AIMC Topic: Hip Fractures

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Logistic regression and machine learning predicted patient mortality from large sets of diagnosis codes comparably.

Journal of clinical epidemiology
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.

Hip Fracture Surgery without Transfusion in Patients with Hemoglobin Less Than 10 g/dL.

Clinics in orthopedic surgery
BACKGROUD: Hip fracture surgery is associated with blood loss, which may lead to adverse patient outcomes. The hemoglobin level declines gradually in most hip fracture cases involving femoral neck fractures and intertrochanteric fractures. It decreas...

Classification of femur trochanteric fracture: Evaluating the reliability of Tang classification.

Injury
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...

The effect of deep convolutional neural networks on radiologists' performance in the detection of hip fractures on digital pelvic radiographs.

European journal of radiology
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.

Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach.

PloS one
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...

Detection and localisation of hip fractures on anteroposterior radiographs with artificial intelligence: proof of concept.

Clinical radiology
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.

Identification of Vertebral Fractures by Convolutional Neural Networks to Predict Nonvertebral and Hip Fractures: A Registry-based Cohort Study of Dual X-ray Absorptiometry.

Radiology
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...

Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs.

European radiology
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...