A Deep Learning-Based Method for Rapid 3D Whole-Heart Modeling in Congenital Heart Disease.

IF 1.9 4区 医学 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS Cardiology Pub Date : 2024-10-11 DOI:10.1159/000541980
Haiping Huang, Yisheng Wu
{"title":"A Deep Learning-Based Method for Rapid 3D Whole-Heart Modeling in Congenital Heart Disease.","authors":"Haiping Huang, Yisheng Wu","doi":"10.1159/000541980","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>This study aimed to develop a deep learning-based method for generating three-dimensional heart mesh models for patients with congenital heart disease by integrating medical imaging and clinical diagnostic information.</p><p><strong>Methods: </strong>A deep learning model was trained using CT and cardiac MRI, along with clinical data from 110 patients. The Web-based platform automatically outputs STL files for 3D printing and Unity 3D OBJ files for virtual reality (VR) applications upon uploading the medical images and diagnostic information. The models were tested on three congenital heart disease cases, with corresponding 3D-printed and VR heart models generated.</p><p><strong>Results: </strong>The 3D-printed and VR heart models received high praise from professional doctors for their anatomical accuracy and clarity. Evaluations indicated that the proposed method effectively and rapidly reconstructs complex congenital heart disease structures, proving useful for preoperative planning and diagnostic support.</p><p><strong>Conclusion: </strong>The 3D modeling approach has the potential to enhance the precision of surgical planning and diagnosis for congenital heart disease. Future studies should explore larger datasets and training models for different types of congenital heart disease to validate the model's broad applicability.</p>","PeriodicalId":9391,"journal":{"name":"Cardiology","volume":" ","pages":"1-16"},"PeriodicalIF":1.9000,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cardiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1159/000541980","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Introduction: This study aimed to develop a deep learning-based method for generating three-dimensional heart mesh models for patients with congenital heart disease by integrating medical imaging and clinical diagnostic information.

Methods: A deep learning model was trained using CT and cardiac MRI, along with clinical data from 110 patients. The Web-based platform automatically outputs STL files for 3D printing and Unity 3D OBJ files for virtual reality (VR) applications upon uploading the medical images and diagnostic information. The models were tested on three congenital heart disease cases, with corresponding 3D-printed and VR heart models generated.

Results: The 3D-printed and VR heart models received high praise from professional doctors for their anatomical accuracy and clarity. Evaluations indicated that the proposed method effectively and rapidly reconstructs complex congenital heart disease structures, proving useful for preoperative planning and diagnostic support.

Conclusion: The 3D modeling approach has the potential to enhance the precision of surgical planning and diagnosis for congenital heart disease. Future studies should explore larger datasets and training models for different types of congenital heart disease to validate the model's broad applicability.

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
基于深度学习的先天性心脏病快速三维全心建模方法
简介:本研究旨在开发一种基于深度学习的方法,通过整合医学影像和临床诊断信息生成先天性心脏病患者的三维心脏网格模型:本研究旨在开发一种基于深度学习的方法,通过整合医学影像和临床诊断信息,为先天性心脏病患者生成三维心脏网状模型:方法:使用 CT 和心脏核磁共振成像(CMR)图像以及 110 名患者的临床数据训练深度学习模型。基于网络的平台在上传医学影像和诊断信息后,会自动输出用于三维打印的 STL 文件和用于虚拟现实(VR)应用的 Unity 3D OBJ 文件。这些模型在三个先天性心脏病病例上进行了测试,并生成了相应的 3D 打印和 VR 心脏模型:结果:3D 打印和 VR 心脏模型的解剖准确性和清晰度得到了专业医生的高度评价。评估结果表明,所提出的方法能有效、快速地重建复杂的先天性心脏病结构,对术前规划和诊断支持非常有用:结论:三维建模方法有望提高先天性心脏病手术规划和诊断的精确度。未来的研究应针对不同类型的先天性心脏病探索更大的数据集和训练模型,以验证该模型的广泛适用性。
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
Cardiology
Cardiology 医学-心血管系统
CiteScore
3.40
自引率
5.30%
发文量
56
审稿时长
1.5 months
期刊介绍: ''Cardiology'' features first reports on original clinical, preclinical and fundamental research as well as ''Novel Insights from Clinical Experience'' and topical comprehensive reviews in selected areas of cardiovascular disease. ''Editorial Comments'' provide a critical but positive evaluation of a recent article. Papers not only describe but offer critical appraisals of new developments in non-invasive and invasive diagnostic methods and in pharmacologic, nutritional and mechanical/surgical therapies. Readers are thus kept informed of current strategies in the prevention, recognition and treatment of heart disease. Special sections in a variety of subspecialty areas reinforce the journal''s value as a complete record of recent progress for all cardiologists, internists, cardiac surgeons, clinical physiologists, pharmacologists and professionals in other areas of medicine interested in current activity in cardiovascular diseases.
期刊最新文献
Comparative study of the therapeutic effects of radiofrequency ablation of ganglionated plexi guided by high-frequency stimulation and anatomical localization methods in the treatment of vagal syncope in young people. Electrocardiographic strain and relationship with LV remodelling and clinical outcomes in patients with aortic stenosis undergoing transcatheter aortic valve implantation. Assessment of coronary microvascular dysfunction by angiography-based index of microcirculatory resistance: an indirect meta-analysis. Genetic Association of the Ins/Del Variant of ACE and Risk of Cardiomyopathy: A Case-Control Study and Updated Meta-Analysis. Real-World Evidence: Integrating Machine Learning with Real-World Big Data for Predictive Analytics in Healthcare.
×
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