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Outcome of kidney function in adults on long-term home parenteral nutrition for chronic intestinal failure.

Nutrition (Burbank, Los Angeles County, Calif.)
OBJECTIVE: The aim of this study was to evaluate kidney function outcome in adults on home parenteral nutrition (HPN) for chronic intestinal failure using the newly recommended equations for estimated glomerular filtration rate (eGFR) assessment in c...

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance.

Journal of sports sciences
Objective assessment of an athlete's performance is of importance in elite sports to facilitate detailed analysis. The implementation of automated detection and recognition of sport-specific movements overcomes the limitations associated with manual ...

Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study.

Human brain mapping
Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large nu...

Decoding attentional states for neurofeedback: Mindfulness vs. wandering thoughts.

NeuroImage
Neurofeedback requires a direct translation of neuronal brain activity to sensory input given to the user or subject. However, decoding certain states, e.g., mindfulness or wandering thoughts, from ongoing brain activity remains an unresolved problem...

Characterization of expressed human meibum using hyperspectral stimulated Raman scattering microscopy.

The ocular surface
PURPOSE: This study examined whether hyperspectral stimulated Raman scattering (hsSRS) microscopy can detect differences in meibum lipid to protein composition of normal and evaporative dry eye subjects with meibomian gland dysfunction.

Retinal image quality assessment using deep learning.

Computers in biology and medicine
Poor-quality retinal images do not allow an accurate medical diagnosis, and it is inconvenient for a patient to return to a medical center to repeat the fundus photography exam. In this paper, a robust automatic system is proposed to assess the quali...

Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study.

Lancet (London, England)
BACKGROUND: Non-contrast head CT scan is the current standard for initial imaging of patients with head trauma or stroke symptoms. We aimed to develop and validate a set of deep learning algorithms for automated detection of the following key finding...

Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: False positives in digital mammography screening lead to high recall rates, resulting in unnecessary medical procedures to patients and health care costs. This study aimed to investigate the revolutionary deep learning methods to distinguish...

Automated seizure prediction.

Epilepsy & behavior : E&B
In the past two decades, significant advances have been made on automated electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number of innovative algorithms have been introduced that can aid in epilepsy diagnosis with a h...

Multimodal MRI-based classification of migraine: using deep learning convolutional neural network.

Biomedical engineering online
BACKGROUND: Recently, deep learning technologies have rapidly expanded into medical image analysis, including both disease detection and classification. As far as we know, migraine is a disabling and common neurological disorder, typically characteri...