OBJECTIVE: To identify the feasibility of using a deep convolutional neural network (DCNN) for the detection and localization of hip fractures on plain frontal pelvic radiographs (PXRs). Hip fracture is a leading worldwide health problem for the elde...
Background Detection of vertebral fractures (VFs) aids in management of osteoporosis and targeting of fracture prevention therapies. Purpose To determine whether convolutional neural networks (CNNs) can be trained to identify VFs at VF assessment (VF...
AIM: To investigate the feasibility of applying a deep convolutional neural network (CNN) for detection/localisation of acute proximal femoral fractures (APFFs) on hip radiographs.
OBJECTIVE: Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to pre...
PURPOSE: The purpose of our study is to develop deep convolutional neural network (DCNN) for detecting hip fractures using CT and MRI as a gold standard, and to evaluate the diagnostic performance of 7 readers with and without DCNN.
Background and purpose - Deep-learning approaches based on convolutional neural networks (CNNs) are gaining interest in the medical imaging field. We evaluated the diagnostic performance of a CNN to discriminate femoral neck fractures, trochanteric f...
INTRODUCTION: Given the drawbacks of a femoral intertrochanteric fracture classification based on 2-dimensional radiographic imaging, an artificial intelligence-based classification system- the Tang classification system-which uses 3-dimensional imag...
OBJECTIVE: The objective of the study was to compare the performance of logistic regression and boosted trees for predicting patient mortality from large sets of diagnosis codes in electronic healthcare records.
The Journal of the American Academy of Orthopaedic Surgeons
33315645
OBJECTIVES: Accurately stratifying patients in the preoperative period according to mortality risk informs treatment considerations and guides adjustments to bundled reimbursements. We developed and compared three machine learning models to determine...