AIMC Topic: Humans

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Artificial intelligence approaches to human-microbiome protein-protein interactions.

Current opinion in structural biology
Host-microbiome interactions play significant roles in human health and disease. Artificial intelligence approaches have been developed to better understand and predict the molecular interplay between the host and its microbiome. Here, we review rece...

Decision Support Systems in HF based on Deep Learning Technologies.

Current heart failure reports
PURPOSE OF REVIEW: Application of deep learning (DL) is growing in the last years, especially in the healthcare domain. This review presents the current state of DL techniques applied to electronic health record structured data, physiological signals...

GestaltMatcher facilitates rare disease matching using facial phenotype descriptors.

Nature genetics
Many monogenic disorders cause a characteristic facial morphology. Artificial intelligence can support physicians in recognizing these patterns by associating facial phenotypes with the underlying syndrome through training on thousands of patient pho...

Convolutional Neural Networks for Segmenting Cerebellar Fissures from Magnetic Resonance Imaging.

Sensors (Basel, Switzerland)
The human cerebellum plays an important role in coordination tasks. Diseases such as spinocerebellar ataxias tend to cause severe damage to the cerebellum, leading patients to a progressive loss of motor coordination. The detection of such damages ca...

An End-to-End Deep Learning Pipeline for Football Activity Recognition Based on Wearable Acceleration Sensors.

Sensors (Basel, Switzerland)
Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvant...

Machine learning-based diagnosis and risk factor analysis of cardiocerebrovascular disease based on KNHANES.

Scientific reports
The prevalence of cardiocerebrovascular disease (CVD) is continuously increasing, and it is the leading cause of human death. Since it is difficult for physicians to screen thousands of people, high-accuracy and interpretable methods need to be prese...

Machine learning-aided risk prediction for metabolic syndrome based on 3 years study.

Scientific reports
Metabolic syndrome (MetS) is a group of physiological states of metabolic disorders, which may increase the risk of diabetes, cardiovascular and other diseases. Therefore, it is of great significance to predict the onset of MetS and the corresponding...

Deep learning segmentation and quantification method for assessing epicardial adipose tissue in CT calcium score scans.

Scientific reports
Epicardial adipose tissue volume (EAT) has been linked to coronary artery disease and the risk of major adverse cardiac events. As manual quantification of EAT is time-consuming, requires specialized training, and is prone to human error, we develope...

A non-invasive method for concurrent detection of early-stage women-specific cancers.

Scientific reports
We integrated untargeted serum metabolomics using high-resolution mass spectrometry with data analysis using machine learning algorithms to accurately detect early stages of the women specific cancers of breast, endometrium, cervix, and ovary across ...