This study aimed to investigate impairments in social cognition in youth with specific learning disorder (SLD) through a cross sectional study. Eighty six adolescents which include of 43 SLD and 43 typically developing (TD) children completed a battery of tests to analyze social cognition, emotional process and clinical psychopathological profile. SLD group performed significantly worse than healthy controls in facial ER total accuracy score (Cohen = .77) and Stroop interference (Cohen = .92). In individual emotion analyses, patients with SLD have a very high deficiency in recognition of angry faces (Cohen = .89). Between-group difference was also significant for Stroop congruent and facilitation scores (Cohen = .99). The Specific Learning Disorder Symptom Check List-Parent Form scores were significant -and only- predictor of the model which for total accuracy score of facial recognition. The results of this study supported an impairment in emotion recognition and executive functions in adolescents with SLD but causality seems still unclear.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1080/21622965.2022.2156290 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Automatic Segmentation of Vestibular Schwannoma From MRI Using Two Cascaded Deep Learning Networks.
Laryngoscope
January 2025
Department of Otorhinolaryngology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Objective: Automatic segmentation and detection of vestibular schwannoma (VS) in MRI by deep learning is an upcoming topic. However, deep learning faces generalization challenges due to tumor variability even though measurements and segmentation of VS are essential for growth monitoring and treatment planning. Therefore, we introduce a novel model combining two Convolutional Neural Network (CNN) models for the detection of VS by deep learning aiming to improve performance of automatic segmentation.
View Article and Find Full Text PDFMachine Learning for Reaction Performance Prediction in Allylic Substitution Enhanced by Automatic Extraction of a Substrate-Aware Descriptor.
J Chem Inf Model
January 2025
Department of Computer Science and Engineering, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
Despite remarkable advancements in the organic synthesis field facilitated by the use of machine learning (ML) techniques, the prediction of reaction outcomes, including yield estimation, catalyst optimization, and mechanism identification, continues to pose a significant challenge. This challenge arises primarily from the lack of appropriate descriptors capable of retaining crucial molecular information for accurate prediction while also ensuring computational efficiency. This study presents a successful application of ML for predicting the performance of Ir-catalyzed allylic substitution reactions.
View Article and Find Full Text PDFUtilization of artificial intelligence in the diagnosis of pes planus and pes cavus with a smartphone camera.
World J Orthop
December 2024
Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA 02114, United States.
Background: Pes planus (flatfoot) and pes cavus (high arch foot) are common foot deformities, often requiring clinical and radiographic assessment for diagnosis and potential subsequent management. Traditional diagnostic methods, while effective, pose limitations such as cost, radiation exposure, and accessibility, particularly in underserved areas.
Aim: To develop deep learning algorithms that detect and classify such deformities using smartphone cameras.
Alleviating the medical strain: a triage method via cross-domain text classification.
Front Comput Neurosci
December 2024
Department of Radiology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China.
It is a universal phenomenon for patients who do not know which clinical department to register in large general hospitals. Although triage nurses can help patients, due to the larger number of patients, they have to stand in a queue for minutes to consult. Recently, there have already been some efforts to devote deep-learning techniques or pre-trained language models (PLMs) to triage recommendations.
View Article and Find Full Text PDFBiomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explainable artificial intelligence.
Front Mol Biosci
December 2024
Department of Teacher Education, NLA University College, Oslo, Norway.
Background: Breast cancer (BC) is a significant cause of morbidity and mortality in women. Although the important role of metabolism in the molecular pathogenesis of BC is known, there is still a need for robust metabolomic biomarkers and predictive models that will enable the detection and prognosis of BC. This study aims to identify targeted metabolomic biomarker candidates based on explainable artificial intelligence (XAI) for the specific detection of BC.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!