{"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.
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.