Automatic Classification of Adult Males With and Without Autism Spectrum Disorder by Non-contact Measurement of Autonomic Nervous System Activation

A face image classification method of autistic children based on the two-phase transfer learning

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, which seriously affects children's normal life. Screening potential autistic children before professional diagnose is helpful to early detection and early intervention. Autistic children have some different facial features from non-autistic children, so the potential autistic children can be screened by taking children's facial images and analyzing them with a mobile phone. The area under curve (AUC) is a more robust metrics than accuracy in evaluating the performance of a model used to carry out the two-category classification, and the AUC of the deep learning model suitable for the mobile terminal in the existing research can be further improved. Moreover, the size of an input image is large, which is not fit for a mobile phone. A deep transfer learning method is proposed in this research, which can use images with smaller size and improve the AUC of existing studies. The proposed transfer method uses the two-phase transfer learning mode and the multi-classifier integration mode. For MobileNetV2 and MobileNetV3-Large that are suitable for a mobile phone, the two-phase transfer learning mode is used to improve their classification performance, and then the multi-classifier integration mode is used to integrate them to further improve the classification performance. A multi-classifier integrating calculation method is also proposed to calculate the final classification results according to the classifying results of the participating models. The experimental results show that compared with the one-phase transfer learning, the two-phase transfer learning can significantly improve the classification performance of MobileNetV2 and MobileNetV3-Large, and the classification performance of the integrated classifier is better than that of any participating classifiers. The accuracy of the integrated classifier in this research is 90.5%, and the AUC is 96.32%, which is 3.51% greater than the AUC (92.81%) of the previous studies.

Identifying activity level related movement features of children with ASD based on ADOS videos

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects about 2% of children. Due to the shortage of clinicians, there is an urgent demand for a convenient and effective tool based on regular videos to assess the symptom. Computer-aided technologies have become widely used in clinical diagnosis, simplifying the diagnosis process while saving time and standardizing the procedure. In this study, we proposed a computer vision-based motion trajectory detection approach assisted with machine learning techniques, facilitating an objective and effective way to extract participants' movement features (MFs) to identify and evaluate children's activity levels that correspond to clinicians' professional ratings. The designed technique includes two key parts: (1) Extracting MFs of participants' different body key points in various activities segmented from autism diagnostic observation schedule (ADOS) videos, and (2) Identifying the most relevant MFs through established correlations with existing data sets of participants' activity level scores evaluated by clinicians. The research investigated two types of MFs, i.e., pixel distance (PD) and instantaneous pixel velocity (IPV), three participants' body key points, i.e., neck, right wrist, and middle hip, and five activities, including Table-play, Birthday-party, Joint-attention, Balloon-play, and Bubble-play segmented from ADOS videos. Among different combinations, the high correlations with the activity level scores evaluated by the clinicians (greater than 0.6 with p < 0.001) were found in Table-play activity for both the PD-based MFs of all three studied key points and the IPV-based MFs of the right wrist key point. These MFs were identified as the most relevant ones that could be utilized as an auxiliary means for automating the evaluation of activity levels in the ASD assessment.

Metformin-Treatment Option for Social Impairment? An Open Clinical Trial to Elucidate the Effects of Metformin Treatment on Steroid Hormones and Social Behavior

BACKGROUND

Social behavior is mediated by steroid hormones, whereby various lines of evidence indicate that metformin might improve the symptoms of social withdrawal. This directly yields to the aim of the study to correlate the impact of metformin treatment on the potential alterations in steroid hormone homeostasis, which is ultimately impacting social behavior. Therefore, urinary samples of patients before and after treatment with metformin will be correlated to social behavior to elucidate potential changes in steroid hormone profiles and social behavior.

MATERIAL AND METHODS

An observational study in healthy adults with a new indication for metformin. Steroid hormone analysis, including the most prominent androgen, estrogen, progesterone, aldosterone, corticosterone, cortisone and cortisol metabolites analyzed with gas chromatography-mass spectrometry and a questionnaire on social behavior (Autism Spectrum Questionnaire (AQ)) will be administered prior to and after around a 12-week phase of metformin treatment.

DISCUSSION

It is likely that due to different pathophysiological mechanisms such as an effect on the respiratory chain in mitochondria or via AMP-activated protein kinase, a general alteration of steroid hormone levels can be detected prior to post treatment. The encompassing measurement of steroid hormones shall give hints concerning the involvement of specific cascades yielding potential pharmacological targets for future research.

An Edge Computing and Ambient Data Capture System for Clinical and Home Environments

The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware. We describe five applications of our ambient data capture system. Namely: (1) Estimating occupancy and human activity phenotyping; (2) Medical equipment alarm classification; (3) Geolocation of humans in a built environment; (4) Ambient light logging; and (5) Ambient temperature and humidity logging. We obtained an accuracy of 94% for estimating occupancy from video. We stress-tested the alarm note classification in the absence and presence of speech and obtained micro averaged F1 scores of 0.98 and 0.93, respectively. The geolocation tracking provided a room-level accuracy of 98.7%. The root mean square error in the temperature sensor validation task was 0.3°C and for the humidity sensor, it was 1% Relative Humidity. The low-cost edge computing system presented here demonstrated the ability to capture and analyze a wide range of activities in a privacy-preserving manner in clinical and home environments and is able to provide key insights into the healthcare practices and patient behaviors.

Efficacy and Safety of Stem Cell Therapy in Children With Autism Spectrum Disorders: A Systematic Review and Meta-Analysis

AIM

There is insufficient evidence regarding the efficacy and safety of stem cell therapy for autism spectrum disorders. We performed the first meta-analysis of stem cell therapy for autism spectrum disorders in children to provide evidence for clinical rehabilitation.

METHODS

The data source includes PubMed/Medline, Web of Science, EMBASE, Cochrane Library and China Academic Journal, from inception to 24th JULY 2021. After sifting through the literature, the Cochrane tool was applied to assess the risk of bias. Finally, we extracted data from these studies and calculated pooled efficacy and safety.

RESULTS

5 studies that met the inclusion criteria were included in current analysis. Meta-analysis was performed using rehabilitation therapy as the reference standard. Data showed that the Childhood Autism Rating Scale score of stem cell group was striking lower than the control group (WMD: -5.96; 95%CI [-8.87, -3.06]; p < 0.0001). The Clinical Global Impression score consolidated effect size RR = 1.01, 95%CI [0.87, 1.18], Z = 0.14 (p = 0.89), the effective rate for The Clinical Global Impression was 62% and 60% in the stem cell group and the control group, respectively. The occurrence events of adverse reactions in each group (RR = 1.55; 95%CI = 0.60 to 3.98; p = 0.36), there was no significant difference in the incidence of adverse reactions between the stem cell group and the control group.

CONCLUSIONS

The results of this meta-analysis suggested that stem cell therapy for children with autism might be safe and effective. However, the evidence was compromised by the limitations in current study size, lacking standardized injection routes and doses of stem cells, as well as shortages in diagnostic tools and long period follow-up studies. Hence, it calls for more studies to systematically confirm the efficacy and safety of stem cell therapy for children with autism spectrum disorders.