Epilepsy Surgery in Drug-Resistant Epilepsy Facilitated by Artificial Intelligence: A Case of Left Temporal Encephalocele.

IF 0.4 Q4 CLINICAL NEUROLOGY
Case Reports in Neurology Pub Date : 2025-12-12 eCollection Date: 2026-01-01 DOI:10.1159/000550076
Mason Henry Crossman, Jasmin Chau Minh Le, Alexander William Wright, Toby Gilbert, John Maddison, Stephen Bacchi
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Abstract

Introduction: Epilepsy surgery remains underutilized despite strong evidence supporting its efficacy for appropriately selected patients with drug-resistant epilepsy (DRE).

Case presentation: We describe the case of a 23-year-old woman with a 4-year history of refractory focal impaired awareness seizures, experiencing up to four episodes per week despite multiple trialed antiseizure medications at tolerated doses. Initial investigations including magnetic resonance imaging (MRI), EEG, and paraneoplastic screening were unremarkable, and she was managed as having non-lesional left temporal lobe epilepsy. As part of an artificial intelligence driven research initiative, a natural language processing based random forest algorithm reviewing neurology clinic documentation identified her as a potential surgical candidate. Complementary large language model analysis supported extraction of relevant seizure and imaging information. This prompted re-evaluation of the case, with PET demonstrating left temporal hypometabolism and repeat MRI revealing a previously unrecognized encephalocele. She subsequently underwent left temporal lobe polectomy with encephalocele disconnection. The procedure was successful, and she has remained seizure-free for 1 year postoperatively, allowing initiation of ASM weaning and the possibility of long-term cure.

Conclusion: This case illustrates the transformative potential of epilepsy surgery for individuals with DRE and the promising role of artificial intelligence augmented triage systems in reducing delays to surgical referral. Given the morbidity, mortality, and economic burden associated with DRE, timely surgical evaluation should be considered the standard of care for eligible patients. Integrating artificial intelligence tools into clinical workflows may help overcome longstanding barriers to access and ensure that life-changing interventions are offered earlier in the disease course.

Abstract Image

人工智能辅助下的耐药癫痫手术一例左颞叶脑膨出。
引言:尽管有强有力的证据支持癫痫手术对适当选择的耐药癫痫(DRE)患者的疗效,但癫痫手术仍未得到充分利用。病例介绍:我们描述了一名23岁女性的病例,她有4年的难治性局灶性意识受损癫痫发作史,尽管多次试验的抗癫痫药物耐受剂量,但每周仍有4次发作。最初的调查包括磁共振成像(MRI)、脑电图(EEG)和副肿瘤筛查均无显著差异,她被诊断为非病变性左颞叶癫痫。作为人工智能驱动的研究计划的一部分,基于自然语言处理的随机森林算法审查神经病学临床文件,将她确定为潜在的外科候选人。补充的大型语言模型分析支持提取相关的癫痫发作和成像信息。这促使对该病例进行重新评估,PET显示左颞叶代谢低下,重复MRI显示先前未识别的脑膨出。随后行左颞叶切除伴脑膨出断开。手术很成功,术后1年无癫痫发作,可以开始ASM脱机,并有可能长期治愈。结论:该病例说明了癫痫手术对DRE患者的变革潜力,以及人工智能增强的分诊系统在减少手术转诊延误方面的重要作用。考虑到与DRE相关的发病率、死亡率和经济负担,及时的手术评估应被视为符合条件的患者的标准护理。将人工智能工具纳入临床工作流程可能有助于克服长期存在的获取障碍,并确保在病程早期提供改变生活的干预措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Case Reports in Neurology
Case Reports in Neurology Medicine-Neurology (clinical)
CiteScore
1.50
自引率
0.00%
发文量
67
审稿时长
14 weeks
期刊介绍: This new peer-reviewed online-only journal publishes original case reports covering the entire spectrum of neurology. Clinicians and researchers are given a tool to disseminate their personal experience to a wider public as well as to review interesting cases encountered by colleagues all over the world. To complement the contributions supplementary material is welcomed. The reports are searchable according to the key words supplied by the authors; it will thus be possible to search across the entire growing collection of case reports with universally used terms, further facilitating the retrieval of specific information. Following the open access principle, the entire contents can be retrieved at no charge, guaranteeing easy access to this valuable source of anecdotal information at all times.
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