Journal of imaging informatics in medicine
Feb 21, 2024
The hyoid bone displacement and rotation are critical kinematic events of the swallowing process in the assessment of videofluoroscopic swallow studies (VFSS). However, the quantitative analysis of such events requires frame-by-frame manual annotatio...
Journal of applied clinical medical physics
Jan 25, 2024
PURPOSE: To evaluate the 3D U-Net model for automatic segmentation and measurement of cervical spine structures using magnetic resonance (MR) images of healthy adults.
OBJECTIVES: To compare diagnostic accuracy of a deep learning artificial intelligence (AI) for cervical spine (C-spine) fracture detection on CT to attending radiologists and assess which undetected fractures were injuries in need of stabilising ther...
Cervical spondylotic myelopathy (CSM) is the most severe type of cervical spondylosis. It is challenging to achieve early diagnosis with current clinical diagnostic tools. In this paper, we propose an end-to-end deep learning approach for early diagn...
The journal of evidence-based dental practice
Sep 28, 2023
ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Neural networks for classification of cervical vertebrae maturation: a systematic review. Mathew R, Palatinus S, Padala S, Alshehri A, Awadh W, Bhandi S, Thomas J, Patil S. Angle Orthod. 2022 Nov 1;92(6):7...
BACKGROUND: This work aims to develop a deep learning model, assessing atlantoaxial subluxation (AAS) in rheumatoid arthritis (RA), which can often be ambiguous in clinical practice.
BACKGROUND: Many scholars have proven cervical vertebral maturation (CVM) method can predict the growth and development and assist in choosing the best time for treatment. However, assessing CVM is a complex process. The experience and seniority of t...
OBJECTIVE: Interspinous motion (ISM) is a representative method for evaluating the functional fusion status following anterior cervical discectomy and fusion (ACDF) surgery, but the associated measuring difficulty and potential errors in the clinical...
OBJECTIVES: To validate the subjective image quality and lesion detectability of deep learning-accelerated Dixon (DL-Dixon) imaging of the cervical spine compared with routine Dixon imaging.
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