Automated Idiopathic Normal-Pressure Hydrocephalus Diagnosis via Artificial Intelligence-Based 3D T1 MRI Volumetric Analysis.

IF 3.1 3区 医学 Q2 CLINICAL NEUROLOGY American Journal of Neuroradiology Pub Date : 2024-09-09 DOI:10.3174/ajnr.a8489
Joonhyung Lee,Dana Kim,Chong Hyun Suh,Suyoung Yun,Kyu Sung Choi,Seungjun Lee,Wooseok Jung,Jinyoung Kim,Hwon Heo,Woo Hyun Shim,Sungyang Jo,Sun Ju Chung,Jae-Sung Lim,Ho Sung Kim,Sang Joon Kim,Jae-Hong Lee
{"title":"Automated Idiopathic Normal-Pressure Hydrocephalus Diagnosis via Artificial Intelligence-Based 3D T1 MRI Volumetric Analysis.","authors":"Joonhyung Lee,Dana Kim,Chong Hyun Suh,Suyoung Yun,Kyu Sung Choi,Seungjun Lee,Wooseok Jung,Jinyoung Kim,Hwon Heo,Woo Hyun Shim,Sungyang Jo,Sun Ju Chung,Jae-Sung Lim,Ho Sung Kim,Sang Joon Kim,Jae-Hong Lee","doi":"10.3174/ajnr.a8489","DOIUrl":null,"url":null,"abstract":"BACKGROUND AND PURPOSE\r\nIdiopathic normal pressure hydrocephalus (iNPH) is reversible dementia, that is underdiagnosed. The purpose of this study was to develop an automated diagnostic method for iNPH using artificial intelligence techniques with a T1-weighted MRI scan.\r\n\r\nMATERIALS AND METHODS\r\nWe quantified iNPH, Parkinson's disease, Alzheimer's disease, and healthy control patients on T1-weighted 3D brain MRI scans using 452 scans for training and 110 scans for testing. Automatic component measurement algorithms were developed for Evans' index, Sylvian fissure enlargement, high-convexity tightness, callosal angle, and normalized lateral ventricle volume. XGBoost models were trained for both automated measurements and manual labels for iNPH prediction.\r\n\r\nRESULTS\r\nA total of 452 patients (200 men; mean age ± standard deviation, 73.2 ± 6.5 years) were included in the training set. Of the 452 patients, 111 (24.6%) had iNPH. We obtained AUC values of 0.956 for automatically measured high-convexity tightness and 0.830 for Sylvian fissure enlargement. Intra-class correlation values of 0.824 for the callosal angle and 0.924 for Evans' index were measured. Using the decision tree of the XGBoost model, the model trained on manual labels obtained an average cross-validation AUC of 0.988 on the training set and 0.938 on the unseen test set, while the fully automated model obtained a cross-validation AUC of 0.983 and an unseen test AUC of 0.936.\r\n\r\nCONCLUSION\r\nWe demonstrated a machine-learning algorithm capable of diagnosing iNPH from a 3D T1-weighted MRI scan that is robust to the failure. We propose a method to scan large numbers of 3D T1-weighted MRI scans with minimal human intervention, making possible large-scale iNPH screening.\r\n\r\nABBREVIATIONS\r\niNPH = idiopathic normal-pressure hydrocephalus; PD = Parkinson's disease; AD = Alzheimer's disease; HC = healthy control; CSF = cerebrospinal fluid; DESH = disproportionately enlarged subarachnoid space hydrocephalus; 3D = three-dimensional.","PeriodicalId":7875,"journal":{"name":"American Journal of Neuroradiology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Neuroradiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3174/ajnr.a8489","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

BACKGROUND AND PURPOSE Idiopathic normal pressure hydrocephalus (iNPH) is reversible dementia, that is underdiagnosed. The purpose of this study was to develop an automated diagnostic method for iNPH using artificial intelligence techniques with a T1-weighted MRI scan. MATERIALS AND METHODS We quantified iNPH, Parkinson's disease, Alzheimer's disease, and healthy control patients on T1-weighted 3D brain MRI scans using 452 scans for training and 110 scans for testing. Automatic component measurement algorithms were developed for Evans' index, Sylvian fissure enlargement, high-convexity tightness, callosal angle, and normalized lateral ventricle volume. XGBoost models were trained for both automated measurements and manual labels for iNPH prediction. RESULTS A total of 452 patients (200 men; mean age ± standard deviation, 73.2 ± 6.5 years) were included in the training set. Of the 452 patients, 111 (24.6%) had iNPH. We obtained AUC values of 0.956 for automatically measured high-convexity tightness and 0.830 for Sylvian fissure enlargement. Intra-class correlation values of 0.824 for the callosal angle and 0.924 for Evans' index were measured. Using the decision tree of the XGBoost model, the model trained on manual labels obtained an average cross-validation AUC of 0.988 on the training set and 0.938 on the unseen test set, while the fully automated model obtained a cross-validation AUC of 0.983 and an unseen test AUC of 0.936. CONCLUSION We demonstrated a machine-learning algorithm capable of diagnosing iNPH from a 3D T1-weighted MRI scan that is robust to the failure. We propose a method to scan large numbers of 3D T1-weighted MRI scans with minimal human intervention, making possible large-scale iNPH screening. ABBREVIATIONS iNPH = idiopathic normal-pressure hydrocephalus; PD = Parkinson's disease; AD = Alzheimer's disease; HC = healthy control; CSF = cerebrospinal fluid; DESH = disproportionately enlarged subarachnoid space hydrocephalus; 3D = three-dimensional.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于人工智能的三维 T1 MRI 容积分析自动诊断特发性正压脑积水
背景和目的特发性正常压力脑积水(iNPH)是一种可逆性痴呆症,诊断率低。本研究的目的是利用人工智能技术和 T1 加权核磁共振成像扫描,开发出一种 iNPH 的自动诊断方法。针对埃文斯指数、Sylvian 裂隙扩大、高凸紧缩度、胼胝体角和归一化侧脑室容积开发了自动成分测量算法。结果共有 452 名患者(200 名男性;平均年龄为 73.2±6.5 岁)被纳入训练集。在 452 名患者中,111 人(24.6%)患有 iNPH。我们获得的自动测量高凸紧度的 AUC 值为 0.956,Sylvian 裂隙增大的 AUC 值为 0.830。胼胝体角的类内相关值为 0.824,埃文斯指数的类内相关值为 0.924。使用 XGBoost 模型的决策树,根据人工标签训练的模型在训练集上的平均交叉验证 AUC 为 0.988,在未见测试集上的平均交叉验证 AUC 为 0.938,而全自动模型的交叉验证 AUC 为 0.983,未见测试 AUC 为 0.936。我们提出了一种只需极少人工干预即可扫描大量三维 T1 加权 MRI 扫描图像的方法,从而实现了大规模 iNPH 筛查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
7.10
自引率
5.70%
发文量
506
审稿时长
2 months
期刊介绍: The mission of AJNR is to further knowledge in all aspects of neuroimaging, head and neck imaging, and spine imaging for neuroradiologists, radiologists, trainees, scientists, and associated professionals through print and/or electronic publication of quality peer-reviewed articles that lead to the highest standards in patient care, research, and education and to promote discussion of these and other issues through its electronic activities.
期刊最新文献
Artificial Intelligence-Generated Editorials in Radiology: Can Expert Editors Detect Them? The many faces of myxopapillary ependynomas. Vestibular schwannoma-related increased labyrinthine post-gadolinium 3D-FLAIR signal intensity and association with hearing impairment. CNS Embryonal Tumor with PLAGL Amplification, a New Tumor Type in Children and Adolescents: Insights from a Comprehensive MRI Analysis. DSA Quantitative Analysis and Predictive Modeling of Obliteration in Cerebral AVM following Stereotactic Radiosurgery.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1