AIMC Topic: Humans

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Feasibility of a deep learning-based method for automated localization of pelvic floor landmarks using stress MR images.

International urogynecology journal
INTRODUCTION AND HYPOTHESIS: Magnetic resonance imaging (MRI) plays an important role in assessing pelvic organ prolapse (POP), and automated pelvic floor landmark localization potentially accelerates MRI-based measurements of POP. Herein, we aimed t...

Prediction and interpretation of cancer survival using graph convolution neural networks.

Methods (San Diego, Calif.)
The survival rate of cancer has increased significantly during the past two decades for breast, prostate, testicular, and colon cancer, while the brain and pancreatic cancers have a much lower median survival rate that has not improved much over the ...

Machine learning combining CT findings and clinical parameters improves prediction of length of stay and ICU admission in torso trauma.

European radiology
OBJECTIVE: To develop machine learning (ML) models capable of predicting ICU admission and extended length of stay (LOS) after torso (chest, abdomen, or pelvis) trauma, by using clinical and/or imaging data.

Modeling autosomal dominant Alzheimer's disease with machine learning.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.

Approaches to assessing the impact of robotics in geriatric mental health care: a scoping review.

International review of psychiatry (Abingdon, England)
The goals of this scoping literature review are to (1) aggregate the current research involving socially assistive robots in the setting of geriatric psychiatry and (2) examine the outcome measures used in these studies and determine where the gaps a...

Natural language processing was effective in assisting rapid title and abstract screening when updating systematic reviews.

Journal of clinical epidemiology
BACKGROUND AND OBJECTIVE: To examine whether the use of natural language processing (NLP) technology is effective in assisting rapid title and abstract screening when updating a systematic review.

Na MRI in ischemic stroke: Acquisition time reduction using postprocessing with convolutional neural networks.

NMR in biomedicine
Quantitative Na magnetic resonance imaging (MRI) provides tissue sodium concentration (TSC), which is connected to cell viability and vitality. Long acquisition times are one of the most challenging aspects for its clinical establishment. K-space un...

Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging.

Journal of neurointerventional surgery
Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibilities in diagnostic radiology. In recent years, the performance of deep learning (DL) algorithms on v...