AIMC Topic: Humans

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Uncovering Predictors of Low Hippocampal Volume: Evidence from a Large-Scale Machine-Learning-Based Study in the UK Biobank.

Neuroepidemiology
INTRODUCTION: Hippocampal atrophy is an established biomarker for conversion from the normal ageing process to developing cognitive impairment and dementia. This study used a novel hypothesis-free machine-learning approach, to uncover potential risk ...

Audiovisual Moments in Time: A large-scale annotated dataset of audiovisual actions.

PloS one
We present Audiovisual Moments in Time (AVMIT), a large-scale dataset of audiovisual action events. In an extensive annotation task 11 participants labelled a subset of 3-second audiovisual videos from the Moments in Time dataset (MIT). For each tria...

Using Natural Language Processing to Identify Home Health Care Patients at Risk for Diagnosis of Alzheimer's Disease and Related Dementias.

Journal of applied gerontology : the official journal of the Southern Gerontological Society
This study aimed to: (1) validate a natural language processing (NLP) system developed for the home health care setting to identify signs and symptoms of Alzheimer's disease and related dementias (ADRD) documented in clinicians' free-text notes; (2) ...

Intravoxel incoherent motion and diffusion kurtosis imaging and their machine-learning-based texture analysis for detection and assessment of prostate cancer severity at 3 T.

NMR in biomedicine
OBJECTIVES: To evaluate the role of combined intravoxel incoherent motion and diffusion kurtosis imaging (IVIM-DKI) and their machine-learning-based texture analysis for the detection and assessment of severity in prostate cancer (PCa).

Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution.

Medical physics
BACKGROUND: Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phant...

Deceptive learning in histopathology.

Histopathology
AIMS: Deep learning holds immense potential for histopathology, automating tasks that are simple for expert pathologists and revealing novel biology for tasks that were previously considered difficult or impossible to solve by eye alone. However, the...

Development of risk-score model in patients with negative surgical margin after robot-assisted radical prostatectomy.

Scientific reports
A total of 739 patients underwent RARP as initial treatment for PCa from November 2011 to October 2018. Data on BCR status, clinical and pathological parameters were collected from the clinical records. After excluding cases with neoadjuvant and/or a...

Application of artificial intelligence and machine learning in drug repurposing.

Progress in molecular biology and translational science
The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achi...

Predicting Extubation Readiness in Preterm Infants Utilizing Machine Learning: A Diagnostic Utility Study.

The Journal of pediatrics
OBJECTIVE: The objective of this study was to predict extubation readiness in preterm infants using machine learning analysis of bedside pulse oximeter and ventilator data.

MLASM: Machine learning based prediction of anticancer small molecules.

Molecular diversity
Cancer, being the second leading cause of death globally. So, the development of effective anticancer treatments is crucial in the field of medicine. Anticancer peptides (ACPs) have shown promising therapeutic potential in cancer treatment compared t...