Hip Fracture

Hip fractures represent a significant health burden, particularly among older adults, demanding improved risk prediction and treatment strategies. Current research focuses on developing predictive models using machine learning techniques, such as convolutional neural networks (CNNs) and temporal graph convolutional neural networks (TG-CNNs), incorporating both clinical data and advanced imaging (e.g., DXA scans) to identify individuals at high risk. These models aim to improve the accuracy and efficiency of fracture risk assessment, potentially guiding preventative interventions and optimizing post-operative care by predicting complications and mortality risk. Ultimately, this research strives to reduce the incidence and impact of hip fractures through earlier identification of at-risk individuals and improved management of the condition.

Papers

October 18, 2024

A novel approach towards the classification of Bone Fracture from Musculoskeletal Radiography images using Attention Based Transfer Learning
Sayeda Sanzida Ferdous Ruhi, Fokrun Nahar, Adnan Ferdous Ashrafi
Transfer Learning Classification Code Human Attention Medical Image Analysis Fracture Detection Hip Fracture

September 10, 2024

Developing the Temporal Graph Convolutional Neural Network Model to Predict Hip Replacement using Electronic Health Records
Zoe Hancox, Sarah R. Kingsbury, Andrew Clegg, Philip G. Conaghan, Samuel D. Relton
Electronic Health Record Temporal Graph Patient Trajectory Total Knee Arthroplasty Hip Fracture

May 30, 2024

A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture
Anjum Shaik, Kristoffer Larsen, Nancy E. Lane, Chen Zhao, Kuan-Jui Su, Joyce H. Keyak, Qing Tian, Qiuying Sha, Hui Shen, Hong-Wen Deng, Weihua Zhou
Machine Learning Constructive Approach Uncertainty Quantification High Quality Risk Description Fracture Risk Hip Fracture

April 29, 2024

Feature importance to explain multimodal prediction models. A clinical use case
Jorn-Jan van de Beld, Shreyasi Pathak, Jeroen Geerdink, Johannes H. Hegeman, Christin Seifert
Feature Importance Multimodal Deep Learning Explainable Model Surgical Datasets Clinical Application Multimodal Prediction Postoperative Complication Hip Fracture

October 26, 2023

Neural Stress Fields for Reduced-order Elastoplasticity and Fracture
Zeshun Zong, Xuan Li, Minchen Li, Maurizio M. Chiaramonte, Wojciech Matusik, Eitan Grinspun, Kevin Carlberg, Chenfanfu Jiang, Peter Yichen Chen
Reduced Order Material Point Method Stress Field Hip Fracture

October 3, 2022

A New Hip Fracture Risk Index Derived from FEA-Computed Proximal Femur Fracture Loads and Energies-to-Failure
Xuewei Cao, Joyce H Keyak, Sigurdur Sigurdsson, Chen Zhao, Weihua Zhou, Anqi Liu, Thomas Lang, Hong-Wen Deng, Vilmundur Gudnason, Qiuying Sha
Fracture Risk Hip Fracture

Hip Fracture

Papers

A novel approach towards the classification of Bone Fracture from Musculoskeletal Radiography images using Attention Based Transfer Learning

Developing the Temporal Graph Convolutional Neural Network Model to Predict Hip Replacement using Electronic Health Records

A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture

Feature importance to explain multimodal prediction models. A clinical use case

Neural Stress Fields for Reduced-order Elastoplasticity and Fracture

A New Hip Fracture Risk Index Derived from FEA-Computed Proximal Femur Fracture Loads and Energies-to-Failure