Orthopedics

Robust optic disc and cup segmentation with deep learning for glaucoma detection.

Glaucoma is rated as the leading cause of irreversible vision loss worldwide. Early detection of glaucoma is important for providing timely treatment and minimizing the vision loss. In this paper, we developed a robust segmentation method for optic disc and cup segmentation using a modified U-Net architecture, which combines the widely adopted pre-trained ResNet-34 model as encoding layers with cl...

Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain F-FDG PET.

Dedicated brain positron emission tomography (PET) devices can provide higher-resolution images with much lower doses compared to conventional whole-body PET systems, which is important to support PET neuroimaging and particularly useful for the diagnosis of neurodegenerative diseases. However, when a dedicated brain PET scanner does not come with a combined CT or transmission source, there is no ...

Natural language processing of radiology reports for identification of skeletal site-specific fractures.

BACKGROUND: Osteoporosis has become an important public health issue. Most of the population, particularly elderly people, are at some degree of risk ...

Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy.

Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in progressive non-traumatic compression of the cervical spinal cord. S...

Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments.

Background and purpose - Artificial intelligence has rapidly become a powerful method in image analysis with the use of convolutional neural networks ...

Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs.

OBJECTIVE: To identify the feasibility of using a deep convolutional neural network (DCNN) for the detection and localization of hip fractures on plai...

Artificial intelligence to diagnose meniscus tears on MRI.

PURPOSE: The purpose of this study was to build and evaluate a high-performance algorithm to detect and characterize the presence of a meniscus tear o...

Integrating spatial configuration into heatmap regression based CNNs for landmark localization.

In many medical image analysis applications, only a limited amount of training data is available due to the costs of image acquisition and the large m...

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity.

In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by ut...

Automatic knee meniscus tear detection and orientation classification with Mask-RCNN.

PURPOSE: This work presents our contribution to a data challenge organized by the French Radiology Society during the Journées Francophones de Radiolo...

Diagnosing osteoarthritis from T maps using deep learning: an analysis of the entire Osteoarthritis Initiative baseline cohort.

OBJECTIVE: We aim to study to what extent conventional and deep-learning-based T relaxometry patterns are able to distinguish between knees with and w...

A multi-scale data fusion framework for bone age assessment with convolutional neural networks.

Bone age assessment (BAA) has various clinical applications such as diagnosis of endocrine disorders and prediction of final adult height for adolesce...

Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach.

BACKGROUND: Cervical spine injuries (CSI) are a major concern in young pediatric trauma patients. The consequences of missed injuries and difficulties...

Posterior scleral deformations around optic disc are associated with visual field damage in open-angle glaucoma patients with myopia.

PURPOSE: To identify important variables associated with visual field (VF) defects in open-angle glaucoma (OAG) with myopia.

Joint sparse graph and flexible embedding for graph-based semi-supervised learning.

This letter introduces a framework for graph-based semi-supervised learning by estimating a flexible non-linear projection and its linear regression m...

Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN.

PURPOSE: Ultrasound (US) provides real-time, two-/three-dimensional safe imaging. Due to these capabilities, it is considered a safe alternative to in...

Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach.

PURPOSE: We present an automated method for extracting anatomical parameters from biplanar radiographs of the spine, which is able to deal with a wide...

Robot-assisted ankle rehabilitation: a review.

The aim of this review paper is to summarize recent developments and research in robotics, relevant to the field of ankle rehabilitation, to overview...

Convolutional neural network to predict the local recurrence of giant cell tumor of bone after curettage based on pre-surgery magnetic resonance images.

OBJECTIVE: To predict the local recurrence of giant cell bone tumors (GCTB) on MR features and the clinical characteristics after curettage using a de...

Learning joint space-time-frequency features for EEG decoding on small labeled data.

Brain-computer interfaces (BCIs), which control external equipment using cerebral activity, have received considerable attention recently. Translating...