Artificial intelligence evaluation of COVID-19 restrictions and speech therapy effects on the autistic children's behavior.

This study looked at how COVID-19 lockdowns and different types of speech therapy affected 87 autistic children's behavior. Parents filled out questionnaires about their children's behavior before and during lockdown. The research found that lockdowns reduced acting-out behaviors but increased anxiety and depression-like problems. When comparing different therapy options during lockdown, face-to-face speech therapy worked better than online therapy or public services for helping children cope with restrictions.

This study examined the effects of COVID-19 restrictions and different speech therapy approaches on 87 autistic children using the Child Behavior Checklist (CBCL) and artificial intelligence analysis. Researchers compared four conditions: in-person therapy, telehealth, public services, and no treatment during lockdown. Parents/caregivers reported on internalizing problems (anxiety, depression) and externalizing problems (aggression, rule-breaking behavior) before and after lockdown. Results showed that COVID-19 restrictions reduced externalizing problems but increased internalizing problems.

Among treatment approaches, in-person speech therapy was most effective for managing behavioral challenges during stay-at-home periods. The study used neuro-fuzzy artificial intelligence methods to analyze relationships between treatment type, duration, and behavioral outcomes.

Clinicians should monitor for increased internalizing symptoms during environmental disruptions. In-person therapy delivery may be preferable when possible. Parents and therapists should be aware that reduced external stimulation may decrease some behavioral challenges while potentially increasing anxiety and mood-related concerns in autistic children.

Small sample size (87 participants), reliance on parent reports only, unclear study design and methodology details. The artificial intelligence analysis approach is novel but validation unclear. No control group comparison or randomization mentioned.

In the present study, we aimed to quantify the effects of COVID-19 restrictions and speech treatment approaches during lockdowns on autistic children using CBCL and neuro-fuzzy artificial intelligence method. In this regard, a survey including CBCL questionnaire is prepared using online forms. In total, 87 children with diagnosed Autism spectrum disorders (ASD) participated in the survey. The influences of three treatment approaches of in-person, telehealth and public services along with no-treatment condition during lockdown were the main factors of the investigation.

The main output factors were internalized and externalized problems in general and their eight subcategory syndromes. We examined the reports by parents/caregivers to find correlation between treatments and CBCL listed problems. Moreover, comparison of the eight syndromes rating scores from pre-lockdown to post-lockdown periods were performed. In addition, artificial intelligence method were engaged to find the influence of speech treatment during restrictions on the level of internalizing and externalizing problems.

In this regard, a fully connected adaptive neuro fuzzy inference system is employed with type and duration of treatments as input and T-scores of the syndromes are the output of the network. The results indicate that restrictions alleviate externalizing problems while intensifying internalizing problems. In addition, it is concluded that in-person speech therapy is the most effective and satisfactory approach to deal with ASD children during stay-at-home periods.