NeuReader: Eye Tracking Enabled Explainable-AI for Empowering Resource Scarce Neurological Healthcare in Pakistan

神经系统疾病给巴基斯坦的卫生保健系统带来了沉重的负担，在这个国家，只有少数训练有素的神经科医生可以为2.31亿人口提供服务。巴基斯坦60%以上的人口居住在农村地区，那里的医疗保健是通过初级医生或护理人员经营的基本保健单位和农村保健中心提供的。这些设施没有人力或资源来提供任何神经护理。NeuReader项目的目标是开发一个系统，帮助改善向巴基斯坦患者提供的神经系统护理。神经系统健康将通过脑电图（EEG）来监测，脑电图通过放置在头皮上的电极来测量大脑的电活动。NeuReader将利用人工智能和自然语言处理来读取脑电图数据，诊断神经系统疾病，并提供解释问题的报告。没有解释的简单诊断决定并不是很有帮助，因为在bhu或rhc管理测试的初级医务人员也需要帮助，以提高患者对其健康状况的认识。更好地了解诊断还将使患者及其家属能够在知情的情况下决定前往城市地区的医院寻求进一步援助，因为这需要大量的旅行和住宿费用。NeuReader的可解释性功能也将帮助神经科医生（远程连接到系统）根据病情的严重程度对患者进行优先排序。建立这样一个系统需要克服几个技术上的挑战。人工智能系统的性能高度依赖于可用于训练它们的数据的数量和质量。训练将使用两种类型的数据：(1)带有医生报告的脑电图记录(2)脑电图记录中发现的异常位置。在数据收集过程中，医生报告将由神经科医生撰写。在脑电图记录中标记异常位置是费时费力的。脑电图记录可能包含几分钟/小时的数据，异常仅持续几秒钟，并且分布在记录中的不同位置。为了避免花费数百小时标记脑电图记录，眼动追踪将用于记录神经学家在日常工作中检查脑电图数据时在电脑屏幕上的凝视模式的位置。然后，这些眼睛注视模式将被用来迅速生成事件标签，节省神经科医生宝贵的时间。记录的标签将用于训练人工智能算法，该算法可以自动发现感兴趣的事件，然后可用于生成文本报告，供初级医生和bhu或rhc的护理人员使用，以帮助疑似患有神经系统疾病的患者。大量时间将用于实地研究，旨在评估将成为该系统最终用户的患者和医生的需求。这些实地研究的学习成果将纳入最终设计，以最大限度地提高对地面的影响。

Neurological disorders place a significant burden on the healthcare system of Pakistan where only a handful of trained neurologists are available to serve a population of 231 million people. More than 60% of Pakistan's population resides in rural areas where healthcare is provided via basic health units (BHU) and rural health centres (RHC) which are run by junior doctors or nursing staff. These facilities do not have the manpower or resources to provide any neurological care. The aim of project NeuReader is to develop a system that helps improve the provision of neurological care to patients in Pakistan. Neurological health will be monitored using electroencephalograms (EEG) which measure the brain's electrical activity via electrodes placed on the scalp. NeuReader will leverage Artificial Intelligence and Natural Language Processing to read EEG data, diagnose neurological disorders, and provide a report explaining the problem. Simple diagnostic decisions with no explanation are not very helpful as junior medical staff administering the tests at BHUs or RHCs would also need help to increase patient awareness about their health condition. A better understanding of the diagnosis will also allow patients and their families make informed decisions about travelling to hospitals in urban areas to seek further assistance since that entails substantial travel and lodging expenses. The explainability features of NeuReader will also help neurologists (connected remotely to the system) prioritise patients based on the gravity of their conditions. Building such a system requires overcoming several technical challenges. The performance of AI systems is highly dependent on the amount and quality of data available for training them. Two types of data will be used for training: (1) EEG recordings with doctor's reports summarising them in words (2) Locations of abnormalities spotted within an EEG recording. Doctor reports will be written by neurologists during data collection. Labelling of locations of abnormalities within EEG recordings is time consuming and laborious. An EEG recording may consist of several minutes/ hours of data with abnormalities lasting only a few seconds and spread out across different locations within the recording. To avoid investing hundreds of hours labelling EEG records, eye tracking will be used to record locations of neurologist gaze patterns on a computer screen as they examine EEG data in their routine practice. These eye gaze patterns will then be used to promptly generate labels of events saving hours of highly valuable neurologist time. The recorded labels will be used to train AI algorithms that can automatically spot events of interest which can then be used to generate a text report that can be used by junior doctors and nursing staff at BHUs or RHCs to assist patients suspected of suffering from neurological disorders. A significant time will be dedicated to field studies designed to assess the needs of patients and doctors who will be the end users of this systems. The learn outcomes of these field studies will be incorporated into the final design to maximise on ground impact.