AIMC Topic: Radiography

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LiDSCUNet++: A lightweight depth separable convolutional UNet++ for vertebral column segmentation and spondylosis detection.

Research in veterinary science
Accurate computer-aided diagnosis systems rely on precise segmentation of the vertebral column to assist physicians in diagnosing various disorders. However, segmenting spinal disks and bones becomes challenging in the presence of abnormalities and c...

Predicting Knee Osteoarthritis Severity from Radiographic Predictors: Data from the Osteoarthritis Initiative.

Annals of biomedical engineering
PURPOSE: In knee osteoarthritis (KOA) treatment, preventive measures to reduce its onset risk are a key factor. Among individuals with radiographically healthy knees, however, future knee joint integrity and condition cannot be predicted by clinicall...

Validation of a novel artificial intelligence model (SpinePose) to automatically and accurately predict spinopelvic parameters using scoliosis radiographs in an external cohort.

Neurosurgical focus
OBJECTIVE: SpinePose was developed in 2024 as a novel artificial intelligence (AI) tool to automatically predict spinopelvic parameters with high accuracy and without the need for manual entry. The authors' published results demonstrated excellent pe...

Can Gpt-4o Accurately Diagnose Trauma X-Rays? A Comparative Study with Expert Evaluations.

The Journal of emergency medicine
BACKGROUND: The latest artificial intelligence (AI) model, GPT-4o, introduced by OpenAI, can process visual data, presenting a novel opportunity for radiographic evaluation in trauma patients.

Automated determination of hip arthrosis on the Kellgren-Lawrence scale in pelvic digital radiographs scans using machine learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automated analysis of digital radiographs of the pelvis to determine the hip arthrosis state in concordance with the Kellgren-Lawrence scale could facilitate and standardize radiogram descriptions.

Diagnosis of trigeminal neuralgia based on plain skull radiography using convolutional neural network.

Scientific reports
This study aimed to determine whether trigeminal neuralgia can be diagnosed using convolutional neural networks (CNNs) based on plain X-ray skull images. A labeled dataset of 166 skull images from patients aged over 16 years with trigeminal neuralgia...

Automatic assessment of lower limb deformities using high-resolution X-ray images.

BMC musculoskeletal disorders
BACKGROUND: Planning an osteotomy or arthroplasty surgery on a lower limb requires prior classification/identification of its deformities. The detection of skeletal landmarks and the calculation of angles required to identify the deformities are trad...

Deep learning-based identification of vertebral fracture and osteoporosis in lateral spine radiographs and DXA vertebral fracture assessment to predict incident fracture.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
Deep learning (DL) identification of vertebral fractures and osteoporosis in lateral spine radiographs and DXA vertebral fracture assessment (VFA) images may improve fracture risk assessment in older adults. In 26 299 lateral spine radiographs from 9...

Advanced feature fusion of radiomics and deep learning for accurate detection of wrist fractures on X-ray images.

BMC musculoskeletal disorders
OBJECTIVE: The aim of this study was to develop a hybrid diagnostic framework integrating radiomic and deep features for accurate and reproducible detection and classification of wrist fractures using X-ray images.