ADHD/ADD

Binding the Person-Specific Approach to Modern AI in the Human Screenome Project: Moving past Generalizability to Transferability.

Advances in ability to comprehensively record individuals' digital lives and in AI modeling of those data facilitate new possibilities for describing, predicting, and generating a wide variety of behavioral processes. In this paper, we consider these advances from a person-specific perspective, including whether the pervasive concerns about of results might be productively reframed with respect t...

Virtual agents and risk-taking behavior in adolescence: the twofold nature of nudging.

Peer pressure can influence risk-taking behavior and it is particularly felt during adolescence. With artificial intelligence (AI) increasingly present in a range of everyday human contexts, including virtual environments, it is important to examine whether AI can have an impact on human's decision making processes and behavior. By using the balloon analogue risk task (BART) evaluating propensity ...

Stenosis Detection and Quantification of Coronary Artery Using Machine Learning and Deep Learning.

The aim of this review is to introduce some applications of artificial intelligence (AI) algorithms for the detection and quantification of coronary s...

Unveiling the benefits of multitasking in disentangled representation formation.

Johnston and Fusi recently investigated the emergence of disentangled representations when a neural network was trained to perform multiple simultaneo...

Detection of ADHD from EEG signals using new hybrid decomposition and deep learning techniques.

Attention deficit hyperactivity disorder (ADHD) is considered one of the most common psychiatric disorders in childhood. The incidence of this disease...

Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks.

BACKGROUND AND OBJECTIVES: Combining knowledge of clinical pathologists and deep learning models is a growing trend in morphological analysis of cells...

Unlock the algorithms: regulation of adaptive algorithms in reproduction.

In the USA, the Food and Drug Administration plans to regulate artificial intelligence and machine learning software systems as medical devices to imp...

Identification of attention deficit hyperactivity disorder with deep learning model.

This article explores the detection of Attention Deficit Hyperactivity Disorder, a neurobehavioral disorder, from electroencephalography signals. Due ...

Actionable artificial intelligence: Overcoming barriers to adoption of prediction tools.

Clinical prediction models based on artificial intelligence algorithms can potentially improve patient care, reduce errors, and add value to the healt...

Evaluating platelet concentrates by platelet indices, thromboelastography, and flow cytometry.

INTRODUCTION: Platelet transfusion has been therapeutically used in patients with thrombocytopenia and platelet function defects over the years. The u...

Facile and Scalable Synthesis of Metal- and Nitrogen-Doped Carbon Nanotubes for Efficient Electrochemical CO Reduction.

Metal- and nitrogen-doped carbon (M-N-C) is a promising material to catalyze electrochemical CO reduction reaction (CORR). However, most M-N-C catalys...

Opportunistic Extraction of Quantitative CT Biomarkers: Turning the Incidental Into Prognostic Information.

The past two decades have seen a significant increase in the use of CT, with a corresponding rise in the mean population radiation dose. This rise in ...

A convolutional attention mapping deep neural network for classification and localization of cardiomegaly on chest X-rays.

Building a reliable and precise model for disease classification and identifying abnormal sites can provide physicians assistance in their decision-ma...

Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Autoencoder.

Intrinsically disordered proteins (IDPs) account for more than 50% of the human proteome and are closely associated with tumors, cardiovascular diseas...

Attention Deficit Hyperactivity Disorder Classification Based on Deep Learning.

Attention Deficit Hyperactivity Disorder (ADHD) is a type of mental health disorder that can be seen from children to adults and affects patients' nor...

Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes.

BACKGROUND: Quantification of chamber size and systolic function is a fundamental component of cardiac imaging. However, the human heart is a complex ...

The promise and peril of interactive embodied agents for studying non-verbal communication: a machine learning perspective.

In face-to-face interactions, parties rapidly react and adapt to each other's words, movements and expressions. Any science of face-to-face interactio...

Explainable deep learning-based clinical decision support engine for MRI-based automated diagnosis of temporomandibular joint anterior disk displacement.

BACKGROUND AND OBJECTIVE: MRI is considered the gold standard for diagnosing anterior disc displacement (ADD), the most common temporomandibular joint...

Developing an Implementation Model for ADHD Intervention in Community Clinics: Leveraging Artificial Intelligence and Digital Technology.

Implementation of behavior therapy for ADHD faces challenges in community settings. We describe development of a community-based implementation model ...

NLS: An accurate and yet easy-to-interpret prediction method.

Over the last years, the predictive power of supervised machine learning (ML) has undergone impressive advances, achieving the status of state of the ...