AIMC Topic: Deep Learning

Clear Filters Showing 21571 to 21580 of 28423 articles

Deep Learning for Fall Risk Assessment With Inertial Sensors: Utilizing Domain Knowledge in Spatio-Temporal Gait Parameters.

IEEE journal of biomedical and health informatics
Fall risk assessment is essential to predict and prevent falls in geriatric populations, especially patients with life-long conditions like neurological disorders. Inertial sensor-based pervasive gait analysis systems have become viable means to faci...

Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage.

Neuroradiology
PURPOSE: To analyze the implementation of deep learning software for the detection and worklist prioritization of acute intracranial hemorrhage on non-contrast head CT (NCCT) in various clinical settings at an academic medical center.

Usefulness of a deep learning system for diagnosing Sjögren's syndrome using ultrasonography images.

Dento maxillo facial radiology
OBJECTIVES: We evaluated the diagnostic performance of a deep learning system for the detection of Sjögren's syndrome (SjS) in ultrasonography (US) images, and compared it with the performance of inexperienced radiologists.

Artificial intelligence applications for thoracic imaging.

European journal of radiology
Artificial intelligence is a hot topic in medical imaging. The development of deep learning methods and in particular the use of convolutional neural networks (CNNs), have led to substantial performance gain over the classic machine learning techniqu...

Comparative analysis of computer-vision and BLE technology based indoor navigation systems for people with visual impairments.

International journal of health geographics
BACKGROUND: Considerable number of indoor navigation systems has been proposed to augment people with visual impairments (VI) about their surroundings. These systems leverage several technologies, such as computer-vision, Bluetooth low energy (BLE), ...

Feasibility study for use of angiographic parametric imaging and deep neural networks for intracranial aneurysm occlusion prediction.

Journal of neurointerventional surgery
BACKGROUND: Angiographic parametric imaging (API), based on digital subtraction angiography (DSA), is a quantitative imaging tool that may be used to extract contrast flow parameters related to hemodynamic conditions in abnormal pathologies such as i...

EMS-Net: A Deep Learning Method for Autodetecting Epileptic Magnetoencephalography Spikes.

IEEE transactions on medical imaging
Epilepsy is a neurological disorder characterized by sudden and unpredictable epileptic seizures, which incurs significant negative impacts on patients' physical, psychological and social health. A practical approach to assist with the clinical asses...

Boosting ICD multi-label classification of health records with contextual embeddings and label-granularity.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: This work deals with clinical text mining, a field of Natural Language Processing applied to biomedical informatics. The aim is to classify Electronic Health Records with respect to the International Classification of Diseas...

An enhanced deep learning approach for brain cancer MRI images classification using residual networks.

Artificial intelligence in medicine
Cancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types depending on their shape, texture, and location. Proper diagnosis ...