Empathizing, systemizing, empathizing-systemizing difference and their association with autistic traits in children with autism spectrum disorder, with and without intellectual disability.

Empathizing, systemizing, and empathizing-systemizing difference can be linked to autistic traits in the general adult population and those with autism spectrum disorder (ASD), but these profiles and associations remain unclear in children with ASD, with and without intellectual disability (ASD + ID; ASD-noID). We recruited three groups including 160 boys with ASD (73 ASD + ID; 87 ASD-noID) and 99 typically developing (TD) boys (6-12 years). We measured empathizing, systemizing, and empathizing-systemizing difference using the parent-reported child Empathy and Systemizing Quotient (EQ-C/SQ-C). We measured autistic traits using the Social Responsiveness Scale (SRS). Among the three groups, children with ASD + ID and ASD-noID scored lower on the EQ-C and SQ-C than TD children (all p < 0.001). There was no difference in the EQ-C between children with ASD + ID and ASD-noID (16.59 ± 5.53 vs. 16.23 ± 5.85, p = 0.973), and the difference in the SQ-C attenuated to null when adjusting for intelligence between children with ASD-noID and TD children (18.89 ± 7.80 vs. 24.15 ± 6.73, p = 0.089). Children with ASD + ID scored higher on empathizing-systemizing difference than TD children but lower than children with ASD-noID (all p < 0.05). Negative associations between EQ-C and all autistic traits, null associations between SQ-C and all autistic traits, and positive associations between empathizing-systemizing difference and all autistic traits were found in all groups. We observed differences in empathizing, systemizing, and empathizing-systemizing difference and the consistency of their associations with autistic traits among the three groups. Our findings provide implication that behavioral interventions of ASD should consider the balance of empathizing and systemizing. LAY SUMMARY: We examined the profiles of empathizing, systemizing, and empathizing-systemizing difference in children with autism spectrum disorder, with and without intellectual disability (ASD + ID; ASD-noID), and typically developing (TD) children aged 6-12 years. We observed differences in these profiles and the consistency of their associations with autistic traits among the three groups. Empathizing and empathizing-systemizing difference, rather than systemizing, were associated with autistic traits within the three groups. Our findings provide implication that behavioral interventions of ASD should consider these imbalance profiles.

The validity and reliability of the simplified Chinese version of the Social Communication Questionnaire.

This study aims to validate the simplified Chinese version of the Social Communication Questionnaire (SCQ) in children aged 2-12 years from both general and clinical populations. We recruited 819 Chinese children in this study, including 505 typically developing (TD) children, 202 children with autism spectrum disorder (ASD) and 112 children with non-ASD neurodevelopmental disorders. All the children's parents completed the simplified Chinese version of the SCQ and all children with ASD were additionally assessed for intelligence and the Childhood Autism Rating Scale to confirm their diagnosis. We have developed a 40-item, 4-factor structure of SCQ with two domains (social communication and social interaction; and restricted, repetitive, and stereotyped patterns of behavior), which showed adequate goodness of fit (comparative fit index [CFI] = 0.96, Tucker-Lewis index [TLI] = 0.95, standardized root mean squared residual [SRMR] = 0.07, root mean square error of approximation [RMSEA] = 0.05), with good internal consistency (Cronbach's alpha = 0.92). We have provided different cut-offs to distinguish ASD cases from TD children (11 for children under 4 years [sensitivity: 0.96, specificity: 0.95], 12 for children 4 years and above [sensitivity: 0.93, specificity: 0.98]) or children with other neurodevelopmental disorders (14 [sensitivity: 0.85, specificity: 0.88]). Through this large sample validation, we confirmed that the simplified Chinese version of the SCQ could be used for children aged 2-12 years with relatively good psychometric properties. LAY SUMMARY: We aimed to develop the simplified Chinese version of the Social Communication Questionnaire (SCQ) for Chinese children aged 2-12 years as a screening tool to identified potential risk of autism spectrum disorder (ASD). We have developed a 40-item, 4-factor structure of SCQ with two domains, which showed adequate goodness of fit and good psychometric properties. We also provided different cut-offs to identify ASD cases in general or clinical populations.

Automated pneumothorax triaging in chest X-rays in the New Zealand population using deep-learning algorithms.

INTRODUCTION: The primary aim was to develop convolutional neural network (CNN)-based artificial intelligence (AI) models for pneumothorax classification and segmentation for automated chest X-ray (CXR) triaging. A secondary aim was to perform interpretability analysis on the best-performing candidate model to determine whether the model's predictions were susceptible to bias or confounding. METHOD: A CANDID-PTX dataset, that included 19,237 anonymized and manually labelled CXRs, was used for training and testing candidate models for pneumothorax classification and segmentation. Evaluation metrics for classification performance included Area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity, whilst segmentation performance was measured using mean Dice and true-positive (TP)-Dice coefficients. Interpretability analysis was performed using Grad-CAM heatmaps. Finally, the best-performing model was implemented for a triage simulation. RESULTS: The best-performing model demonstrated a sensitivity of 0.93, specificity of 0.95 and AUC-ROC of 0.94 in identifying the presence of pneumothorax. A TP-Dice coefficient of 0.69 is given for segmentation performance. In triage simulation, mean reporting delay for pneumothorax-containing CXRs is reduced from 9.8 ± 2 days to 1.0 ± 0.5 days (P-value < 0.001 at 5% significance level), with sensitivity 0.95 and specificity of 0.95 given for the classification performance. Finally, interpretability analysis demonstrated models employed logic understandable to radiologists, with negligible bias or confounding in predictions. CONCLUSION: AI models can automate pneumothorax detection with clinically acceptable accuracy, and potentially reduce reporting delays for urgent findings when implemented as triaging tools.

Curation of the CANDID-PTX Dataset with Free-Text Reports.

Supplemental material is available for this article. Keywords: Conventional Radiography, Thorax, Trauma, Ribs, Catheters, Segmentation, Diagnosis, Classification, Supervised Learning, Machine Learning © RSNA, 2021.