Orthopedics

Crash test-based assessment of injury risks for adults and children when colliding with personal mobility devices and service robots.

Autonomous mobility devices such as transport, cleaning, and delivery robots, hold a massive economic and social benefit. However, their deployment should not endanger bystanders, particularly vulnerable populations such as children and older adults who are inherently smaller and fragile. This study compared the risks faced by different pedestrian categories and determined risks through crash test...

Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods.

OBJECTIVES: In this study, we aimed to differentiate normal cervical graphs and graphs of diseases that cause mechanical neck pain by using deep convolutional neural networks (DCNN) technology.

Artificial intelligence in spine care: current applications and future utility.

PURPOSE: The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given...

Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs using an Adaptation of the Genant Semiquantitative Criteria.

RATIONALE AND OBJECTIVES: Osteoporosis affects 9% of individuals over 50 in the United States and 200 million women globally. Spinal osteoporotic comp...

Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classification.

The hypercube optimization search (HOS) approach is a new efficient and robust metaheuristic algorithm that simulates the dove's movement in quest of ...

Evaluation of deep learning reconstructed high-resolution 3D lumbar spine MRI.

OBJECTIVES: To compare interobserver agreement and image quality of 3D T2-weighted fast spin echo (T2w-FSE) L-spine MRI images processed with a deep l...

A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images.

Automatic CT segmentation of proximal femur has a great potential for use in orthopedic diseases, especially in the imaging-based assessments of hip f...

Image Recognition and Extraction of Students' Human Motion Features Based on Graph Neural Network.

In order to improve students' overall subhealth behavior, teenagers' physical health problems have attracted more and more attention. The state clearl...

Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance.

PURPOSE: To evaluate stability and machine learning-based classification performance of radiomic features of spine bone tumors using diffusion- and T2...

Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening.

The preoperative qualitative and hierarchical diagnosis of intervertebral foramen stenosis is very important for clinicians to explore the effect of m...

Deep learning and morphometric approach for Sex determination of the lumbar vertebrae in a Thai population.

Sex determination is a fundamental step in biological profile estimation from skeletal remains in forensic anthropology. This study proposes deep lear...

Lower-Limb Joint Torque Prediction Using LSTM Neural Networks and Transfer Learning.

Estimation of joint torque during movement provides important information in several settings, such as effect of athletes' training or of a medical in...

Improved-Mask R-CNN: Towards an accurate generic MSK MRI instance segmentation platform (data from the Osteoarthritis Initiative).

INTRODUCTION: Objective assessment of osteoarthritis (OA) Magnetic Resonance Imaging (MRI) scans can address the limitations of the current OA assessm...

Fermatean fuzzy ELECTRE multi-criteria group decision-making and most suitable biomedical material selection.

ELECTRE is a family of multi-criteria decision analysis techniques, which has the ability to provide as much as possible precise and suitable set of a...

John Charnley Award: Deep Learning Prediction of Hip Joint Center on Standard Pelvis Radiographs.

BACKGROUND: Accurate hip joint center (HJC) determination is critical for preoperative planning, intraoperative execution, clinical outcomes after tot...

Impact of the training loss in deep learning-based CT reconstruction of bone microarchitecture.

PURPOSE: Computed tomography (CT) is a technique of choice to image bone structure at different scales. Methods to enhance the quality of degraded rec...

BirdBot achieves energy-efficient gait with minimal control using avian-inspired leg clutching.

Designers of legged robots are challenged with creating mechanisms that allow energy-efficient locomotion with robust and minimalistic control. Source...

Fast Kinematic Re-Calibration for Industrial Robot Arms.

Accurate kinematic modelling is pivotal in the safe and reliable execution of both contact and non-contact robotic applications. The kinematic models ...

Utility of a novel integrated deep convolutional neural network for the segmentation of hip joint from computed tomography images in the preoperative planning of total hip arthroplasty.

PURPOSE: Preoperative three-dimensional planning is important for total hip arthroplasty. To simulate the placement of joint implants on computed tomo...

Multi-landmark environment analysis with reinforcement learning for pelvic abnormality detection and quantification.

Morphological abnormalities of the femoroacetabular (hip) joint are among the most common human musculoskeletal disorders and often develop asymptomat...