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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...

Measuring the impact of age, gender and dementia on communication-robot interventions in residential care homes.

Geriatrics & gerontology international
AIM: The primary aim of this study was to examine the impact of age, gender and the stage of dementia on the results of an assistive technology intervention that make use of communication robots (com-robots). The intervention was designed to improve ...

Comparison of statistical machine learning models for rectal protocol compliance in prostate external beam radiation therapy.

Medical physics
PURPOSE: Limiting the dose to the rectum can be one of the most challenging aspects of creating a dosimetric external beam radiation therapy (EBRT) plan for prostate cancer treatment. Rectal sparing devices such as hydrogel spacers offer the prospect...

A Framework for Applying Natural Language Processing in Digital Health Interventions.

Journal of medical Internet research
BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open corresponde...

fNIRS-GANs: data augmentation using generative adversarial networks for classifying motor tasks from functional near-infrared spectroscopy.

Journal of neural engineering
OBJECTIVE: Functional near-infrared spectroscopy (fNIRS) is expected to be applied to brain-computer interface (BCI) technologies. Since lengthy fNIRS measurements are uncomfortable for participants, it is difficult to obtain enough data to train cla...

Convolutional and recurrent neural network for human activity recognition: Application on American sign language.

PloS one
Human activity recognition is an important and difficult topic to study because of the important variability between tasks repeated several times by a subject and between subjects. This work is motivated by providing time-series signal classification...

Virtual reality operating room with AI guidance: design and validation of a fire scenario.

Surgical endoscopy
BACKGROUND: Operating room (OR) fires are uncommon but disastrous events. Inappropriate handling of OR fires can result in injuries, even death. Aiming to simulate OR fire emergencies and effectively train clinicians to react appropriately, we have d...

Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: To develop a deep-learning-based computer-aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician-based accuracy of diagnostic assessments of laryngosco...

Numerosity discrimination in deep neural networks: Initial competence, developmental refinement and experience statistics.

Developmental science
Both humans and non-human animals exhibit sensitivity to the approximate number of items in a visual array, as indexed by their performance in numerosity discrimination tasks, and even neonates can detect changes in numerosity. These findings are oft...

Initial classification of low back and leg pain based on objective functional testing: a pilot study of machine learning applied to diagnostics.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
OBJECTIVE: The five-repetition sit-to-stand (5R-STS) test was designed to capture objective functional impairment and thus provided an adjunctive dimension in patient assessment. The clinical interpretability and confounders of the 5R-STS remain poor...