A5031921339- Dental Radiography and Imaging
- Dental Implant Techniques and Outcomes
- Dental Research and COVID-19
- Dental materials and restorations
- Medical Imaging Techniques and Applications
- Endodontics and Root Canal Treatments
- Advanced Neural Network Applications
- COVID-19 diagnosis using AI
- Spectroscopy Techniques in Biomedical and Chemical Research
- Anatomy and Medical Technology
University of Illinois Urbana-Champaign
2022-2024
Zhejiang University
2023
High-fidelity three-dimensional (3D) models of tooth-bone structures are valuable for virtual dental treatment planning; however, they require integrating data from cone-beam computed tomography (CBCT) and intraoral scans (IOS) using methods that either error-prone or time-consuming. Hence, this study presents Deep Dental Multimodal Fusion (DDMF), an automatic multimodal framework reconstructs 3D CBCT IOS. Specifically, the DDMF comprises IOS segmentation modules as well a reconstruction...
Infrared (IR) spectroscopic imaging is of potentially wide use in medical applications due to its ability capture both chemical and spatial information. This complexity the data necessitates using machine intelligence as well presents an opportunity harness a high-dimensionality set that offers far more information than today's manually-interpreted images. While convolutional neural networks (CNNs), including well-known U-Net model, have demonstrated impressive performance image...
Abstract Cone-beam computed tomography (CBCT) is one of the most widely used digital models in dental practices. A critical step virtual treatment planning to accurately delineate all tooth-bone structures from CBCT with high fidelity. Previous studies have established several methods for segmentation using deep learning. However, inherent resolution discrepancy and loss occlusal information largely limited its clinical applicability. Here, we present a Deep Dental Multimodal Analysis (DDMA)...
A critical step in virtual dental treatment planning is to accurately delineate all tooth-bone structures from CBCT with high fidelity and accurate anatomical information. Previous studies have established several methods for segmentation using deep learning. However, the inherent resolution discrepancy of loss occlusal dentition information largely limited its clinical applicability. Here, we present a Deep Dental Multimodal Analysis (DDMA) framework consisting model, an intraoral scan...