AIMC Topic: Middle Aged

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A deep learning model for diagnosing dystrophinopathies on thigh muscle MRI images.

BMC neurology
BACKGROUND: Dystrophinopathies are the most common type of inherited muscular diseases. Muscle biopsy and genetic tests are effective to diagnose the disease but cost much more than primary hospitals can reach. The more available muscle MRI is promis...

Additional value of deep learning computed tomographic angiography-based fractional flow reserve in detecting coronary stenosis and predicting outcomes.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Deep learning (DL) has achieved great success in medical imaging and could be utilized for the non-invasive calculation of fractional flow reserve (FFR) from coronary computed tomographic angiography (CCTA) (CT-FFR).

Screening of sleep apnea based on heart rate variability and long short-term memory.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Sleep apnea syndrome (SAS) is a prevalent sleep disorder in which apnea and hypopnea occur frequently during sleep and result in increase of the risk of lifestyle-related disease development as well as daytime sleepiness. Although SAS is a c...

Robotic transanal minimally invasive surgery: a case series.

Surgical endoscopy
INTRODUCTION: This study describes the experience with robot-assisted transanal minimally invasive surgery (rTAMIS) at a single institution. TAMIS has become a popular minimally invasive technique for local excision of well-selected rectal lesions. r...

The Space Between: Nature and Machine Heuristics in Evaluations of Organisms, Cyborgs, and Robots.

Cyberpsychology, behavior and social networking
Cues delivered by agents are known to trigger mental shortcuts associated with ontological category or the kind of thing an agent is. Two such heuristics are key to considering organic and machine agents, and result in biased evaluations: the machine...

Breath biopsy of breast cancer using sensor array signals and machine learning analysis.

Scientific reports
Breast cancer causes metabolic alteration, and volatile metabolites in the breath of patients may be used to diagnose breast cancer. The objective of this study was to develop a new breath test for breast cancer by analyzing volatile metabolites in t...

Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs.

American journal of ophthalmology
PURPOSE: To assess whether longitudinal changes in a deep learning algorithm's predictions of retinal nerve fiber layer (RNFL) thickness based on fundus photographs can predict future development of glaucomatous visual field defects.