Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence.
Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence.
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DOI：
10.1093/brain/awab439
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发表时间：
2022-04-18
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
中风是全球发病率和死亡率的主要原因之一。近年来，新的和不断改进的治疗方案，如溶栓和血栓切除术，已经彻底改变了急性卒中的治疗。按照现代节奏，下一场革命很可能是战略性地使用稳步增加的患者相关数据来生成模型，从而实现个性化的结果预测。随着大数据和人工智能进入日常生活，在几个医疗保健领域已经取得了重大进展。这篇综述的目的是概括性地说明和讨论人工智能方法如何帮助计算急性，亚急性和慢性阶段卒中结局研究中的单个患者预测。我们将介绍考虑人口统计学，临床和电生理数据，以及源自各种成像模式及其组合的数据的方法。我们将概述它们的优点，缺点，潜在的陷阱以及它们所持有的承诺，特别关注临床受众。在整个综述中，我们将重点介绍新型机器学习方法的方法学方面，因为它们对于实现精准医学尤其重要。最后，我们将展望人工智能方法如何有助于改善中风后的良好结果。Bonkhoff和Grefkes讨论了急性、亚急性和慢性阶段卒中结局研究中现有的人工智能方法和单一受试者预测方案。他们概述了日益丰富的数据如何能够带来新的科学见解，并最终有助于改善中风后的结果。
Stroke ranks among the leading causes for morbidity and mortality worldwide. New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience. Throughout the review we will highlight methodological aspects of novel machine-learning approaches as they are particularly crucial to realize precision medicine. We will finally provide an outlook on how artificial intelligence approaches might contribute to enhancing favourable outcomes after stroke. Bonkhoff and Grefkes discuss existing artificial intelligence approaches and single-subject prediction scenarios within stroke outcome research in the acute, subacute and chronic stages. They outline how an increasing data richness could allow new scientific insights and ultimately contribute to improved outcomes after stroke.
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影响因子：
4.8
作者：
Bonkhoff AK;Rehme AK;Hensel L;Tscherpel C;Volz LJ;Espinoza FA;Gazula H;Vergara VM;Fink GR;Calhoun VD;Rost NS;Grefkes C
通讯作者：
Grefkes C
急性缺血性中风后的结果与性别特异性病变模式有关。
影响因子：
16.6
作者：
Bonkhoff AK;Schirmer MD;Bretzner M;Hong S;Regenhardt RW;Brudfors M;Donahue KL;Nardin MJ;Dalca AV;Giese AK;Etherton MR;Hancock BL;Mocking SJT;McIntosh EC;Attia J;Benavente OR;Bevan S;Cole JW;Donatti A;Griessenauer CJ;Heitsch L;Holmegaard L;Jood K;Jimenez-Conde J;Kittner SJ;Lemmens R;Levi CR;McDonough CW;Meschia JF;Phuah CL;Rolfs A;Ropele S;Rosand J;Roquer J;Rundek T;Sacco RL;Schmidt R;Sharma P;Slowik A;Söderholm M;Sousa A;Stanne TM;Strbian D;Tatlisumak T;Thijs V;Vagal A;Wasselius J;Woo D;Zand R;McArdle PF;Worrall BB;Jern C;Lindgren AG;Maguire J;Bzdok D;Wu O;MRI-GENIE and GISCOME Investigators and the International Stroke Genetics Consortium;Rost NS
通讯作者：
Rost NS
影响因子：
8.3
作者：
Bowman H;Bonkhoff A;Hope T;Grefkes C;Price C
通讯作者：
Price C
影响因子：
4.3
作者：
Bretzner M;Bonkhoff AK;Schirmer MD;Hong S;Dalca AV;Donahue KL;Giese AK;Etherton MR;Rist PM;Nardin M;Marinescu R;Wang C;Regenhardt RW;Leclerc X;Lopes R;Benavente OR;Cole JW;Donatti A;Griessenauer CJ;Heitsch L;Holmegaard L;Jood K;Jimenez-Conde J;Kittner SJ;Lemmens R;Levi CR;McArdle PF;McDonough CW;Meschia JF;Phuah CL;Rolfs A;Ropele S;Rosand J;Roquer J;Rundek T;Sacco RL;Schmidt R;Sharma P;Slowik A;Sousa A;Stanne TM;Strbian D;Tatlisumak T;Thijs V;Vagal A;Wasselius J;Woo D;Wu O;Zand R;Worrall BB;Maguire JM;Lindgren A;Jern C;Golland P;Kuchcinski G;Rost NS
通讯作者：
Rost NS
影响因子：
6.7
作者：
Bernhardt, Julie;Hayward, Kathryn S.;Cramer, Steven C.
通讯作者：
Cramer, Steven C.