|
artificial intelligence |
13 |
|
cone-beam computed tomography |
11 |
|
generative ai |
10 |
|
nasopharyngeal carcinoma |
9 |
|
impacted third molar |
8 |
|
low-dose protocols |
8 |
|
mandibular canal |
8 |
|
mri |
8 |
|
periodontal ligament |
8 |
|
radiomics |
8 |
|
deep learning |
7 |
|
large language models |
7 |
|
extranodal extension |
6 |
|
nodal staging |
6 |
|
outcome |
6 |
|
class iii malocclusion |
5 |
|
computed tomography |
5 |
|
convolutional neural network |
5 |
|
dentistry |
5 |
|
diagnosis and treatment |
5 |
|
diagnostic accuracy |
5 |
|
magnetic resonance imaging |
5 |
|
maxillofacial diseases |
5 |
|
panoramic radiographs |
5 |
|
radiographic imaging |
5 |
|
vision-language models |
5 |
|
chatbot |
4 |
|
chatgpt |
4 |
|
dento-maxillofacial radiology |
4 |
|
gpt |
4 |
|
llms |
4 |
|
maxillary sinus |
4 |
|
mucosal retention cyst |
4 |
|
mucosal thickening |
4 |
|
orthodontics |
4 |
|
scoping review |
4 |
|
semi-supervised learning |
4 |
|
tooth identification |
4 |
|
tooth segmentation |
4 |
|
transformer neural network |
4 |
|
bone-to-implant contact |
3 |
|
edentulous maxilla |
3 |
|
head and neck cancer |
3 |
|
imaging |
3 |
|
salivary glands tumor |
3 |
|
systematic review |
3 |
|
texture analysis |
3 |
|
zygomatic implants |
3 |
|
zygomatic thickness |
3 |
|
adjuvant chemotherapy |
2 |
|
benign and reactive |
2 |
|
cervical nodal necrosis |
2 |
|
diffusion magnetic resonance imaging |
2 |
|
diffusion-weighted imaging |
2 |
|
early detection |
2 |
|
head and neck |
2 |
|
head and neck cancer detection |
2 |
|
hyperplasia |
2 |
|
lymph nodes |
2 |
|
meta-analysis |
2 |
|
normal nodal size |
2 |
|
npc |
2 |
|
prognostic value |
2 |
|
tonsillar asymmetry |
2 |
|
treatment response |
2 |
|
tumor heterogeneity map |
2 |
|
amide proton transfer |
1 |
|
amide proton transfer-weighted imaging |
1 |
|
automatic tumor delineation |
1 |
|
automatic tumor detection and segmentation |
1 |
|
bariatric surgery |
1 |
|
benign and malignant |
1 |
|
benign hyperplasia |
1 |
|
blood cadmium |
1 |
|
brown adipose tissue |
1 |
|
chemical exchange saturation transfer |
1 |
|
circadian rhythm |
1 |
|
cohort study |
1 |
|
computational neural network |
1 |
|
conventional magnetic resonance imaging |
1 |
|
convolutional neural network (cnn) |
1 |
|
diffusion weighted imaging |
1 |
|
diffusion-weighted magnetic resonance imaging |
1 |
|
disease-free survival |
1 |
|
distant metastases |
1 |
|
early detection of cancer |
1 |
|
endoscopic examination |
1 |
|
endoscopy |
1 |
|
epstein-barr virus |
1 |
|
feature selection stability |
1 |
|
genetic predisposition to disease |
1 |
|
genetic risk |
1 |
|
head and neck cancers |
1 |
|
heart failure |
1 |
|
histogram analysis |
1 |
|
inflammatory microenvironment |
1 |
|
intravoxel incoherent motion |
1 |
|
loneliness |
1 |
|
machine learning |
1 |
|
magnetic resonance imaging (mri) |
1 |
|
magnetic resonance spectroscopy |
1 |
|
marginal zone b-cell lymphoma |
1 |
|
mortality |
1 |
|
mtnr1b rs10830963 |
1 |
|
mucosa-associated lymphoid tissue |
1 |
|
nasopharyngeal cancer |
1 |
|
nasopharyngeal carcinomas (npcs) |
1 |
|
national health and nutrition examination survey |
1 |
|
nodal volume |
1 |
|
non-contrast-enhanced mri |
1 |
|
outcome prediction |
1 |
|
parotid gland tumors |
1 |
|
physical activity |
1 |
|
plasma epstein–barr virus dna |
1 |
|
polarization |
1 |
|
prediction |
1 |
|
primary tumour invasion |
1 |
|
prognostic marker |
1 |
|
quantitative mri sequence |
1 |
|
recist |
1 |
|
recist guideline |
1 |
|
recursive-partitioning analysis |
1 |
|
retropharyngeal node |
1 |
|
risk stratification |
1 |
|
salivary gland neoplasms |
1 |
|
screening |
1 |
|
social isolation |
1 |
|
stroke |
1 |
|
survival |
1 |
|
t1rho imaging |
1 |
|
tendon regeneration |
1 |
|
tendon stem/progenitor cells |
1 |
|
test-retest repeatability |
1 |
|
texture |
1 |
|
tnm stage |
1 |
|
treatment outcome |
1 |
|
type 2 diabetes mellitus |
1 |
|
uk biobank |
1 |
|
vasoactive intestinal peptide |
1 |
|
volumetric measurement |
1 |
|
white adipose tissue |
1 |
