Yi Yang, Zhenyao Chang, Xin Nie, Jun Wu, Jingang Chen, Weiqi Liu, Hongwei He, Shuo Wang, Chengcheng Zhu, Qingyuan Liu
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{"title":"Integrated Deep Learning Model for the Detection, Segmentation, and Morphologic Analysis of Intracranial Aneurysms Using CT Angiography.","authors":"Yi Yang, Zhenyao Chang, Xin Nie, Jun Wu, Jingang Chen, Weiqi Liu, Hongwei He, Shuo Wang, Chengcheng Zhu, Qingyuan Liu","doi":"10.1148/ryai.240017","DOIUrl":null,"url":null,"abstract":"<p><p><i>\"Just Accepted\" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To develop a deep learning model for the morphologic measurement of unruptured intracranial aneurysms (UIAs) based on CT angiography (CTA) data and validate its performance using a multicenter dataset. Materials and Methods In this retrospective study, patients with CTA examinations, including those with and without UIAs, in a tertiary referral hospital from February 2018 to February 2021 were included as the training dataset. Patients with UIAs who underwent CTA at multiple centers between April 2021 to December 2022 were included as the multicenter external testing set. An integrated deep-learning (IDL) model was developed for UIA detection, segmentation and morphologic measurement using an nnU-net algorithm. Model performance was evaluated using the Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC), with measurements by senior radiologists serving as the reference standard. The ability of the IDL model to improve performance of junior radiologists in measuring morphologic UIA features was assessed. Results The study included 1182 patients with UIAs and 578 controls without UIAs as the training dataset (55 years [IQR, 47-62], 1,012 [57.5%] females) and 535 patients with UIAs as the multicenter external testing set (57 years [IQR, 50-63], 353 [66.0%] females). The IDL model achieved 97% accuracy in detecting UIAs and achieved a DSC of 0.90 (95%CI, 0.88-0.92) for UIA segmentation. Model-based morphologic measurements showed good agreement with reference standard measurements (all ICCs > 0.85). Within the multicenter external testing set, the IDL model also showed agreement with reference standard measurements (all ICCs > 0.80). Junior radiologists assisted by the IDL model showed significantly improved performance in measuring UIA size (ICC improved from 0.88 [0.80-0.92] to 0.96 [0.92-0.97], <i>P</i> < .001). Conclusion The developed integrated deep learning model using CTA data showed good performance in UIA detection, segmentation and morphologic measurement and may be used to assist less experienced radiologists in morphologic analysis of UIAs. ©RSNA, 2024.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240017"},"PeriodicalIF":8.1000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiology-Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1148/ryai.240017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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Abstract
"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence . This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning model for the morphologic measurement of unruptured intracranial aneurysms (UIAs) based on CT angiography (CTA) data and validate its performance using a multicenter dataset. Materials and Methods In this retrospective study, patients with CTA examinations, including those with and without UIAs, in a tertiary referral hospital from February 2018 to February 2021 were included as the training dataset. Patients with UIAs who underwent CTA at multiple centers between April 2021 to December 2022 were included as the multicenter external testing set. An integrated deep-learning (IDL) model was developed for UIA detection, segmentation and morphologic measurement using an nnU-net algorithm. Model performance was evaluated using the Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC), with measurements by senior radiologists serving as the reference standard. The ability of the IDL model to improve performance of junior radiologists in measuring morphologic UIA features was assessed. Results The study included 1182 patients with UIAs and 578 controls without UIAs as the training dataset (55 years [IQR, 47-62], 1,012 [57.5%] females) and 535 patients with UIAs as the multicenter external testing set (57 years [IQR, 50-63], 353 [66.0%] females). The IDL model achieved 97% accuracy in detecting UIAs and achieved a DSC of 0.90 (95%CI, 0.88-0.92) for UIA segmentation. Model-based morphologic measurements showed good agreement with reference standard measurements (all ICCs > 0.85). Within the multicenter external testing set, the IDL model also showed agreement with reference standard measurements (all ICCs > 0.80). Junior radiologists assisted by the IDL model showed significantly improved performance in measuring UIA size (ICC improved from 0.88 [0.80-0.92] to 0.96 [0.92-0.97], P < .001). Conclusion The developed integrated deep learning model using CTA data showed good performance in UIA detection, segmentation and morphologic measurement and may be used to assist less experienced radiologists in morphologic analysis of UIAs. ©RSNA, 2024.
利用 CT 血管造影检测、分割和形态分析颅内动脉瘤的集成深度学习模型。
"刚刚接受 "的论文经过同行评审,已被接受在《放射学》上发表:人工智能》上发表。这篇文章在以最终版本发表之前,还将经过校对、排版和校样审核。请注意,在制作最终校对稿的过程中,可能会发现一些可能影响内容的错误。目的 基于 CT 血管造影(CTA)数据,开发一种用于未破裂颅内动脉瘤(UIAs)形态测量的深度学习模型,并使用多中心数据集验证其性能。材料与方法 在这项回顾性研究中,将 2018 年 2 月至 2021 年 2 月在一家三级转诊医院接受 CTA 检查的患者(包括有 UIA 和无 UIA 的患者)作为训练数据集。2021 年 4 月至 2022 年 12 月期间在多个中心接受 CTA 检查的 UIA 患者作为多中心外部测试集。利用 nnU-net 算法开发了一个集成深度学习(IDL)模型,用于 UIA 检测、分割和形态测量。使用狄斯相似系数(DSC)和类内相关系数(ICC)对模型性能进行了评估,并将资深放射科医生的测量结果作为参考标准。评估了 IDL 模型提高初级放射医师测量 UIA 形态特征的能力。结果 研究纳入了 1182 名 UIA 患者和 578 名无 UIA 的对照组作为训练数据集(55 岁 [IQR,47-62],1,012 [57.5%] 女性),并纳入了 535 名 UIA 患者作为多中心外部测试集(57 岁 [IQR,50-63],353 [66.0%] 女性)。IDL 模型检测 UIA 的准确率达到 97%,UIA 分割的 DSC 为 0.90(95%CI,0.88-0.92)。基于模型的形态测量结果与参考标准测量结果显示出良好的一致性(所有 ICC 均大于 0.85)。在多中心外部测试集中,IDL 模型也与参考标准测量结果一致(所有 ICC 均大于 0.80)。由 IDL 模型辅助的初级放射医师在测量 UIA 大小方面的表现明显提高(ICC 从 0.88 [0.80-0.92] 提高到 0.96 [0.92-0.97],P < .001)。结论 利用 CTA 数据开发的集成深度学习模型在 UIA 检测、分割和形态测量方面表现出色,可用于协助经验不足的放射科医生对 UIA 进行形态分析。©RSNA,2024。
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