AIMC Topic: Female

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BrainLossNet: a fast, accurate and robust method to estimate brain volume loss from longitudinal MRI.

International journal of computer assisted radiology and surgery
PURPOSE: MRI-derived brain volume loss (BVL) is widely used as neurodegeneration marker. SIENA is state-of-the-art for BVL measurement, but limited by long computation time. Here we propose "BrainLossNet", a convolutional neural network (CNN)-based m...

Multimodal deep learning for dementia classification using text and audio.

Scientific reports
Dementia is a progressive neurological disorder that affects the daily lives of older adults, impacting their verbal communication and cognitive function. Early diagnosis is important to enhance the lifespan and quality of life for affected individua...

A deep-learning-based model for assessment of autoimmune hepatitis from histology: AI(H).

Virchows Archiv : an international journal of pathology
Histological assessment of autoimmune hepatitis (AIH) is challenging. As one of the possible results of these challenges, nonclassical features such as bile-duct injury stays understudied in AIH. We aim to develop a deep learning tool (artificial int...

EEG emotion recognition based on data-driven signal auto-segmentation and feature fusion.

Journal of affective disorders
Pattern recognition based on network connections has recently been applied to the brain-computer interface (BCI) research, offering new ideas for emotion recognition using Electroencephalogram (EEG) signal. However unified standards are currently lac...

Machine-learning clustering analysis identifies novel phenogroups in patients with ST-elevation acute myocardial infarction.

International journal of cardiology
BACKGROUND: Machine learning clustering of patients with ST-elevation acute myocardial infarction (STEMI) may provide important insights into their risk profile, management and prognosis.

Assessing inclusion and representativeness on digital platforms for health education: Evidence from YouTube.

Journal of biomedical informatics
BACKGROUND: Studies confirm that significant biases exist in online recommendation platforms, exacerbating pre-existing disparities and leading to less-than-optimal outcomes for underrepresented demographics. We study issues of bias in inclusion and ...

Patient stratification based on the risk of severe illness in emergency departments through collaborative machine learning models.

The American journal of emergency medicine
OBJECTIVES: Emergency department (ED) overcrowding presents a global challenge that inhibits prompt care for critically ill patients. Traditional 5-level triage system that heavily rely on the judgment of the triage staff could fail to detect subtle ...

Machine learning evaluation of immune infiltrate through digital tumour score allows prediction of survival outcome in a pooled analysis of three international stage III colon cancer cohorts.

EBioMedicine
BACKGROUND: T-cell immune infiltrates are robust prognostic variables in localised colon cancer. Evaluation of prognosis using artificial intelligence is an emerging field. We evaluated whether machine learning analysis improved prediction of patient...

Machine Learning-Based Critical Congenital Heart Disease Screening Using Dual-Site Pulse Oximetry Measurements.

Journal of the American Heart Association
BACKGROUND: Oxygen saturation (Spo) screening has not led to earlier detection of critical congenital heart disease (CCHD). Adding pulse oximetry features (ie, perfusion data and radiofemoral pulse delay) may improve CCHD detection, especially coarct...