Orthopedics

Predicting Readmission After Anterior, Posterior, and Posterior Interbody Lumbar Spinal Fusion: A Neural Network Machine Learning Approach.

BACKGROUND: Readmission after spine surgery is costly and a relatively common occurrence. Previous research identified several risk factors for readmission; however, the conclusions remain equivocal. Machine learning algorithms offer a unique perspective in analysis of risk factors for readmission and can help predict the likelihood of this occurrence. This study evaluated a neural network (NN), a...

Development and validation of a sensitive LC-MS/MS method for pioglitazone: application towards pharmacokinetic and tissue distribution study in rats.

In the present study, a sensitive LC-MS/MS method was developed and validated to measure pioglitazone (PGZ) concentrations in rat plasma and tissues. The chromatographic separation was achieved by using a YMC Pro C column (100 mm × 4.6 mm, 3μ) with a mobile phase consisting of formic acid (0.1% v/v) and acetonitrile (5 : 95) at a flow rate of 0.7 mL min and injection volume of 10 μL (IS: rosiglita...

Multitask Feature Learning Meets Robust Tensor Decomposition for EEG Classification.

In this article, we study a tensor-based multitask learning (MTL) method for classification. Taking into account the fact that in many real-world appl...

Opposite valence social information provided by bio-robotic demonstrators shapes selection processes in the green bottle fly.

Social learning represents a high-level complex process to acquire information about the environment, which is increasingly reported in invertebrates....

Critical evaluation of deep neural networks for wrist fracture detection.

Wrist Fracture is the most common type of fracture with a high incidence rate. Conventional radiography (i.e. X-ray imaging) is used for wrist fractur...

Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients.

Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection a...

Machine learning analysis of gene expression profile reveals a novel diagnostic signature for osteoporosis.

BACKGROUND: Osteoporosis (OP) is increasingly prevalent with the aging of the world population. It is urgent to identify efficient diagnostic signatur...

Using artificial intelligence to diagnose fresh osteoporotic vertebral fractures on magnetic resonance images.

BACKGROUND CONTEXT: Accurate diagnosis of osteoporotic vertebral fracture (OVF) is important for improving treatment outcomes; however, the gold stand...

Machine learning for the prediction of bone metastasis in patients with newly diagnosed thyroid cancer.

OBJECTIVES: This study aimed to establish a machine learning prediction model that can be used to predict bone metastasis (BM) in patients with newly ...

Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors.

OBJECTIVES: To build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of t...

Deep Learning-Based Diabetic Retinopathy Severity Grading System Employing Quadrant Ensemble Model.

The diabetic retinopathy accounts in the deterioration of retinal blood vessels leading to a serious compilation affecting the eyes. The automated DR ...

Robotics Versus Navigation Versus Conventional Total Hip Arthroplasty: Does the Use of Technology Yield Superior Outcomes?

BACKGROUND: The use of technology such as navigation and robotic systems may improve the accuracy of component positioning in total hip arthroplasty (...

[The superiority of navigation and robotics in hip arthroplasty: fact or myth?].

Computer-assisted surgery represents a relatively novel treatment option in total hip arthroplasty, which has been supported by the technological prog...

A robot-aided visuomotor wrist training induces gains in proprioceptive and movement accuracy in the contralateral wrist.

Proprioceptive training is a neurorehabilitation approach known to improve proprioceptive acuity and motor performance of a joint/limb system. Here, w...

Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study.

BACKGROUND: Osteoporosis has increased and developed into a serious public health concern worldwide. Despite the high prevalence, osteoporosis is sile...

XAOM: A method for automatic alignment and orientation of radiographs for computer-aided medical diagnosis.

BACKGROUND AND OBJECTIVES: Computer-aided diagnosis relies on machine learning algorithms that require filtered and preprocessed data as the input. Al...

Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring.

Ataxic gait monitoring and assessment of neurological disorders belong to important multidisciplinary areas that are supported by digital signal proce...

Application of artificial intelligence in diagnosis of osteoporosis using medical images: a systematic review and meta-analysis.

Artificial intelligence (AI) is a potentially reliable assistant in the diagnosis of osteoporosis. This meta-analysis aims to assess the diagnostic ac...

Harnessing the Multistability of Kresling Origami for Reconfigurable Articulation in Soft Robotic Arms.

This study examines a biology-inspired approach of using reconfigurable articulation to reduce the control requirement for soft robotic arms. We const...

Deep neural networks with promising diagnostic accuracy for the classification of atypical femoral fractures.

Background and purpose - A correct diagnosis is essential for the appropriate treatment of patients with atypical femoral fractures (AFFs). The diagno...