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The utility of standing knee radiographs for detection of lipohemarthrosis: comparison with supine horizontal beam radiographs
ObjectivesLipohemarthrosis is a key finding in acute trauma patients and indicates an intra-articular fracture. The horizontal beam lateral...
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Performance of AI to exclude normal chest radiographs to reduce radiologists’ workload
IntroductionThis study investigates the performance of a commercially available artificial intelligence (AI) system to identify normal chest...
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Checklists for interpreting chest radiographs: a sco** review protocol
IntroductionWhat is known about checklists for interpreting chest radiographs? The question will guide the development of the inclusion criteria for...
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Vertebral fracture severity assessment on anteroposterior radiographs with a new semi-quantitative technique
SummaryWe developed a new tool to assess the severity of osteoporotic vertebral fracture using radiographs of the spine. Our technique can be used in...
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Residual networks models detection of atrial septal defect from chest radiographs
ObjectThe purpose of this study was to explore a machine learning-based residual networks (ResNets) model to detect atrial septal defect (ASD) on...
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A novel approach for screening standard anteroposterior pelvic radiographs in children
Anteroposterior pelvic radiography is the first‐line imaging modality for diagnosing developmental dysplasia of the hip (DDH). Nonstandard...
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Artificial intelligence for automated detection and measurements of carpal instability signs on conventional radiographs
ObjectivesTo develop and validate an artificial intelligence (AI) system for measuring and detecting signs of carpal instability on conventional...
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Deep learning model performance for identifying pediatric acute respiratory distress syndrome on chest radiographs
PurposePediatric acute respiratory distress syndrome (PARDS) is underrecognized in the pediatric intensive care unit and the interpretation of chest...
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Chiropractors’ perceptions on the use of spinal radiographs in clinical practice: a qualitative study
BackgroundRadiography is commonly used in the assessment of spinal disorders, despite a lack of high-quality evidence demonstrating improved clinical...
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An AI-Based Image Quality Control Framework for Knee Radiographs
Image quality control (QC) is crucial for the accurate diagnosis of knee diseases using radiographs. However, the manual QC process is subjective,...
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Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs
ObjectivesTo develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs.
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Enhancing Caries Detection in Bitewing Radiographs Using YOLOv7
The study aimed to evaluate the impact of image size, area of detection (IoU) thresholds and confidence thresholds on the performance of the YOLO...
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A deep learning approach for projection and body-side classification in musculoskeletal radiographs
BackgroundThe growing prevalence of musculoskeletal diseases increases radiologic workload, highlighting the need for optimized workflow management...
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Evaluation of anterior translation in total knee arthroplasty utilizing stress radiographs
BackgroundFlexion instability is a common cause for revision after total knee arthroplasty (TKA); however, little objective criteria exist to...
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Radiologic blind spots in hip and pelvic radiographs
PurposeThe purpose of our study was to identify the locations at which hip and pelvic fractures are commonly missed on radiographs.
Methods ...
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Deep Learning Model for Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Chest Radiographs
Bronchopulmonary dysplasia (BPD) is common in preterm infants and may result in pulmonary vascular disease, compromising lung function. This study...
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Deep Learning–Assisted Identification of Femoroacetabular Im**ement (FAI) on Routine Pelvic Radiographs
To use a novel deep learning system to localize the hip joints and detect findings of cam-type femoroacetabular im**ement (FAI). A retrospective...
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External validation of a deep learning model for predicting bone mineral density on chest radiographs
SummaryWe developed a new model for predicting bone mineral density on chest radiographs and externally validated it using images captured at...
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Detecting Avascular Necrosis of the Lunate from Radiographs Using a Deep-Learning Model
Deep-learning (DL) algorithms have the potential to change medical image classification and diagnostics in the coming decade. Delayed diagnosis and...
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Pelvis radiographs in children with cerebral palsy: effects of patient positioning on calculating migration percentages
BackgroundHip displacement in children with cerebral palsy (CP) is monitored by measuring migration percentage on anteroposterior pelvis radiographs....
