditis by showing edema and late gadolinium enhancement. Point-of-care ultrasound has been approved as a good imaging method for early detection. It can be used as an effective screening tool for high-risk patients. Positron emission tomography scan is very sensitive in detecting disease early in its acute phase, especially if combined with CMR. All imaging studies are prone to interpretation bias, leading to a misdiagnosis. Endomyocardial biopsy is the gold standard method for diagnosis. However, it is time-consuming and ineffective as an early detection tool. Artificial intelligence (AI) helps with interpretation, decreasing bias, improving accuracy, and saving time and manpower. With more research and evidence, adopting AI-based methods to diagnose myocarditis in pediatrics could offer early detection, reduce costs, and save time for early intervention. Genetics helps identify inflammatory pathways involved in vulnerable patients, and genetic therapy may suppress disease progression by mitigating these pathways. Research focused on children is highly encouraged, and collaboration between healthcare institutions to develop telemedicine-based programs is influential.
病毒性心肌炎是由病毒感染引起的心肌细胞炎症。儿科人群中的发病率可达每年十万分之二,而新冠病毒感染是一个重要风险因素,会使感染可能性增加40倍。早期检测有助于尽早发现疾病,从而改善预后。儿童病毒性心肌炎的临床表现可能从轻微前驱症状到严重心力衰竭不等。临床检查、心电图和胸部X光检查可能会提供通常与该疾病相关的生理和结构体征线索。然而,这些检查缺乏准确性和特异性,因此诊断结果不明确。用于追踪该疾病的生物标志物通常缺乏敏感性和特异性。心脏磁共振成像(CMR)通过显示水肿和钆延迟强化,是诊断病毒性心肌炎的首选成像方法。即时超声已被批准为早期检测的良好成像方法。它可作为高危患者的有效筛查工具。正电子发射断层扫描在疾病急性期早期检测中非常敏感,尤其是与CMR结合使用时。所有成像研究都容易出现解读偏差,导致误诊。心内膜心肌活检是诊断的金标准方法。然而,它作为早期检测工具既耗时又无效。人工智能(AI)有助于解读,减少偏差,提高准确性,节省时间和人力。随着更多研究和证据的出现,采用基于AI的方法诊断儿科心肌炎可以实现早期检测,降低成本,并节省早期干预的时间。遗传学有助于识别易患患者中涉及的炎症途径,基因治疗可能通过减轻这些途径来抑制疾病进展。强烈鼓励开展针对儿童的研究,医疗机构之间合作开展基于远程医疗的项目具有重要意义。