{"title":"在清醒手术过程中同步采集多模态数据的模块化系统:朝着建立专用临床数据库的方向迈进。","authors":"Ilias Maoudj, Charles Garraud, Celine Panheleux, Vanessa Saliou, Romuald Seizeur, Guillaume Dardenne","doi":"10.1109/EMBC40787.2023.10340545","DOIUrl":null,"url":null,"abstract":"<p><p>Awake Surgery (AS) is considered the best treatment for brain tumors located in or near eloquent areas. During this intervention, Direct Electrical Stimulations (DES) are delivered by the surgeon on the patient's brain in order to obtain an accurate brain mapping of the patient. The patient is asked to perform various tasks (e.g. counting, object naming, emotion recognition) through neuropsychological tests during these stimulations. These DES may cause a reversible lesion inducing deficits on the patient which can be observed during these tasks by the medical staff. The resection is then performed or not according to the patient's response. The intraoperative deficits can take several forms and can be difficult to analyze and identify. The development of new solutions allowing the automatic detection of these deficits could be therefore essential. However, still today, no structured and organized AS dedicated database is available that could be used to train and test these algorithms. We propose a modular system allowing the synchronized multimodal acquisition of various information including physiological measurements, DES signals and parameters, and task-related data to create such database.Clinical relevance- Acquiring synchronized multimodal data during AS will allow the creation of a dedicated database that could then be used to reveal new correlations between DES and the patient's response, and to develop and test new algorithms for the automatic detection of deficits.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2023 ","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A modular system for the synchronized multimodal data acquisition during Awake Surgery: towards the emergence of a dedicated clinical database.\",\"authors\":\"Ilias Maoudj, Charles Garraud, Celine Panheleux, Vanessa Saliou, Romuald Seizeur, Guillaume Dardenne\",\"doi\":\"10.1109/EMBC40787.2023.10340545\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Awake Surgery (AS) is considered the best treatment for brain tumors located in or near eloquent areas. During this intervention, Direct Electrical Stimulations (DES) are delivered by the surgeon on the patient's brain in order to obtain an accurate brain mapping of the patient. The patient is asked to perform various tasks (e.g. counting, object naming, emotion recognition) through neuropsychological tests during these stimulations. These DES may cause a reversible lesion inducing deficits on the patient which can be observed during these tasks by the medical staff. The resection is then performed or not according to the patient's response. The intraoperative deficits can take several forms and can be difficult to analyze and identify. The development of new solutions allowing the automatic detection of these deficits could be therefore essential. However, still today, no structured and organized AS dedicated database is available that could be used to train and test these algorithms. We propose a modular system allowing the synchronized multimodal acquisition of various information including physiological measurements, DES signals and parameters, and task-related data to create such database.Clinical relevance- Acquiring synchronized multimodal data during AS will allow the creation of a dedicated database that could then be used to reveal new correlations between DES and the patient's response, and to develop and test new algorithms for the automatic detection of deficits.</p>\",\"PeriodicalId\":72237,\"journal\":{\"name\":\"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. 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引用次数: 0
摘要
清醒手术(AS)被认为是治疗位于或靠近发音区的脑肿瘤的最佳方法。在这一干预过程中,外科医生会对患者的大脑进行直接电刺激(DES),以获得患者的准确脑图。在这些刺激过程中,患者需要通过神经心理学测试来完成各种任务(如计数、物体命名、情绪识别)。这些 DES 可能会造成可逆性病变,导致患者出现障碍,医务人员可以在这些任务中观察到这些病变。然后根据患者的反应决定是否进行切除。术中缺陷有多种形式,难以分析和识别。因此,开发可自动检测这些缺陷的新解决方案至关重要。然而,目前仍没有结构化、有组织的 AS 专用数据库可用于训练和测试这些算法。我们提出了一个模块化系统,允许同步多模态采集各种信息,包括生理测量、DES 信号和参数以及与任务相关的数据,以创建这样的数据库。
A modular system for the synchronized multimodal data acquisition during Awake Surgery: towards the emergence of a dedicated clinical database.
Awake Surgery (AS) is considered the best treatment for brain tumors located in or near eloquent areas. During this intervention, Direct Electrical Stimulations (DES) are delivered by the surgeon on the patient's brain in order to obtain an accurate brain mapping of the patient. The patient is asked to perform various tasks (e.g. counting, object naming, emotion recognition) through neuropsychological tests during these stimulations. These DES may cause a reversible lesion inducing deficits on the patient which can be observed during these tasks by the medical staff. The resection is then performed or not according to the patient's response. The intraoperative deficits can take several forms and can be difficult to analyze and identify. The development of new solutions allowing the automatic detection of these deficits could be therefore essential. However, still today, no structured and organized AS dedicated database is available that could be used to train and test these algorithms. We propose a modular system allowing the synchronized multimodal acquisition of various information including physiological measurements, DES signals and parameters, and task-related data to create such database.Clinical relevance- Acquiring synchronized multimodal data during AS will allow the creation of a dedicated database that could then be used to reveal new correlations between DES and the patient's response, and to develop and test new algorithms for the automatic detection of deficits.