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Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain F-FDG PET.

Physics in medicine and biology
Dedicated brain positron emission tomography (PET) devices can provide higher-resolution images with much lower doses compared to conventional whole-body PET systems, which is important to support PET neuroimaging and particularly useful for the diag...

Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy.

PloS one
Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in progressive non-traumatic compression of the cervical spinal cord. Spine surgeons must consider a large quantity of information relating to disease presentation, imagin...

Multilayer network analysis of miRNA and protein expression profiles in breast cancer patients.

PloS one
MiRNAs and proteins play important roles in different stages of breast tumor development and serve as biomarkers for the early diagnosis of breast cancer. A new algorithm that combines machine learning algorithms and multilayer complex network analys...

Utilization of Minimally Invasive Thymectomy and Margin-Negative Resection for Early-Stage Thymoma.

The Annals of thoracic surgery
BACKGROUND: Minimally invasive thymectomy (MIT) has demonstrated improved short-term outcomes compared with open thymectomy (OT). Although adoption of MIT for thymoma is increasing, oncologic outcomes have not been well characterized.

Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments.

Acta orthopaedica
Background and purpose - Artificial intelligence has rapidly become a powerful method in image analysis with the use of convolutional neural networks (CNNs). We assessed the ability of a CNN, with a fast object detection algorithm previously identify...

Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach.

NeuroImage. Clinical
BACKGROUND: It is becoming increasingly clear that pathophysiological processes underlying psychiatric disorders categories are heterogeneous on many levels, including symptoms, disease course, comorbidity and biological underpinnings. This heterogen...

Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease.

NeuroImage. Clinical
BACKGROUND: In a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patter...

Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods.

Journal of psychiatric research
Schizophrenia is a common mental disorder with high heritability. It is genetically complex and to date more than a hundred risk loci have been identified. Association of environmental factors and schizophrenia has also been reported, while epigeneti...

Predicting discharge placement after elective surgery for lumbar spinal stenosis using machine learning methods.

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
PURPOSE: An excessive amount of total hospitalization is caused by delays due to patients waiting to be placed in a rehabilitation facility or skilled nursing facility (RF/SNF). An accurate preoperative prediction of who would need a RF/SNF place aft...