Abstract：
Familial hypercholesterolemia (FH) poses a global health challenge due to high incidence rates and underdiagnosis, leading to increased risks of early-onset atherosclerosis and cardiovascular diseases. Early detection and treatment of FH is critical in reducing the risk of cardiovascular events and improving the long-term outcomes and quality of life for affected individuals and their families. Traditional therapeutic approaches revolve around lipid-lowering interventions, yet challenges persist, particularly in accurate and timely diagnosis. The current diagnostic landscape heavily relies on genetic testing of specific LDL-C metabolism genes, often limited to specialized centers. This constraint has led to the adoption of alternative clinical scores for FH diagnosis. However, the rapid advancements in artificial intelligence (AI) and machine learning (ML) present promising solutions to these diagnostic challenges. This review explores the intricacies of FH, highlighting the challenges that are encountered in the diagnosis and management of the disorder. The revolutionary potential of ML, particularly in large-scale population screening, is highlighted. Applications of ML in FH screening, diagnosis, and risk stratification are discussed, showcasing its ability to outperform traditional criteria. However, challenges and ethical considerations, including algorithmic stability, data quality, privacy, and consent issues, are crucial areas that require attention. The review concludes by emphasizing the significant promise of AI and ML in FH management while underscoring the need for ethical and practical vigilance to ensure responsible and effective integration into healthcare practices.
摘要：
家族性高胆固醇血症（FH）由于发病率高和诊断不足而构成了全球健康挑战。导致早发性动脉粥样硬化和心血管疾病的风险增加。FH的早期发现和治疗对于降低心血管事件的风险和改善受影响的个人及其家人的长期结果和生活质量至关重要。传统的治疗方法围绕降脂干预，然而挑战依然存在，特别是在准确和及时的诊断。当前的诊断环境严重依赖于特定LDL-C代谢基因的基因检测，通常仅限于专业中心。这种限制导致FH诊断采用替代临床评分。然而,人工智能（AI）和机器学习（ML）的快速发展为这些诊断挑战提供了有希望的解决方案。这篇综述探讨了FH的复杂性，强调在疾病诊断和管理中遇到的挑战。ML的革命性潜力，特别是在大规模人群筛查中，突出显示。ML在FH筛查中的应用,诊断,并讨论了风险分层，展示其超越传统标准的能力。然而,挑战和道德考虑，包括算法稳定性，数据质量，隐私,和同意问题，是需要关注的关键领域。审查最后强调了AI和ML在FH管理中的重要前景，同时强调了道德和实践警惕的必要性，以确保负责任和有效地整合到医疗保健实践中。
