通过使用MRI的人工智能检测焦点皮层发育不良:系统性审查和元分析
Mohammad Dashtkoohi1, Delaram J Ghadimi2, Farzan Moodi3
1Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran; Interdisciplinary Neuroscience Research Program, Tehran University of Medical Sciences, Tehran, Iran.
Epilepsy & behavior : E&B
|March 30, 2025
概括
机器学习和人工神经网络 (ANN) 对患者的焦点皮质发育不良 (FCD) 检测有希望,提高MRI准确性. 然而,需要进一步的研究来将这些AI工具整合到常规的临床实践中.
科学领域:
- 神经学 神经学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 焦点皮层发育不良 (FCD) 是抗药性的主要原因.
- 磁共振成像 (MRI) 单独可以在检测FCD时面临挑战.
- 人工智能 (AI) 提供了增强诊断能力的潜力.
研究的目的:
- 系统地审查和分析利用机器学习 (ML) 和人工神经网络 (ANN) 来检测FCD的研究.
- 评估AI增强型MRI在识别FCD病变方面的有效性.
- 在FCD患者中评估AI模型的诊断性能.
主要方法:
- 在Embase,PubMed,Scopus和Web of Science进行了系统的文献搜索.
- 通过使用QUADAS-AI评估了研究质量.
- 使用双变随机效应元分析来确定聚合的灵敏度和特异性.
主要成果:
- 该审查包括41项研究 (24项基于ANN,17项基于ML).
- 人工智能模型在内部验证数据集上显示了0.81的聚合灵敏度和0.92的特异性.
- 外部验证显示,聚合灵敏度为0.73和特异性为0.66,具有中等异质性.
结论:
- 虽然ML和ANN模型显示出FCD检测的潜力,但它们的临床实用性目前有限.
- 进一步细化,优化和纵向研究对于临床整合至关重要.
- 解决目前的局限性对于提高AI在FCD诊断中的可靠性至关重要.
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