NeurAssist- An innovative AI-based neuro-imaging platform that assists clinicians with unbiased AI predictions and analytical tools.
NeurAssist - 一个基于人工智能的创新神经影像平台,通过公正的人工智能预测和分析工具帮助临床医生。
基本信息
- 批准号:10043592
- 负责人:
- 金额:$ 6.34万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Grant for R&D
- 财政年份:2022
- 资助国家:英国
- 起止时间:2022 至 无数据
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
In 2021, more than one fifth of the EU population was aged 65 and over, and the proportion of people aged 80 or over is projected to be more than double by the year 2100 (Eurostat). This naturally increases the prevalence of age-related neurological disorders such as dementia and Alzheimer's disease. For instance, someone in the world develops dementia every 3 seconds. There are over 55 million people worldwide living with dementia in 2020\. This number will almost double every 20 years, reaching 78 million in 2030 and 139 million in 2050 (WHO).According to a study conducted by the Johns Hopkins University School of Medicine and appearing in Diagnosis, data confirm that an inaccurate diagnosis is the No. 1 cause of serious medical errors. The risk of misdiagnosis lead to excessive medical costs for the patients. For example, Vascular dementia patients who were previously misdiagnosed had, on average, 40%--143% more inpatient days, 45%--79% more ER visits, 19%--48% more outpatient/physician visits, 61%--221% more skilled nursing facility visits, 13%--56% more home health care days, and up to 44% more claims for durable medical equipment per year, compared with patients correctly diagnosed. (2015, Craig A. Hunter, et)The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. (2021 Elsevier B.V.) However, we identified opportunities to improve the current technologies. The project NeurAssist is an AI-based innovation that aids neurology clinicians with predictive diagnostics and visual analytics for the identification of pathological brain biomarkers, prediction of disease and support data interpretation using state-of-the-art approaches in machine learning.NeurAssist will strengthens the accuracy of interpretation of patient's neurological conditions, and it improves the predictive accuracy for the doctors and medical professionals to make correct diagnosis more often. In addition, it enhances transparency, unbiased and interpretability via explainable AI algorithms for visualisation, which helps the doctor to understand why the patient has a particular condition. It can reduce medical costs by reducing the rate of misdiagnoses and getting the faster results to the patients- the improved diagnosis results, early diagnosis to neurological conditions.
到2021年,超过五分之一的欧盟人口年龄在65岁及以上,预计到2100年,80岁及以上人口的比例将增加一倍以上(欧统局)。这自然会增加与年龄有关的神经系统疾病的患病率,如痴呆症和阿尔茨海默病。例如,世界上每3秒钟就有一个人患上痴呆症。到2020年,全球有超过5500万人患有痴呆症。这一数字几乎每20年翻一番,2030年达到7800万,2050年达到1. 39亿(世卫组织)。根据约翰霍普金斯大学医学院进行的一项研究,并出现在诊断中,数据证实,不准确的诊断是严重医疗错误的首要原因。误诊的风险导致患者的医疗费用过高。例如,以前被误诊的血管性痴呆患者平均每年住院天数增加40%-143%,急诊室就诊次数增加45%-79%,门诊/医生就诊次数增加19%-48%,专业护理机构就诊次数增加61%-221%,家庭保健天数增加13%-56%,耐用医疗设备索赔增加44%,与正确诊断的患者相比。(2015,克雷格A.机器学习的兴起开启了分析结构神经成像数据的新方法,包括大脑年龄预测。(2021 Elsevier B.V.)然而,我们发现了改进现有技术的机会。NeurAssist项目是一项基于人工智能的创新,帮助神经科临床医生进行预测诊断和可视化分析,以识别病理性大脑生物标志物,预测疾病,并使用最先进的机器学习方法支持数据解释。NeurAssist将加强患者神经系统状况解释的准确性,并且它提高了医生和医疗专业人员的预测准确性,以更经常地做出正确的诊断。此外,它还通过可解释的AI算法进行可视化,提高了透明度,无偏见和可解释性,这有助于医生了解患者为什么会有特定的病情。它可以通过降低误诊率和更快地为患者提供结果来降低医疗成本-改善诊断结果,早期诊断神经系统疾病。
项目成果
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其他文献
吉治仁志 他: "トランスジェニックマウスによるTIMP-1の線維化促進機序"最新医学. 55. 1781-1787 (2000)
Hitoshi Yoshiji 等:“转基因小鼠中 TIMP-1 的促纤维化机制”现代医学 55. 1781-1787 (2000)。
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LiDAR Implementations for Autonomous Vehicle Applications
- DOI:
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2021 - 期刊:
- 影响因子:0
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吉治仁志 他: "イラスト医学&サイエンスシリーズ血管の分子医学"羊土社(渋谷正史編). 125 (2000)
Hitoshi Yoshiji 等人:“血管医学与科学系列分子医学图解”Yodosha(涉谷正志编辑)125(2000)。
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Effect of manidipine hydrochloride,a calcium antagonist,on isoproterenol-induced left ventricular hypertrophy: "Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,K.,Teragaki,M.,Iwao,H.and Yoshikawa,J." Jpn Circ J. 62(1). 47-52 (1998)
钙拮抗剂盐酸马尼地平对异丙肾上腺素引起的左心室肥厚的影响:“Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,
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{{ truncateString('', 18)}}的其他基金
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$ 6.34万 - 项目类别:
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2908693 - 财政年份:2027
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2879438 - 财政年份:2027
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$ 6.34万 - 项目类别:
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2879865 - 财政年份:2027
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$ 6.34万 - 项目类别:
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$ 6.34万 - 项目类别:
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2876993 - 财政年份:2027
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$ 6.34万 - 项目类别:
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