AIMC Topic: Radiography
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Can computer vision / artificial intelligence locate key reference points and make clinically relevant measurements on axillary radiographs?
International orthopaedics
Nov 12, 2024
PURPOSE: Computer vision and artificial intelligence (AI) offer the opportunity to rapidly and accurately interpret standardized x-rays. We trained and validated a machine learning tool that identified key reference points and determined glenoid retr...
A novel semi-supervised learning model based on pelvic radiographs for ankylosing spondylitis diagnosis reduces 90% of annotation cost.
Computers in biology and medicine
Nov 8, 2024
OBJECTIVE: Our study aims to develop a deep learning-based Ankylosing Spondylitis (AS) diagnostic model that achieves human expert-level performance using only a minimal amount of labeled samples for training, in regions with limited access to expert...
Development and accuracy of an artificial intelligence model for predicting the progression of hip osteoarthritis using plain radiographs and clinical data: a retrospective study.
BMC musculoskeletal disorders
Nov 8, 2024
BACKGROUND: Predicting the progression of hip osteoarthritis (OA) remains challenging, and no reliable predictive method has been established. This study aimed to develop an artificial intelligence (AI) model to predict hip OA progression via plain r...
Artificial intelligence tools trained on human-labeled data reflect human biases: a case study in a large clinical consecutive knee osteoarthritis cohort.
Scientific reports
Nov 5, 2024
Humans have been shown to have biases when reading medical images, raising questions about whether humans are uniform in their disease gradings. Artificial intelligence (AI) tools trained on human-labeled data may have inherent human non-uniformity. ...
HarDNet-based deep learning model for osteoporosis screening and bone mineral density inference from hand radiographs.
Bone
Nov 3, 2024
PURPOSE: Osteoporosis, affecting over 200 million individuals, often remains unrecognized and untreated, increasing the risk of fractures in older adults. Osteoporosis is typically diagnosed with bone mineral density (BMD) measured by dual-energy X-r...
Detection, classification, and characterization of proximal humerus fractures on plain radiographs.
The bone & joint journal
Nov 1, 2024
AIMS: The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular ...
A deep learning algorithm that aids visualization of femoral neck fractures and improves physician training.
Injury
Oct 31, 2024
PURPOSE: Missed fractures are the most common radiologic error in clinical practice, and erroneous classification could lead to inappropriate treatment and unfavorable prognosis. Here, we developed a fully automated deep learning model to detect and ...
Uncertainty-Aware Deep Learning Characterization of Knee Radiographs for Large-Scale Registry Creation.
The Journal of arthroplasty
Oct 29, 2024
BACKGROUND: We present an automated image ingestion pipeline for a knee radiography registry, integrating a multilabel image-semantic classifier with conformal prediction-based uncertainty quantification and an object detection model for knee hardwar...
Automatic Hardy and Clapham's classification of hallux sesamoid position on foot radiographs using deep neural network.
Foot and ankle surgery : official journal of the European Society of Foot and Ankle Surgeons
Oct 16, 2024
BACKGROUND: There is currently no deep neural network (DNN) capable of automatically classifying tibial sesamoid position (TSP) on foot radiographs.
Deep Learning-Based Estimation of Radiographic Position to Automatically Set Up the X-Ray Prime Factors.
Journal of imaging informatics in medicine
Oct 14, 2024
Radiation dose and image quality in radiology are influenced by the X-ray prime factors: KVp, mAs, and source-detector distance. These parameters are set by the X-ray technician prior to the acquisition considering the radiographic position. A wrong ...
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