AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus

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dc.contributor.authorZhou, Xi
dc.contributor.authorYe, Qinghao
dc.contributor.authorYang, Xiaolin
dc.contributor.authorChen, Jiakun
dc.contributor.authorMa, Haiqin
dc.contributor.authorXia, Jun
dc.contributor.authorDel Ser, Javier
dc.contributor.authorYang, Guang
dc.contributor.institutionIA
dc.date.issued2022-02-24
dc.descriptionThis study is supported in part by Project of Shenzhen International Cooperation Foundation (GJHZ20180926165402083),_x000D_ in part by the National Natural Science Foundation of China (grant number 82171913), _x000D_ in part by the funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research_x000D_ Group MATHMODE (IT1294-19),_x000D_ in part by the British Heart Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), _x000D_ in part by the Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology),_x000D_ in part by the European Research Council Innovative Medicines Initiative on Development of Therapeutics and Diagnostics Combat ting Coronavirus Infections Award ‘‘DRAGON: rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavirus paNdemics’’ [H2020-JTI-IMI2 101005122],_x000D_ in part by the AI for Health Imaging Award ‘‘CHAIMELEON: Accelerating the Lab to Market Transition of AI Tools for Cancer Management’’ [H2020-SC1-FA-DTS-2019-1 952172],_x000D_ in part by the MRC (MC/PC/21013), _x000D_ and in part by the UK Research and Innovation Future Leaders Fellowship [MR/V023799/1]
dc.description.abstractBased on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use the machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2 ± 2.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0 ± 3.8, respectively. The whole process takes 3.4 ± 0.3 s. In MRI images, the DSC, ICC, Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0 ± 0.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9 ± 3.8, respectively. The whole process took 1.9 ± 0.1 s. We have established a multimodal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient’s ventricles.en
dc.description.statusPeer reviewed
dc.format.extent10
dc.format.extent1255438
dc.identifier.citationZhou , X , Ye , Q , Yang , X , Chen , J , Ma , H , Xia , J , Del Ser , J & Yang , G 2022 , ' AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus ' , Neural Computing and Applications , vol. unknown , no. 22 , pp. 16011-16020 . https://doi.org/10.1007/s00521-022-07048-0
dc.identifier.doi10.1007/s00521-022-07048-0
dc.identifier.issn0941-0643
dc.identifier.otherresearchoutputwizard: 11556/1288
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85125136847&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofNeural Computing and Applications
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsNormal pressure hydrocephalus
dc.subject.keywordsMachine learning
dc.subject.keywordsComputed tomography
dc.subject.keywordsMagnetic resonance imaging
dc.subject.keywordsVentricular volume
dc.subject.keywordsIntracranial volume
dc.subject.keywordsMedical AI
dc.subject.keywordsNormal pressure hydrocephalus
dc.subject.keywordsMachine learning
dc.subject.keywordsComputed tomography
dc.subject.keywordsMagnetic resonance imaging
dc.subject.keywordsVentricular volume
dc.subject.keywordsIntracranial volume
dc.subject.keywordsMedical AI
dc.subject.keywordsAI-based diagnosis
dc.subject.keywordsSoftware
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsSDG 3 - Good Health and Well-being
dc.subject.keywordsProject ID
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/101005122/EU/rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavirus paNdemics/DRAGON
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/952172/EU/Accelerating the Lab to Market Transition of AI Tools for Cancer Management/CHAIMELEON
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/101005122/EU/rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavirus paNdemics/DRAGON
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/952172/EU/Accelerating the Lab to Market Transition of AI Tools for Cancer Management/CHAIMELEON
dc.subject.keywordsFunding Info
dc.subject.keywordsThis study is supported in part by Project of Shenzhen International Cooperation Foundation (GJHZ20180926165402083),_x000D_ in part by the National Natural Science Foundation of China (grant number 82171913), _x000D_ in part by the funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research_x000D_ Group MATHMODE (IT1294-19),_x000D_ in part by the British Heart Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), _x000D_ in part by the Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology),_x000D_ in part by the European Research Council Innovative Medicines Initiative on Development of Therapeutics and Diagnostics Combat ting Coronavirus Infections Award ‘‘DRAGON: rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavirus paNdemics’’ [H2020-JTI-IMI2 101005122],_x000D_ in part by the AI for Health Imaging Award ‘‘CHAIMELEON: Accelerating t
dc.subject.keywordsThis study is supported in part by Project of Shenzhen International Cooperation Foundation (GJHZ20180926165402083),_x000D_ in part by the National Natural Science Foundation of China (grant number 82171913), _x000D_ in part by the funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research_x000D_ Group MATHMODE (IT1294-19),_x000D_ in part by the British Heart Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), _x000D_ in part by the Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology),_x000D_ in part by the European Research Council Innovative Medicines Initiative on Development of Therapeutics and Diagnostics Combat ting Coronavirus Infections Award ‘‘DRAGON: rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavirus paNdemics’’ [H2020-JTI-IMI2 101005122],_x000D_ in part by the AI for Health Imaging Award ‘‘CHAIMELEON: Accelerating t
dc.titleAI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalusen
dc.typejournal article
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