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心房颤动作为全球最常见的持续性心律失常,其高发病率和死亡率对公共卫生系统构成了重大经济负担。早期发现房颤对于及时治疗和预防如中风等并发症至关重要。尽管12导联心电图是诊断房颤的金标准,但其依赖于心脏病专家的临床经验,并可能受到观察者间差异的影响。人工智能(AI),尤其是深度学习(DL)中的卷积神经网络(CNN),在图像识别任务中取得了显著进展,能够以更高的准确性和效率自动评估复杂医学图像。本文旨在全面概述人工智能技术,探讨其在房颤疾病研究与临床应用中的潜力。
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