AIMC Topic: Anatomic Landmarks
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Automatic Pavlov ratio measurement method based on spinal landmarks identification by a deep-learning model.
Medical physics
Dec 23, 2024
BACKGROUND: Cervical canal stenosis is one of the important pathogenic factors of cervical spondylosis. The accuracy of the Pavlov ratio measurement is crucial for the diagnosis and treatment of cervical spinal stenosis. Manual measurement is influen...
Evaluation of the clinical utility of lateral cephalometry reconstructed from computed tomography extracted by artificial intelligence.
Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
Dec 9, 2024
This study assessed the accuracy and reliability of artificial intelligence (AI)-reconstructed images of two-dimensional (2D) lateral cephalometric analyses of facial computed tomography (CT) images, which is widely used for the diagnosis of craniofa...
Artificial intelligence for orthodontic diagnosis and treatment planning: A scoping review.
Journal of dentistry
Nov 4, 2024
OBJECTIVES: To provide an overview of artificial intelligence (AI) applications in orthodontic diagnosis and treatment planning, and to evaluate whether AI improves accuracy, reliability, and time efficiency compared to expert-based manual approaches...
A Multi-Task Based Deep Learning Framework With Landmark Detection for MRI Couinaud Segmentation.
IEEE journal of translational engineering in health and medicine
Nov 4, 2024
To achieve precise Couinaud liver segmentation in preoperative planning for hepatic surgery, accommodating the complex anatomy and significant variations, optimizing surgical approaches, reducing postoperative complications, and preserving liver func...
AI-based open-source software for cephalometric analysis from limited FOV radiographs.
Journal of dentistry
Oct 20, 2024
BACKGROUND: Artificial Intelligence (AI) in dental diagnostics is evolving, offering innovative approaches for conducting cephalometric analysis with less manual input and overcoming the limitations of traditional imaging methods. To enhance the diag...
Automatic point detection on cephalograms using convolutional neural networks: A two-step method.
Dental materials journal
Sep 4, 2024
This project aimed to develop an artificial intelligence program tailored for cephalometric images. The program employs a convolutional neural network with 6 convolutional layers and 2 affine layers. It identifies 18 key points on the skull to comput...
A machine learning approach for age prediction based on trigeminal landmarks.
Journal of forensic and legal medicine
Aug 26, 2024
OBJECTIVE: The aim of this study was to estimate the chronological age (CA) of a growing individual using a new machine learning approach on Cone Beam Computed Tomography (CBCT).
Automatic localization of anatomical landmarks in head cine fluoroscopy images via deep learning.
Medical physics
Aug 14, 2024
BACKGROUND: Fluoroscopy guided interventions (FGIs) pose a risk of prolonged radiation exposure; personalized patient dosimetry is necessary to improve patient safety during these procedures. However, current FGIs systems do not capture the precise e...
Assessment of left ventricular wall thickness and dimension: accuracy of a deep learning model with prediction uncertainty.
The international journal of cardiovascular imaging
Aug 10, 2024
Left ventricular (LV) geometric patterns aid clinicians in the diagnosis and prognostication of various cardiomyopathies. The aim of this study is to assess the accuracy and reproducibility of LV dimensions and wall thickness using deep learning (DL)...
Optimization of the automated Sunnybrook Facial Grading System - Improving the reliability of a deep learning network with facial landmarks.
European annals of otorhinolaryngology, head and neck diseases
Aug 7, 2024
OBJECTIVE: The Sunnybrook Facial Grading System (SFGS) is a well-established grading system to assess the severity and progression of a unilateral facial palsy. The automation of the SFGS makes the SFGS more accessible for researchers, students, clin...
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