Figure 1 - uploaded by Ayse Rodopman Arman
Content may be subject to copyright.
Source publication
ABSTRACT
Emotion recognition behavior and performance may vary between people with major
neurodevelopmental disorders such as Autism Spectrum Disorder (ASD), Attention
Deficit Hyperactivity Disorder (ADHD) and control groups. It is crucial to identify these
differences for early diagnosis and individual treatment purposes. This study represents
a m...
Contexts in source publication
Context 1
... participants were shown two different scenes in the experimental phase of the TrackEmo. As seen in Figure 1a the first one is the empty scene, which represents the transition between choices scene and the next image scene. The second one is a human face scene (Figure 1b). ...
Context 2
... seen in Figure 1a the first one is the empty scene, which represents the transition between choices scene and the next image scene. The second one is a human face scene (Figure 1b). The emotive (one of these emotions; angry, fear, happy, neutral and sad) face images are shown in this scene. ...
Similar publications
Autism Spectrum Disorder (ASD) is a developmental disorder characterized by difficulties in social interaction, communication, and restricted or repetitive patterns of thought and behaviour. Diagnosing ASD is important since it is a life long condition and early diagnosis of ASD has a great deal of importance in terms of controlling the disease. Th...
Citations
... Among brain-based studies, three studies used electroencephalography (EEG) (Gross et al., 2012;Shephard et al., 2019;Tye et al., 2014), one functional magnetic resonance imaging (fMRI) (Vandewouw et al., 2020), and one magnetoencephalography (MEG) (Safar et al., 2022) to assess neural activity in response to emotional faces in children with and without ASD and ADHD. Among eye-tracking studies, one applied the eye-tracker to measure gaze fixation and cognitive vergence responses to the eye regions on the faces used as stimuli (Bustos-Valenzuela et al., 2022), and one performed a multimodal classification with the noisy eye tracker in order to detect the diagnosis of the participants (Ozturk et al., 2018). ...
... Within each section (behavioral, and brain-based and eyetracking studies), the demands of the tasks, the stimuli used, and the emotions considered will be summarized, along with the main results obtained. Three of the studies that used brain-based (Gross et al., 2012) and eye-tracking (Bustos-Valenzuela et al., 2022;Ozturk et al., 2018) techniques also reported behavioral findings (accuracy and/or RTs) which will be included as well in the "Behavioral Studies" section. Finally, nine behavioral, brain-based, and eye-tracking studies considered the role of cognitive functioning on ER, 3 age-related differences in ER, 6 ER as a transdiagnostic endophenotype, and 7 the role of comorbidities; thus, findings will be described altogether at the end of the "Results" section. ...
... Among behavioral findings, overall differences between the ASD and ADHD groups' performance were not statistically significant (Bühler et al., 2011;Dyck et al., 2001;Economides et al., 2020;Fine et al., 2008;Greco et al., 2021;Lee et al., 2018;Löytömäki et al., 2020), but both clinical groups performed worse than ND participants, when the control group was present. The most common statistically significant differences between the groups emerged for ASD and ND, with ASD < ND (Berggren et al., 2016;Dyck et al., 2001;Economides et al., 2020;Fine et al., 2008;Greco et al., 2021;Löytömäki et al., 2020), and for ADHD and ND, with ADHD < ND (Bustos-Valenzuela et al., 2022;Dyck et al., 2001;Economides et al., 2020;Greco et al., 2021;Löytömäki et al., 2020;Ozturk et al., 2018;Sinzig et al., 2008). It was only in 4 studies that the ASD sample fared significantly worse than the ADHD sample (Demopoulos et al., 2013;Downs & Smith, 2004;Flores-Buils & Andrés-Roqueta, 2022;Semrud-Clikeman et al., 2010). ...
Children and adolescents with autism spectrum disorder (ASD) and attention deficit and hyperactivity disorder (ADHD) show difficulties in recognizing emotions. Similarities and differences between these two clinical groups’ emotion recognition (ER) have been little explored. This systematic review aims to summarize the results of comparative studies that included samples of cases with ASD and ADHD. A systematic search was conducted following PRISMA guidelines, and 24 papers were included. Behavioral, brain-based, and eye-tracking studies were considered, paying particular attention to the different methods used and to the characteristics of the study groups, such as cognitive factors, age-related differences, and comorbidities. This review provides some insight on the complex process of ER in ASD and ADHD, highlighting important directions for future research.
... In this, (Jiang et al., 2019) utilized RF to differentiate autistic controls based on eye fixations. In (Ozturk et al., 2018) individuals with ASD, ADHD, and typical control viewed two different scenes, such as an empty scene and a human face scene, presented by a web application called TrackEmo. As classifiers, Random Forest was used and achieved 86.36% for ASD/control. ...
Eye tracking is a promising tool for Autism Spectrum Disorder (ASD) detection in both children and adults. An important aspect of social communication is keeping eye contact, which is something that people with ASD frequently struggle with. Eye tracking can assess the duration of eye contact and the frequency and direction of gaze movements, offering quantifiable indicators of social communication deficits. People with ASD may also demonstrate other abnormalities in visual processing, such as an increased concentration on detail, sensory sensitivity, and trouble with complicated visual activities. These variations can be measured via Eye tracking, which offers critical information for the planning of therapy and diagnosis. The primary objective of this work is to provide a thorough description of the most recent studies that use Eye tracking combined with various Machine Learning (ML) and Deep Learning (DL) models for the detection of ASD. This will provide insights into the identification, and behavioral assessment, and distinguish between autistic people and those who are Typically Developing (TD). A detailed review of the various ML and DL models with their datasets and performance criteria is presented. Different types of eye movement datasets with diagnostic standards and eye tracker devices are also discussed. Finally, the study addresses the potential of gaze prediction in ASD patients for the design of interventions.
... The system is mainly composed of four modules, which are data preprocessing module, data integration module [17,18], correlation algorithm implementation module, and external query module. ...
In this Internet era, people use more and more network data, and hospital diagnosis is even more important. The improvement of medical standards has brought about the rapid growth of medical data. How to analyze related symptoms from a large amount of data performing an analysis and accurately diagnosing its symptoms has become one of the difficult problems. This article is aimed at studying the role of medical huge amount of data and artificial intelligence in the diagnosis of knee osteoarthritis combined with cardiovascular and cerebrovascular diseases. To this end, this article proposes to improve data mining methods and proposes a comprehensive analysis of patient data in the field of artificial intelligence for intelligent diagnosis and treatment so that doctors can make accurate judgments about the symptoms of patients and designed related experiments to explore its specific effects. The experimental results in this article show that the improved data mining speed has increased by 17%, the data integrity has increased by 31%, and the proportion of valid data has also increased by 23%, which is very effective for clinical diagnosis.
... Saenz et al. [155] analyzed the brain activity patterns of ASD, ADHD and TD individuals using fMRI data and found that brain activation patterns were different in each of the groups. The research work reported in [156] was based on multimodal data including eye tracker and application log data. A total of 34 participants considered in the research work, among them 12 were ASD individuals, 12 with ADHD and 10 healthy controls. ...
In new era of emerging technologies, diagnosing the neurodevelopmental disorders at the primitive stage has led to a rise in the development of automated diagnostic systems. Socio-behavioral disorder (SBD),a subtype of NDDs, being amongst the most complex behavioral impairments is the root cause for disability, specifically in young children. Under the term SBD, falls a group of heterogeneous disorders; Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) whose diagnosis remains a challenging task due to the spectrum of conditions associated to it and also due to their high comorbidity with each other. Traditional based recognition of SBD is based on qualitative parameters which make diagnosis more time consuming. Hence, diagnosis of SBD is a preeminent area that demands deliberations from the researchers, scientists as well as academicians to introduce more automated diagnostic systems based on quantitative parameters that not only provides reliability and accuracy besides affordable for everyone. Motivated by these facts,this article seeks to provide a thorough investigation of efforts on the subject of automated diagnostic systems using artificial intelligence techniques based on quantitative parameters (biomarkers and multimodal data) for early ASD or ADHD detection. An extensive survey of AI based diagnostic systems for ASD, ADHD are also presented. Furthermore, the studies of various automated AI based diagnostic systems for the detection of ASD, ADHD and ASD+ADHD are presented in this research work. Finally, this article highlights the open issues existing in the literature that needs to be explored further for providing more effective SBD analysis and also presented an outline of the proposed work which will be an aid in the diagnostic field of ASD, ADHD and comorbid ASD.
Purpose
With the increasing prevalence of autism spectrum disorders (ASD), the importance of early screening and diagnosis has been subject to considerable discussion. Given the subtle differences between ASD children and typically developing children during the early stages of development, it is imperative to investigate the utilization of automatic recognition methods powered by artificial intelligence. We aim to summarize the research work on this topic and sort out the markers that can be used for identification.
Methods
We searched the papers published in the Web of Science, PubMed, Scopus, Medline, SpringerLink, Wiley Online Library, and EBSCO databases from 1st January 2013 to 13th November 2023, and 43 articles were included.
Results
These articles mainly divided recognition markers into five categories: gaze behaviors, facial expressions, motor movements, voice features, and task performance. Based on the above markers, the accuracy of artificial intelligence screening ranged from 62.13 to 100%, the sensitivity ranged from 69.67 to 100%, the specificity ranged from 54 to 100%.
Conclusion
Therefore, artificial intelligence recognition holds promise as a tool for identifying children with ASD. However, it still needs to continually enhance the screening model and improve accuracy through multimodal screening, thereby facilitating timely intervention and treatment.
