AIMC Topic: Humans

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A Model of Normality Inspired Deep Learning Framework for Depression Relapse Prediction Using Audiovisual Data.

Computer methods and programs in biomedicine
BACKGROUND: Depression (Major Depressive Disorder) is one of the most common mental illnesses. According to the World Health Organization, more than 300 million people in the world are affected. A first depressive episode can be solved by a spontaneo...

Application of artificial intelligence and machine learning technology for the prediction of postmortem interval: A systematic review of preclinical and clinical studies.

Forensic science international
BACKGROUND /PURPOSE: Establishing an accurate postmortem interval (PMI) is exceptionally crucial in forensic investigation. Artificial intelligence (AI) and Machine learning (ML) models are widely employed in forensic practice. ML is a part of AI, bo...

Robot-Assisted Versus Laparoscopic Approach for Splenectomy in Children: Systematic Review and Meta-Analysis.

Journal of laparoendoscopic & advanced surgical techniques. Part A
To compare the outcomes of pediatric splenectomies for hematologic diseases performed by robot-assisted laparoscopic surgery (RALS) and laparoscopic approach. Web of Science, Scopus, and PubMed databases were systematically searched for publication...

The potential scope of a humanoid robot in anatomy education: a review of a unique proposal.

Surgical and radiologic anatomy : SRA
INTRODUCTION: Applications based on artificial intelligence and machine learning are becoming more popular in teaching learning. Advanced technologies have facilitated robots to carry out various human-like functions, which have navigated the interes...

Present status and future directions: Imaging techniques for the detection of periapical lesions.

International endodontic journal
Diagnosing and treating apical periodontitis (AP) in an attempt to preserve the natural dentition, and to prevent the direct and indirect systemic effects of this condition, is the major goal in endodontics. Considering that AP is frequently asymptom...

Fast Temporal Graph Convolutional Model for Skeleton-Based Action Recognition.

Sensors (Basel, Switzerland)
Human action recognition has a wide range of applications, including Ambient Intelligence systems and user assistance. Starting from the recognized actions performed by the user, a better human-computer interaction can be achieved, and improved assis...

Automatic Segmentation of Periodontal Tissue Ultrasound Images with Artificial Intelligence: A Novel Method for Improving Dataset Quality.

Sensors (Basel, Switzerland)
UNLABELLED: This research aimed to evaluate Mask R-CNN and U-Net convolutional neural network models for pixel-level classification in order to perform the automatic segmentation of bi-dimensional images of US dental arches, identifying anatomical el...

Characterizing Subjects Exposed to Humidifier Disinfectants Using Computed-Tomography-Based Latent Traits: A Deep Learning Approach.

International journal of environmental research and public health
Around nine million people have been exposed to toxic humidifier disinfectants (HDs) in Korea. HD exposure may lead to HD-associated lung injuries (HDLI). However, many people who have claimed that they experienced HD exposure were not diagnosed with...

Enhancing the quality of cognitive behavioral therapy in community mental health through artificial intelligence generated fidelity feedback (Project AFFECT): a study protocol.

BMC health services research
BACKGROUND: Each year, millions of Americans receive evidence-based psychotherapies (EBPs) like cognitive behavioral therapy (CBT) for the treatment of mental and behavioral health problems. Yet, at present, there is no scalable method for evaluating...

Predicting demographics from meibography using deep learning.

Scientific reports
This study introduces a deep learning approach to predicting demographic features from meibography images. A total of 689 meibography images with corresponding subject demographic data were used to develop a deep learning model for predicting gland m...