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

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.