AIMC Topic: Biomarkers

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Establishment of a machine learning predictive model for non-alcoholic fatty liver disease: A longitudinal cohort study.

Nutrition, metabolism, and cardiovascular diseases : NMCD
BACKGROUND AND AIMS: Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver disease, which lacks effective drug treatments. This study aimed to construct an eXtreme Gradient Boosting (XGBoost) prediction model to identify or evaluate pot...

Surface-functionalized SERS platform for deep learning-assisted diagnosis of Alzheimer's disease.

Biosensors & bioelectronics
Early diagnosis of Alzheimer's disease is crucial to stall the deterioration of brain function, but conventional diagnostic methods require complicated analytical procedures or inflict acute pain on the patient. Then, label-free Surface-enhanced Rama...

Mitral Valve Segmentation and Tracking from Transthoracic Echocardiography Using Deep Learning.

Ultrasound in medicine & biology
OBJECTIVE: Valvular heart diseases (VHDs) pose a significant public health burden, and deciding the best treatment strategy necessitates accurate assessment of heart valve function. Transthoracic echocardiography (TTE) is the key modality to evaluate...

Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review.

Reviews in the neurosciences
Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides ...

Multi-omics analysis identifies potential microbial and metabolite diagnostic biomarkers of bacterial vaginosis.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Bacterial vaginosis (BV) is a common clinical manifestation of a perturbed vaginal ecology associated with adverse sexual and reproductive health outcomes if left untreated. The existing diagnostic modalities are either cumbersome or requ...

AI-generated CT body composition biomarkers associated with increased mortality risk in socioeconomically disadvantaged individuals.

Abdominal radiology (New York)
PURPOSE: To evaluate the relationship between socioeconomic disadvantage using national area deprivation index (ADI) and CT-based body composition measures derived from fully automated artificial intelligence (AI) tools to identify body composition m...

Enhancing foveal avascular zone analysis for Alzheimer's diagnosis with AI segmentation and machine learning using multiple radiomic features.

Scientific reports
We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machine learning classification using multiple features extracted from the foveal avascular zone (FAZ)-a retinal biomarker for Alzheimer's disease-to impro...

Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers.

Scientific reports
The COVID-19 influenza emerged and proved to be fatal, causing millions of deaths worldwide. Vaccines were eventually discovered, effectively preventing the severe symptoms caused by the disease. However, some of the population (elderly and patients ...