中国卒中杂志 ›› 2020, Vol. 15 ›› Issue (03): 283-289.DOI: 10.3969/j.issn.1673-5765.2020.03.010
李子孝,刘涛,丁玲玲,刘子阳,李鑫鑫,王拥军
收稿日期:
2020-01-10
出版日期:
2020-03-20
发布日期:
2020-03-20
通讯作者:
王拥军 yongjunwang111@aliyun.com
基金资助:
科技部“十三五”重点研发计划(2017YFC1310901,2016YFC0901002,2017YFC1307905,2015BAI12B00)
中国医学科学院脑血管病人工智能研究创新单元(2019RU018)
北京市科学技术委员会基于人工智能的脑血管病临床诊疗决策研究(Z201100005620010)
北京市百千万人才项目(2018A13)
北京市青年拔尖人才项目(2018000021223ZK03)
Received:
2020-01-10
Online:
2020-03-20
Published:
2020-03-20
李子孝,刘涛,丁玲玲,刘子阳,李鑫鑫,王拥军. 机器学习在脑血管病诊疗应用中的研究进展[J]. 中国卒中杂志, 2020, 15(03): 283-289.
LI Zi-Xiao, LIU Tao, DING Ling-Ling, LIU Zi-Yang, LI Xin-Xin, WANG Yong-Jun. Machine Learning in Stroke Care[J]. Chinese Journal of Stroke, 2020, 15(03): 283-289.
[1] WU S,WU B,LIU M,et al. Stroke in China:advances and challenges in epidemiology,prevention,and management[J]. Lancet Neurol,2019,18(4):394-405.[2] ZHOU M,WANG H,ZENG X,et al. Mortality,morbidity,and risk factors in China and its provinces,1990-2017:a systematic analysis for the GlobalBurden of Disease Study 2017[J]. Lancet,2019,394(10204):1145-1158.[3] POWERS W J,RABINSTEIN A A,ACKERSONT,et al. Guidelines for the early management ofpatients with acute ischemic stroke:2019 updateto the 2018 guidelines for the early management ofacute ischemic stroke:a guideline for healthcareprofessionals from the American Heart Association/American Stroke Association[J/OL]. Stroke,2019,50(12):e344-e418[2020-01-10]. https://doi.org/10.1161/STR.0000000000000211.[4] LI Z,SINGHAL A B,WANG Y. Stroke physiciantraining in China[J/OL]. Stroke,2017,48(12):e338-e340[2020-01-10]. https://doi.org/10.1161/STROKEAHA.117.019462.[5] 乔红艳,郭邦俊,张龙江. 机器学习在心血管影像中的研究进展[J]. 中华医学杂志,2019,99(17):1353-1357.[6] 卢光明,许强,张志强. 神经系统疾病人工智能医学影像现状与发展[J]. 中华放射学杂志,2018,52(10):734-737.[7] SONG T. Generative model-based ischemic strokelesion segmentation[DB/OL]. Electrical Engineeringand Systems Science,2019[2020-01-10]. https://arxiv.org/abs/1906.02392.[8] WU O,WINZECK S,GIESE A-K,et al. Big dataapproaches to phenotyping acute ischemic strokeusing automated lesion segmentation of multi-centermagnetic resonance imaging data[J]. Stroke,2019,50(7):1734-1741.[9] WOO I,LEE A,JUNG S C,et al. Fully automaticsegmentation of acute ischemic lesions ondiffusion-weighted imaging using convolutionalneural networks:comparison with conventionalalgorithms[J]. Korean J Radiol,2019,20(8):1275-1284.[10] KIM Y C,LEE J E,YU I,et al. Evaluation ofdiffusion lesion volume measurements in acuteischemic stroke using encoder-decoder convolutionalnetwork[J]. Stroke,2019,50(6):1444-1451. [11] WINZECK S,MOCKING S J T,BEZERRA R,et al. Ensemble of convolutional neural networksimproves automated segmentation of acute ischemiclesions using multiparametric diffusion-weightedMRI[J]. AJNR Am J Neuroradiol,2019,40(6):938-945.[12] BOLDSEN J K,ENGEDAL T S,PEDRAZA S,etal. Better diffusion segmentation in acute ischemicstroke through automatic tree learning anomalysegmentation[J]. Front Neuroinform,2018,12:21.[13] ZHANG R,ZHAO L,LOU W,et al. Automaticsegmentation of acute ischemic stroke from DWIusing 3-D fully convolutional DenseNets[J]. IEEETrans Med Imaging,2018,37(9):2149-2160.[14] CHEN L,BENTLEY P,RUECKERT D. Fullyautomatic acute ischemic lesion segmentationin DWI using convolutional neural networks[J].Neuroimage Clin,2017,15:633-643.[15] LIEW S L,ANGLIN J M,BANKS N W,et al. Alarge,open source dataset of stroke anatomical brainimages and manual lesion segmentations[J]. Sci Data,2018,5:180011.[16] QI K,YANG H,LI C,et al. X-net:Brain strokelesion segmentation based on depthwise separableconvolution and long-range dependencies.International Conference on Medical ImageComputing and Computer-Assisted Intervention(MICCAI)2019[C/OL]. Springer,2019:247-255[2020-01-10]. https://doi.org/ 10.1007/978-3-030-32248-9_28.[17] ZHOU Y J,HUANG W J,DONG P,et al. D-UNet:a dimension-fusion U shape network for chronicstroke lesion segmentation[DB/OL]. ElectricalEngineering and Systems Science,2019[2020-01-10].https://arxiv.org/abs/1908.05104.[18] YANG H,HUANG W,QI K,et al. CLCINet:Cross-Level Fusion and context inferencenetworks for lesion segmentation of chronicstroke. International conference on Medical ImageComputing and Computer-Assisted Intervention(MICCAI)2019[DB/OL]. Springer,2019:266-274[2020-01-10]. https://arxiv.org/abs/1907.07008.[19] XUE Y,XIE M,FARHAT F G,et al. A fully3D multi-path convolutional neural network withfeature fusion and feature weighting for automaticlesion identification in brain MRI images[DB/OL]. Electrical Engineering and Systems Science,2019[2020-01-10]. https://arxiv.org/abs/1907.07807.[20] DHAR R,FALCONE G J,CHEN Y,et al. Deeplearning for automated measurement of hemorrhageand perihematomal edema in supratentorialintracerebral hemorrhage[J]. Stroke,2020,51(2):648-651.[21] DEBETTE S,MARKUS H S. The clinicalimportance of white matter hyperintensities on brainmagnetic resonance imaging:systematic review andmeta-analysis[J/OL]. BMJ,2010,341:c3666[2020-01-10]. https://doi.org/10.1136/bmj.c3666.[22] LAWRENCE A J,ZEESTRATEN E A,BENJAMINP,et al. Longitudinal decline in structuralnetworks predicts dementia in cerebral smallvessel disease[J/OL]. Neurology,2018,90(21):e1898-e1910[2020-01-10]. https://doi.org/10.1212/WNL.0000000000005551.[23] JIANG J,LIU T,ZHU W,et al. UBO Detector - Acluster-based,fully automated pipeline for extractingwhite matter hyperintensities[J]. Neuroimage,2018,174:539-549.[24] LI H,JIANG G,ZHANG J,et al. Fullyconvolutional network ensembles for white matterhyperintensities segmentation in MR images[J].Neuroimage,2018,183:650-665.[25] RONNEBERGER O,FISCHER P,BROX T. U-net:Convolutional networks for biomedical imagesegmentation[C/OL]//International Conference onMedical Image Computing and Computer-assistedIntervention(MICCAI)2015. Springer,2015:234-241[2020-01-10]. https://doi.org/10.1007/978-3-319-24574-4_28.[26] WANG Y,CATINDIG J A,HILAL S,et al. Multistagesegmentation of white matter hyperintensity,cortical and lacunar infarcts[J]. Neuroimage,2012,60(4):2379-2388.[27] HALLER S,VERNOOIJ M W,KUIJER J P A,etal. Cerebral microbleeds:imaging and clinicalsignificance[J]. Radiology,2018,287(1):11-28.[28] ZHANG Y D,ZHANG Y,HOU X X,et al.Seven-layer deep neural network based on sparseautoencoder for voxelwise detection of cerebralmicrobleed[J]. Multimed Tools Appl,2018,77(9):10521-10538.[29] BRIDLE J S. Probabilistic interpretation offeedforward classification network outputs,withrelationships to statistical pattern recognition[J].Neurocomputing,1990,68:227-236.[30] QI DOU,HAO CHEN,LEQUAN YU,et al.Automatic detection of cerebral microbleeds fromMR Images via 3D convolutional neural networks[J].IEEE Trans Med Imaging,2016,35(5):1182-1195.[31] ZHU Y C,TZOURIO C,SOUMARE A,etal. Severity of dilated Virchow-Robin spaces isassociated with age,blood pressure,and MRI markers of small vessel disease:a population-basedstudy[J]. Stroke,2010,41(11):2483-2490.[32] GONZALEZ-CASTRO V,VALDES HERNANDEZM D C,CHAPPELL F M,et al. Reliability of anautomatic classifier for brain enlarged perivascularspaces burden and comparison with humanperformance[J]. Clin Sci(Lond),2017,131(13):1465-1481.[33] DUBOST F,YILMAZ P,ADAMS H,et al. Enlargedperivascular spaces in brain MRI:automatedquantification in four regions[J]. Neuroimage,2019,185:534-544.[34] CHILAMKURTHY S,GHOSH R,TANAMALAS,et al. Deep learning algorithms for detection ofcritical findings in head CT scans:a retrospectivestudy[J]. Lancet,2018,392(10162):2388-2396.[35] TITANO J J,BADGELEY M,SCHEFFLEIN J,etal. Automated deep-neural-network surveillance ofcranial images for acute neurologic events[J]. NatMed,2018,24(9):1337-1341.[36] LEE H,YUNE S,MANSOURI M,et al. Anexplainable deep-learning algorithm for the detectionof acute intracranial haemorrhage from smalldatasets[J]. Nat Biomed Eng,2019,3(3):173-182.[37] LIU J,XU H,CHEN Q,et al. Prediction ofhematoma expansion in spontaneous intracerebralhemorrhage using support vector machine[J].EBioMedicine,2019,43:454-459.[38] WANG H L,HSU W Y,LEE M H,et al. Automaticmachine-learning-based outcome prediction inpatients with primary intracerebral hemorrhage[J].Front Neurol,2019,10:910.[39] HEO J,YOON J G,PARK H,et al. Machinelearning-based model for prediction of outcomes inacute stroke[J]. Stroke,2019,50(5):1263-1265.[40] REHME A K,VOLZ L J,FEIS D L,et al.Identifying neuroimaging markers of motor disabilityin acute stroke by machine learning techniques[J].Cereb Cortex,2015,25(9):3046-3056.[41] SIEGEL J S,RAMSEY L E,SNYDER A Z,etal. Disruptions of network connectivity predictimpairment in multiple behavioral domains afterstroke[J/OL]. Proc Natl Acad Sci USA,2016,113(30):e4367-e4376[2020-01-10]. https://doi.org/10.1073/pnas.1521083113.[42] THOMALLA G,CHENG B,EBINGER M,etal. DWI-FLAIR mismatch for the identification ofpatients with acute ischaemic stroke within 4. 5 hof symptom onset(PRE-FLAIR):a multicentreobservational study[J]. Lancet Neurol,2011,10(11):978-986.[43] LEE H,LEE E-J,HAM S,et al. Machine Learningapproach to identify stroke within 4.5 hours[J/OL].Stroke,2020[2020-01-10]. https://doi.org/10.1161/STROKEAHA.119.027611.[44] CHUNG J W,KIM Y C,CHA J,et al.Characterization of clot composition in acute cerebralinfarct using machine learning techniques[J]. AnnClin Transl Neurol,2019,6(4):739-747.[45] BENTLEY P,GANESALINGAM J,CARLTONJONES A L,et al. Prediction of stroke thrombolysisoutcome using CT brain machine learning[J].Neuroimage Clin,2014,4:635-640.[46] QIU W,KUANG H,NAIR J,et al. Radiomicsbasedintracranial thrombus features on CT and CTApredict recanalization with intravenous alteplase inpatients with acute ischemic stroke[J]. AJNR Am JNeuroradiol,2019,40(1):39-44.[47] BACCHI S,ZERNER T,OAKDEN-RAYNER L,et al. Deep learning in the prediction of ischaemicstroke thrombolysis functional outcomes:apilot study[J/OL]. Acad Radiol,2020,27(2):e19-e23[2020-01-10]. https://doi.org/10.1016/j.acra.2019.03.015.[48] RODRIGUES G M,BARREIRA C,FROEHLERM,et al. Multicenter ALADIN:Automated LargeArtery Occlusion Detection in Stroke ImagingUsing Artificial Intelligence[J/OL]. Stroke,2019,50(Suppl_1):AWP71[2020-01-10]. https://www.ahajournals.org/doi/10.1161/str.50.suppl_1.WP71.[49] AMUKOTUWA S A,STRAKA M,SMITH H,etal. Automated detection of intracranial large vesselocclusions on computed tomography angiography:asingle center experience[J]. Stroke,2019,50(10):2790-2798.[50] ALBERS G W,WALD M J,MLYNASH M,et al.Automated calculation of Alberta Stroke Programearly CT score:validation in patients with largehemispheric infarct[J]. Stroke,2019,50(11):3277-3279. |
[1] | 吴春艳, 尹雅诗, 王广志, 岳奎涛. 急性缺血性卒中不同时间窗影像学评价及应用进展[J]. 中国卒中杂志, 2024, 19(9): 1094-1101. |
[2] | 梁艳超, 王晓岩, 单凯. 基于DMAIC模型的妊娠合并脑血管病急诊就诊流程优化研究 [J]. 中国卒中杂志, 2024, 19(8): 873-879. |
[3] | 中国卒中学会医疗质量管理与促进分会, 《中英文标准化动脉粥样硬化性脑血管病术语中国专家共识》编写组. 中英文标准化动脉粥样硬化性脑血管病术语中国专家共识 [J]. 中国卒中杂志, 2024, 19(8): 973-977. |
[4] | 吴春艳, 朱新颖, 于文轩, 高云彬, 季丽丽, 战同霞, 谢海. 立德树人视域下脑血管病情景模拟案例教学效果评价问卷编制及信效度检验 [J]. 中国卒中杂志, 2024, 19(8): 978-982. |
[5] | 李丽君, 张宁, 陈琦, 王春雪. 主观性失眠与脑血管病慢性期功能预后的关系研究:基于多中心前瞻性研究的事后分析 [J]. 中国卒中杂志, 2024, 19(7): 815-821. |
[6] | 张啟铿, 林丽, 谢臻彦, 李雪松. 外泌体减轻出血性脑血管病后继发性损伤的研究进展 [J]. 中国卒中杂志, 2024, 19(7): 840-847. |
[7] | 冯致远, 李子孝, 王春娟. 数字健康在脑血管病领域的应用及未来发展趋势[J]. 中国卒中杂志, 2024, 19(6): 607-612. |
[8] | 孟令涉, 王春娟. 人工智能与机器学习在心脑血管疾病管理中的应用与前景:美国心脏学会使用人工智能改善心脏疾病结局科学声明解读[J]. 中国卒中杂志, 2024, 19(6): 621-631. |
[9] | 尚媛媛, 杜正静, 陈静怡, 彭波, 龙杰琦. 心脑血管疾病与气象因素关系预测模型的建立与评估[J]. 中国卒中杂志, 2024, 19(6): 632-639. |
[10] | 温家琦, 庞江霞, 陈超, 姜长春, 郝喜娃. 牛磺酸在代谢性脑血管病中的潜在作用及其机制研究进展[J]. 中国卒中杂志, 2024, 19(6): 706-713. |
[11] | 谷鸿秋. 临床预测模型的困境与机遇[J]. 中国卒中杂志, 2024, 19(5): 481-487. |
[12] | 金奥铭, 谷鸿秋. 临床预测模型的展现形式[J]. 中国卒中杂志, 2024, 19(5): 515-519. |
[13] | 李世雨, 张星, 胡文立. 脂蛋白(a)与颈动脉粥样硬化不稳定斑块的关系[J]. 中国卒中杂志, 2024, 19(5): 539-544. |
[14] | 许杰, 王拥军. 代谢性脑血管病的“多系统对话”与“多学科共管”[J]. 中国卒中杂志, 2024, 19(2): 125-129. |
[15] | 张方圆, 薛婧, 许杰, 王拥军. 干预代谢危险因素对脑血管病影响的研究进展[J]. 中国卒中杂志, 2024, 19(2): 131-137. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||