AIMC Topic: Osteoporotic Fractures
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Evaluation and analysis of risk factors for adverse events of the fractured vertebra post-percutaneous kyphoplasty: a retrospective cohort study using multiple machine learning models.
Journal of orthopaedic surgery and research
Sep 18, 2024
BACKGROUND: Adverse events of the fractured vertebra (AEFV) post-percutaneous kyphoplasty (PKP) can lead to recurrent pain and neurological damage, which considerably affect the prognosis of patients and the quality of life. This study aimed to analy...
Subject-level spinal osteoporotic fracture prediction combining deep learning vertebral outputs and limited demographic data.
Archives of osteoporosis
Sep 10, 2024
UNLABELLED: Automated screening for vertebral fractures could improve outcomes. We achieved an AUC-ROC = 0.968 for the prediction of moderate to severe fracture using a GAM with age and three maximal vertebral body scores of fracture from a convoluti...
Machine learning value in the diagnosis of vertebral fractures: A systematic review and meta-analysis.
European journal of radiology
Sep 1, 2024
PURPOSE: To evaluate the diagnostic accuracy of machine learning (ML) in detecting vertebral fractures, considering varying fracture classifications, patient populations, and imaging approaches.
Predicting Secondary Vertebral Compression Fracture After Vertebral Augmentation via CT-Based Machine Learning Radiomics-Clinical Model.
Academic radiology
Jul 10, 2024
RATIONALE AND OBJECTIVES: Secondary vertebral compression fractures (SVCF) are very common in patients after vertebral augmentation (VA). The aim of this study was to establish a radiomic-based model to predict SVCF and specify appropriate treatment ...
Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal.
Endocrine
Jul 9, 2024
BACKGROUND: It was essential to identify individuals at high risk of fragility fracture and prevented them due to the significant morbidity, mortality, and economic burden associated with fragility fracture. The quantitative ultrasound (QUS) showed p...
Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi-Center Study.
Orthopaedic surgery
Jul 1, 2024
BACKGROUND: The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X-ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicator...
The potential role for artificial intelligence in fracture risk prediction.
The lancet. Diabetes & endocrinology
Jun 25, 2024
Osteoporotic fractures are a major health challenge in older adults. Despite the availability of safe and effective therapies for osteoporosis, these therapies are underused in individuals at high risk for fracture, calling for better case-finding an...
Deep learning for osteoporosis screening using an anteroposterior hip radiograph image.
European journal of orthopaedic surgery & traumatology : orthopedie traumatologie
Jun 19, 2024
PURPOSE: Osteoporosis is a common bone disorder characterized by decreased bone mineral density (BMD) and increased bone fragility, which can lead to fractures and eventually cause morbidity and mortality. It is of great concern that the one-year mor...
Prediction of subsequent fragility fractures: application of machine learning.
BMC musculoskeletal disorders
Jun 4, 2024
BACKGROUND: Machine learning (ML) has shown exceptional promise in various domains of medical research. However, its application in predicting subsequent fragility fractures is still largely unknown. In this study, we aim to evaluate the predictive p...
A novel case-finding strategy based on artificial intelligence for the systematic identification and management of individuals with osteoporosis or at varying risk of fragility fracture.
Archives of osteoporosis
May 31, 2024
UNLABELLED: An artificial intelligence-based case-finding strategy has been developed to systematically identify individuals with osteoporosis or at varying risk of fragility fracture. This strategy has the potential to close the critical care gap in...
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