Early identification of autism spectrum disorder based on machine learning with eye-tracking data.

BACKGROUND: Early identification of autism spectrum disorder (ASD) improves long-term outcomes, yet significant diagnostic delays persist. METHODS: A retrospective cohort of 449 children (ASD: 246, typically developing [TD]: 203) was used for model development. Eye-movement data were collected from the participants watching videos that featured eye-tracking paradigms for assessing social and non-social cognition. Five machine learning algorithms, namely random forest, support vector machine, logistic regression, artificial neural network, and extreme gradient boosting, were trained to classify children with ASD and TD. The best-performing algorithm was selected to build the final model which was further evaluated in a prospective cohort of 80 children. The Shapley values interpreted important eye-tracking features. RESULTS: Random forest outperformed other algorithms during model development and achieved an area under the curve of 0.849 (< 3 years: 0.832, ≥ 3 years: 0.868) on the external validation set. Of the ten most important eye-tracking features, three measured social cognition, and the rest were related to non-social cognition. A deterioration in model performance was observed using only the social or non-social cognition-related eye-tracking features. LIMITATIONS: The sample size of this study, although larger than that of existing studies of ASD based on eye-tracking data, was still relatively small compared to the number of features. CONCLUSIONS: Machine learning models based on eye-tracking data have the potential to be cost- and time-efficient digital tools for the early identification of ASD. Eye-tracking phenotypes related to social and non-social cognition play an important role in distinguishing children with ASD from TD children.

Nomogram for the Estimation of Intestinal Necrosis Risk in Incarcerated Inguinal Hernia in Infants under 6 Months.

OBJECTIVE: This article develops a nomogram to estimate intestinal necrosis risk in the incarcerated inguinal hernia (IIH) in infants under 6 months. METHODS: A total of 273 infants who underwent an emergency operation due to IIH were investigated retrospectively. Univariate and multivariate logistic regression were used to analyze the relationship between variables and intestinal necrosis and construct a nomogram of intestinal necrosis. The discrimination and concordance of the model were verified by receiver operating characteristic (ROC) analysis and calibration curve, and the bootstrap method was used for internal validation of the model. The clinical applicability of the model was evaluated using the decision curve and the clinical impact curve. RESULTS: Intestinal necrosis was found in 37 of 273 infants (13.6%) in this study. The vomiting symptoms, platelet count, C-reactive protein, and neutrophil-lymphocyte ratio were independent risk factors for intestinal necrosis in IIH. We then constructed a nomogram with these four factors. ROC analysis showed that the nomogram had a good diagnostic performance, with the area under the curve (AUC), sensitivity, and specificity of 0.918 (95% confidence interval: 0.880-0.956), 97.3%, and 69.9%, respectively. The nomogram was further validated using 2,000-repetition internal bootstrap validation, and the values of AUC, sensitivity, and specificity were 0.899, 95.7%, and 50.5%, respectively. The decision curve and the clinical impact curve indicated that the predictive model has a favorable clinical application. CONCLUSION: The nomogram can be used to predict intestinal necrosis in IIH, and allow us to estimate the severity of IIH more accurately and arrange the treatment process more reasonably.

Developmental Language Differences in Children with Autism Spectrum Disorders and Possible Sex Difference.

Developmental difference is a common characteristic of autism spectrum disorder (ASD) with unclear sex differences. The current study included 610 children with ASD, aged between 2 and 7 years, with completed language profiles. We used a nonparametric item response theory model called Mokken scale analysis to examine the order of acquisition of developmental language milestones in children with ASD. Our results demonstrated the developmental language differences in the expressive and receptive language dimensions in children with ASD compared with typical developmental sequences. Furthermore, The acquisition of gestures and pragmatics was more impaired in the female subgroup than in the male subgroup. The identified developmental language sequence could help provide a more comprehensive ASD developmental profile.