In drug‐resistant epilepsy, magnetic resonance imaging (MRI) plays a central role in detecting lesions as it offers unmatched spatial resolution and whole‐brain coverage. In addition, the last decade has witnessed continued developments in MRI‐based computer‐aided machine‐learning techniques for improved diagnosis and prognosis. In this review, we focus on automated algorithms for the detection of hippocampal sclerosis and focal cortical dysplasia, particularly in cases deemed as MRI negative, with an emphasis on studies with histologically validated data. In addition, we discuss imaging‐derived prognostic markers, including response to anti‐seizure medication, post‐surgical seizure outcome, and cognitive reserves. We also highlight the advantages and limitations of these approaches and discuss future directions toward person‐centered care.

中文翻译：

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使用自动化和人工智能驱动的算法来检测海马硬化和局灶性皮质发育不良

在耐药性癫痫中，磁共振成像 (MRI) 在检测病变方面发挥着核心作用，因为它提供了无与伦比的空间分辨率和全脑覆盖。此外，过去十年见证了基于 MRI 的计算机辅助机器学习技术的持续发展，以改善诊断和预后。在这篇综述中，我们重点关注用于检测海马硬化和局灶性皮质发育不良的自动化算法，特别是在 MRI 阴性的病例中，重点是使用组织学验证的数据进行的研究。此外，我们还讨论了影像学预后标志物，包括抗癫痫药物的反应、术后癫痫结果和认知储备。我们还强调了这些方法的优点和局限性，并讨论了以人为本的护理的未来方向。