AIMC Topic: Humans

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Achieving affective human-virtual agent communication by enabling virtual agents to imitate positive expressions.

Scientific reports
Affective communication, communicating with emotion, during face-to-face communication is critical for social interaction. Advances in artificial intelligence have made it essential to develop affective human-virtual agent communication. A person's b...

Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach.

European radiology experimental
BACKGROUND: To evaluate whether machine learning algorithms allow the prediction of Child-Pugh classification on clinical multiphase computed tomography (CT).

Role of Artificial Intelligence in Kidney Disease.

International journal of medical sciences
Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidne...

Topics and trends in artificial intelligence assisted human brain research.

PloS one
Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous gro...

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome.

PloS one
State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide clinical decision support systems to physicians. Modern ML approaches such as artificial neural networks (ANN...

Unveiling new disease, pathway, and gene associations via multi-scale neural network.

PloS one
Diseases involve complex modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, new biological knowledge about a disease can be extracted from these prof...

Predicting mental health problems in adolescence using machine learning techniques.

PloS one
BACKGROUND: Predicting which children will go on to develop mental health symptoms as adolescents is critical for early intervention and preventing future, severe negative outcomes. Although many aspects of a child's life, personality, and symptoms h...

Application of Generalized Split Linearized Bregman Iteration algorithm for Alzheimer's disease prediction.

Aging
In this paper, we applied a novel method for the detection of Alzheimer's disease (AD) based on a structural magnetic resonance imaging (sMRI) dataset. Specifically, the method involved a new classification algorithm of machine learning, named Genera...

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning.

Computational intelligence and neuroscience
Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagn...

Detection of hip osteoarthritis by using plain pelvic radiographs with deep learning methods.

Skeletal radiology
OBJECTIVE: The incidence of osteoarthritis is gradually increasing in public due to aging and increase in obesity. Various imaging methods are used in the diagnosis of hip osteoarthritis, and plain pelvic radiography is the first preferred imaging me...