Orthopedics

Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network.

BACKGROUND: We aimed to construct an artificial intelligence (AI) guided identification of suspicious bone metastatic lesions from the whole-body bone scintigraphy (WBS) images by convolutional neural networks (CNNs).

Your mileage may vary: impact of data input method for a deep learning bone age app's predictions.

OBJECTIVE: The purpose of this study was to evaluate agreement in predictions made by a bone age prediction application ("app") among three data input methods.

Detecting hip osteoarthritis on clinical CT: a deep learning application based on 2-D summation images derived from CT.

UNLABELLED: We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that res...

Development and internal validation of machine learning algorithms to predict patient satisfaction after total hip arthroplasty.

BACKGROUND: Patient satisfaction is a unique and important measure of success after total hip arthroplasty (THA). Our study aimed to evaluate the use ...

Can co-authorship networks be used to predict author research impact? A machine-learning based analysis within the field of degenerative cervical myelopathy research.

INTRODUCTION: Degenerative Cervical Myelopathy (DCM) is a common and disabling condition, with a relatively modest research capacity. In order to acce...

Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches.

Deep learning-based MRI diagnosis of internal joint derangement is an emerging field of artificial intelligence, which offers many exciting possibilit...

A CNN-based method to reconstruct 3-D spine surfaces from US images in vivo.

Three-dimensional (3-D) reconstruction of the spine surface is of strong clinical relevance for the diagnosis and prognosis of spine disorders and int...

Report of clinical bone age assessment using deep learning for an Asian population in Taiwan.

INTRODUCTION: A deep learning-based automatic bone age identification system (ABAIs) was introduced in medical imaging. This ABAIs enhanced accurate, ...

Contralaterally Enhanced Networks for Thoracic Disease Detection.

Identifying and locating diseases in chest X-rays are very challenging, due to the low visual contrast between normal and abnormal regions, and distor...

Efficacy of wrist robot-aided orthopedic rehabilitation: a randomized controlled trial.

BACKGROUND: In recent years, many studies focused on the use of robotic devices for both the assessment and the neuro-motor reeducation of upper limb ...

The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions.

Gait analysis based on inertial sensors has become an effective method of quantifying movement mechanics, such as joint kinematics and kinetics. Machi...

Artificial Intelligence Algorithm with ICD Coding Technology Guided by the Embedded Electronic Medical Record System in Medical Record Information Management.

The study aims to explore the application of international classification of diseases (ICD) coding technology and embedded electronic medical record (...

Automated description of the mandible shape by deep learning.

PURPOSE: The shape of the mandible has been analyzed in a variety of fields, whether to diagnose conditions like osteoporosis or osteomyelitis, in for...

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis.

The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes i...

Deep learning takes the pain out of back breaking work - Automatic vertebral segmentation and attenuation measurement for osteoporosis.

BACKGROUND: Osteoporosis is an underdiagnosed and undertreated disease worldwide. Recent studies have highlighted the use of simple vertebral trabecul...

Machine Learning-Based Individualized Survival Prediction Model for Total Knee Replacement in Osteoarthritis: Data From the Osteoarthritis Initiative.

OBJECTIVE: By using machine learning, our study aimed to build a model to predict risk and time to total knee replacement (TKR) of an osteoarthritic k...

A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors.

OBJECTIVES: To build and validate deep learning and machine learning fusion models to classify benign, malignant, and intermediate bone tumors based o...

Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning.

Judging swallowing kinematic impairments via videofluoroscopy represents the gold standard for the detection and evaluation of swallowing disorders. H...

Deep Learning-Based Image Automatic Assessment and Nursing of Upper Limb Motor Function in Stroke Patients.

This paper mainly introduces the relevant contents of automatic assessment of upper limb mobility after stroke, including the relevant knowledge of cl...

Robotic sample changers for macromolecular X-ray crystallography and biological small-angle X-ray scattering at the National Synchrotron Light Source II.

Here we present two robotic sample changers integrated into the experimental stations for the macromolecular crystallography (MX) beamlines AMX and FM...