AIMC Topic: Female

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Significance of gender, brain region and EEG band complexity analysis for Parkinson's disease classification using recurrence plots and machine learning algorithms.

Physical and engineering sciences in medicine
Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogram (EEG) plays an important role in diagnosing PD as it offers a non-invasive continuous assessment ...

Beyond averaging: A transformer approach to decoding event related brain potentials.

NeuroImage
The objective of this study is to assess the potential of a transformer-based deep learning approach applied to event-related brain potentials (ERPs) derived from electroencephalographic (EEG) data. Traditional methods involve averaging the EEG signa...

Development and Validation of Machine Learning-Based Model for Hospital Length of Stay in Patients Undergoing Endovascular Interventional Embolization for Intracranial Aneurysms.

World neurosurgery
OBJECTIVE: This study was to explore the factors associated with prolonged hospital length of stay (LOS) in patients with intracranial aneurysms (IAs) undergoing endovascular interventional embolization and construct prediction model machine learning...

QuanFormer: A Transformer-Based Precise Peak Detection and Quantification Tool in LC-MS-Based Metabolomics.

Analytical chemistry
In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. Current peak picking methods still cause high rates of incorrectly picked peaks to influence the reli...

Optimizing stroke prediction using gated recurrent unit and feature selection in Sub-Saharan Africa.

Clinical neurology and neurosurgery
BACKGROUND: Stroke remains a leading cause of death and disability worldwide, with African populations bearing a disproportionately high burden due to limited healthcare infrastructure. Early prediction and intervention are critical to reducing strok...

Natural language processing-based classification of early Alzheimer's disease from connected speech.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: The automated analysis of connected speech using natural language processing (NLP) emerges as a possible biomarker for Alzheimer's disease (AD). However, it remains unclear which types of connected speech are most sensitive and specific...

An unsupervised learning approach for clustering joint trajectories of Alzheimer's disease biomarkers: An application to ADNI Data.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Current models of Alzheimer's disease (AD) progression assume a common pattern and pathology, oversimplifying the heterogeneity of clinical AD.

Impact of Deep Learning 3D CT Super-Resolution on AI-Based Pulmonary Nodule Characterization.

Tomography (Ann Arbor, Mich.)
BACKGROUND/OBJECTIVES: Correct pulmonary nodule volumetry and categorization is paramount for accurate diagnosis in lung cancer screening programs. CT scanners with slice thicknesses of multiple millimetres are still common worldwide, and slice thick...

A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study.

JMIR cancer
BACKGROUND: The rising number of cancer survivors and the shortage of health care professionals challenge the accessibility of cancer care. Health technologies are necessary for sustaining optimal patient journeys. To understand individuals' daily li...

Artificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation study.

BMC cancer
BACKGROUND: Accurate detection of hepatocellular carcinoma (HCC) in multiphasic contrast CT is essential for effective treatment and surgical planning. However, the variety of CT images, the misdiagnosis and missed diagnosis, and the inconsistent dia...