Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review

S Mureșanu, O Almășan, M Hedeșiu, L Dioșan… - Oral Radiology, 2023 - Springer
This study aimed at performing a systematic review of the literature on the application of
artificial intelligence (AI) in dental and maxillofacial cone beam computed tomography …

Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis

M Serafin, B Baldini, F Cabitza, G Carrafiello… - La radiologia …, 2023 - Springer
Objectives The aim of the present systematic review and meta-analysis is to assess the
accuracy of automated landmarking using deep learning in comparison with manual tracing …

[HTML][HTML] Accuracy of artificial intelligence in the detection and segmentation of oral and maxillofacial structures using cone-beam computed tomography images: a …

F Abesi, AS Jamali, M Zamani - Polish Journal of Radiology, 2023 - ncbi.nlm.nih.gov
Purpose The aim of the present systematic review and meta-analysis was to resolve the
conflicts on the diagnostic accuracy of artificial intelligence systems in detecting and …

FDGR-Net: Feature Decouple and Gated Recalibration Network for medical image landmark detection

X Li, S Lv, J Zhang, M Li, JJ Rodriguez-Andina… - Expert Systems with …, 2024 - Elsevier
Medical image landmark detection can automatically determine the pre-set position
coordinates in medical images, so as to further assist disease diagnosis and treatment …

Semi-supervised anatomical landmark detection via shape-regulated self-training

R Chen, Y Ma, L Liu, N Chen, Z Cui, G Wei, W Wang - Neurocomputing, 2022 - Elsevier
Well-annotated medical images are costly and sometimes even impossible to acquire,
hindering landmark detection accuracy to some extent. Semi-supervised learning alleviates …

CephalFormer: incorporating global structure constraint into visual features for general cephalometric landmark detection

Y Jiang, Y Li, X Wang, Y Tao, J Lin, H Lin - International Conference on …, 2022 - Springer
Accurate cephalometric landmark detection is a crucial step in orthodontic diagnosis and
therapy planning. However, existing deep learning-based methods lack the ability to …

Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset

J Sahlsten, J Järnstedt, J Jaskari, H Naukkarinen… - PloS one, 2024 - journals.plos.org
Cephalometric analysis is critically important and common procedure prior to orthodontic
treatment and orthognathic surgery. Recently, deep learning approaches have been …

CMF-Net: craniomaxillofacial landmark localization on CBCT images using geometric constraint and transformer

G Lu, H Shu, H Bao, Y Kong, C Zhang… - Physics in Medicine …, 2023 - iopscience.iop.org
Accurate and robust anatomical landmark localization is a mandatory and crucial step in
deformation diagnosis and treatment planning for patients with craniomaxillofacial (CMF) …

[HTML][HTML] Automatic generation of knee kinematic models from medical imaging

B Shi, M Barzan, A Nasseri, JN Maharaj… - Computer Methods and …, 2024 - Elsevier
Abstract Background and Objective Three-dimensional spatial mechanisms have been used
to accurately predict passive knee kinematics, and have shown potential to be used in …

Anchor Ball Regression Model for large-scale 3D skull landmark detection

T He, G Xu, L Cui, W Tang, J Long, J Guo - Neurocomputing, 2024 - Elsevier
Recent deep learning models have exhibited impressive performance in the area of 3D skull
landmark detection, but most of them aimed to detect a fixed number of landmarks. This …