AIMC Topic: Humans

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Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Protruding lesions of the small bowel vary in wireless capsule endoscopy (WCE) images, and their automatic detection may be difficult. We aimed to develop and test a deep learning-based system to automatically detect protruding l...

Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening.

European radiology
OBJECTIVE: Osteoporosis is a prevalent and treatable condition, but it remains underdiagnosed. In this study, a deep learning-based system was developed to automatically measure bone mineral density (BMD) for opportunistic osteoporosis screening usin...

Combination of compositional data analysis and machine learning approaches to identify sources and geochemical associations of potentially toxic elements in soil and assess the associated human health risk in a mining city.

Environmental pollution (Barking, Essex : 1987)
Mining activities change the chemical composition of the environment and have negative reflection on people's health and there is no single measure to deal with adverse consequences of mining activities, as each case is specific and needs to be under...

Machine learning algorithms applied to a prediction of personal overall thermal comfort using skin temperatures and occupants' heating behavior.

Applied ergonomics
Thermal comfort modeling has been of interest in built environment research for decades. Mostly the modeling approaches focused on an average response of a large group of building occupants. Recently, the focus has been shifted towards personal comfo...

Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains.

Medical image analysis
Ultra-high field 7T MRI scanners, while producing images with exceptional anatomical details, are cost prohibitive and hence highly inaccessible. In this paper, we introduce a novel deep learning network that fuses complementary information from spat...

Early detection of sepsis utilizing deep learning on electronic health record event sequences.

Artificial intelligence in medicine
BACKGROUND: The timeliness of detection of a sepsis incidence in progress is a crucial factor in the outcome for the patient. Machine learning models built from data in electronic health records can be used as an effective tool for improving this tim...

Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction.

Artificial intelligence in medicine
Antimicrobial resistance has become one of the most important health problems and global action plans have been proposed globally. Prevention plays a key role in these actions plan and, in this context, we propose the use of Artificial Intelligence, ...

Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVE: Multimodal data analysis and large-scale computational capability is entering medicine in an accelerative fashion and has begun to influence investigational work in a variety of disciplines. It is also informing us of therap...

Detecting vulnerable plaque with vulnerability index based on convolutional neural networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Plaque rupture and subsequent thrombosis are major processes of acute cardiovascular events. The Vulnerability Index is a very important indicator of whether a plaque is ruptured, and these easily ruptured or fragile plaques can be detected early. Th...