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Early and objective autism spectrum disorder (ASD) assessment, as well as early intervention are particularly important and may have long term benefits in the lives of ASD people. ASD assessment relies on subjective rather on objective criteria, whereas advances in research point to up-to-date procedures for early ASD assessment comprising eye-tracking technology, machine learning, as well as other assessment tools. This systematic review, the first to our knowledge of its kind, provides a comprehensive discussion of 30 studies irrespective of the stimuli/tasks and dataset used, the algorithms applied, the eye-tracking tools utilised and their goals. Evidence indicates that the combination of machine learning and eye-tracking technology could be considered a promising tool in autism research regarding early and objective diagnosis. Limitations and suggestions for future research are also presented.

Background:Sjögren’s Disease (SjD) is a chronic systemic autoimmune disease targeting the salivary gland (SG) and lacrimal gland (LG). Ultrasound(US)is a noninvasive and repeatable imaging technique which has been used to evaluate the major salivary glands in SjD. In recent years several US scoring systems for the SG have been applied to assist in the classification of SjD[1]. However, studies on lacrimal gland US of SjD patients are still limited.Objectives:To assess the interobserver reliability of US of LG in patients clinically suspected of SjD.Methods:Ten consecutive outpatients with clinically suspected SjD underwent US evaluation of both LGs. US images and videos for each LG were assessed and scored by 4 independent observers using the Hocevar and OMERACT scoring systems. If no lacrimal gland was judged as visible in either still images or video files by at least 3 scorers, the gland was not included for further analysis. For patients with an invisible gland, we only considered the score of single side LG when performing the analysis. The Hocevar scoring system [2] includes echogenicity (compared to the surrounding tissue), parenchymal homogeneity, presence of hypoechogenic areas, hyperechogenic reflections, and clearness of posterior glandular border (total score range 0-26). The OMERACT scoring system [3] evaluates semi-quantitively greyscale and color Doppler (both scores range from 0-3 per eye). The interobserver reliability was calculated using intraclass correlation coefficient (ICC). ICC values were interpreted as follows: 0.00-0.20: poor;0.20-0.40: fair;0.40-0.60: moderate; 0.60-0.80: good; and 0.80-1.00: excellent)Results:Two LG from 2 patients were excluded from analysis, following consensus on difficulty identifying the gland. Eighteen lacrimal glands from ten patients were finally included. Examples of high-scoring and low-scoring LG US are provided (Figure 1). The ICC of the total Hocevar score of each single gland was fair, with an ICC of 0.52, ICC of each patient (both LGs) was good with an ICC of 0.70. The ICC of the greyscale OMERACT score of each gland was good with a value of 0.62 and the total score of each patient was also good, with an ICC of 0.71. Finally, the color Doppler OMERACT score ICC of each gland is moderate, calculated at 0.58 and the total score of each patient is good, generating an ICC of 0.72.Conclusion:Our preliminary study shows that both greyscale and color Doppler US of LG are reliable imaging techniques for patients with clinically suspected SjD, using all three scoring methods. Interobserver reliability for the total score of both LGs was better than that for unilateral LGs. For further study, intraobserver reliability will be calculated, more patients will be included in the analysis and the specificity and sensitivity of LG ultrasonography in term of classification patients with SjD will be investigated, as well as the comparison with ACR EULAR criteria, salivary gland US and clinical characteristics.REFERENCES:[1] K. Delli et al., “Ultrasound of the Major Salivary Glands is a Reliable Imaging Technique in Patients with Clinically Suspected Primary Sjögren’s Syndrome,” Ultraschall in der Medizin - European Journal of Ultrasound, vol. 39, no. 03, pp. 328–333, Jun. 2018, doi: 10.1055/s-0043-104631.[2] A. Hocevar, A. Ambrozic, B. Rozman, T. Kveder, and M. Tomsic, “Ultrasonographic changes of major salivary glands in primary Sjogren’s syndrome. Diagnostic value of a novel scoring system.,” Rheumatology (Oxford), vol. 44, no. 6, pp. 768–72, Jun. 2005, doi: 10.1093/rheumatology/keh588.[3] S. Jousse-Joulin et al., “Video clip assessment of a salivary gland ultrasound scoring system in Sjögren’s syndrome using consensual definitions: an OMERACT ultrasound working group reliability exercise.,” Ann Rheum Dis, vol. 78, no. 7, pp. 967–973, 2019, doi: 10.1136/annrheumdis-2019-215024.Acknowledgements:NIL.Disclosure of Interests:None declared.

Employment appears to be one of the greatest problems individuals with ASD have to deal with during their transition to adult life. In particular, unemployment or underemployment appears to be common among them, which suggests a gap in employment theory and practice focusing on the needs of this population. Tech-aided interventions appear to be promising since they can provide them opportunities to access competitive employment. The purpose of the current article is to examine the use of technology in interventions for adolescents and young adults with ASD in school, home, and community settings. In particular, it focused on the users of technology, the goals addressed, the type of technology employed, the contexts in which intervention practices were employed, and the outcomes for adolescents and young adults with ASD. In most of the studies, positive results were recorded and the importance of the work-related social skills was underlined. Technology appears to show potential for the enhancement of vocational skills of adolescents and young adults with ASD. Future research should focus on the improvement of work-related social skills and the skills needed for successful job seeking and an interview process. The maintenance and the generalization of the acquired skills should be examined too.

Background: The debate and research over autism spectrum disorders (ASD) encounter a theoretical and interpretive impasse that reflects our inability to provide a coherent definition. This challenge is confirmed by the current description of the condition as a spectrum, and its management stretches the limits of the various relevant fields of knowledge and research, including medicine, psychology, language and communication, education, sociology, human rights, ethics and legal issues, philosophy. Objectives: In the diagnostic and therapeutic approach to persons with physical disabilities, technology has been tool. In the case of ASD, however, although some lines of research are focused on the study of sensory defects, the cause appears to lie, not only in perception, but also in interpretation of stimuli from the outside world. Since we entered the “digital era”, the use of technology as an assistive tool in interpretation of the surrounding world appears to provide a borderline between our knowledge and the dark area of our ignorance. For tackling this, an interdisciplinary approach is required. Which kind of assistive technology (AT) should be employed in the case of ASD, which leads us to an encounter with the theoretical and institutional void that the stormy pace of the digital transformation and evolution has created. Methods: Searching was through PubMed, National Institute of Health (NIH) publications, the official websites of European Union , Autism Europe, resources were found in the library of the University of Macedonia. Ninety eight papers were identified through the literature review in the period 2000-2021 Results: A features of postmodern society that is taking shape under the influence of the digital technology could be the crossing from the physical reality into the virtual realm. Another feature might be the reference to symbolic language that characterizes the various different fields of knowledge, together with their protocols and communication jargon. This entails the creation of hybrid knowledge which is expanding our physical world, and which makes possible intercommunication between isolated disciplinary fields. People with ASD feel more at ease when dealing with digital entities than in interaction with other people. Also individuals who are involved in the digital realm for long periods present characteristics similar to those of ASD. Could the creation of a virtual realm be possible, which would act as a common locus between the so-called normal and people with ASD. Conclusion: We conducted a preliminary study to explore the possibility of an interdisciplinary research program with the participation of experts from the various fields involved in the many aspects of ASD.

This study provides a comprehensive analysis of the evolution of Autism Spectrum Disorder (ASD) diagnostics, tracing its progression from psychoanalytic origins to the integration of advanced artificial intelligence (AI) technologies. The study explores, through scientific data bases like Pub Med, Scopus, and Google Scholar, how theoretical frameworks, including psychoanalysis, behavioral psychology, cognitive development, and neurobiological paradigms, have shaped diagnostic methodologies over time. Each paradigm’s associated assessment tools, such as the Autism Diagnostic Observation Schedule (ADOS) and the Vineland Adaptive Behavior Scales, are discussed in relation to their scientific advancements and limitations. Emerging technologies, particularly AI, are highlighted for their transformative impact on ASD diagnostics. The application of AI in areas such as video analysis, Natural Language Processing (NLP), and biodata integration demonstrates significant progress in precision, accessibility, and inclusivity. Ethical considerations, including algorithmic transparency, data security, and inclusivity for underrepresented populations, are critically examined alongside the challenges of scalability and equitable implementation. Additionally, neurodiversity- informed approaches are emphasized for their role in reframing autism as a natural variation of human cognition and behavior, advocating for strength-based, inclusive diagnostic frameworks. This synthesis underscores the interplay between evolving theoretical models, technological advancements, and the growing focus on compassionate, equitable diagnostic practices. It concludes by advocating for continued innovation, interdisciplinary collaboration, and ethical oversight to further refine ASD diagnostics and improve outcomes for individuals across the autism spectrum.

Communication deficits are one of the core symptoms of autism spectrum disorder (ASD), and augmentative and alternative communication (AAC) systems are utilized to facilitate the communication and language development of children with ASD. This review examines the research literature on the use of aided and unaided AAC systems in interventions for children with ASD, and investigates their effectiveness in enhancing language and communication skills in this population. Systematic review methodology was used to limit bias in the search of electronic databases, and relevant studies were selected, 20 of which met the inclusion criteria for the review. The findings of these studies indicate that AAC systems are able to facilitate and enhance communication skills in children with ASD. It is apparent that this is a method that will be used increasingly in the future, and it is imperative that meticulous research is conducted on the effects of the applications. Refinements in the study methodology are recommended, and additional questions that might be addressed in future research are discussed.

BackgroundSalivary gland ultrasound (SGUS) is cheap, non-invasive and easy to perform in an outpatient setting. The ACR-EULAR criteria were recently developed to reach international consensus regarding the classification of primary Sjögren’s syndrome (pSS), but SGUS is not yet included as a classification item.ObjectivesTo assess the performance of the ACR-EULAR criteria when salivary gland ultrasound (SGUS) replaces current items, in a large cohort of patients clinically suspected or diagnosed with pSS in daily clinical practice.MethodsIncluded were all consecutive outpatients who underwent SGUS between October 2014 and , July 2017 and had a complete data-set on all ACR-EULAR items. Classification according to the criteria was determined separately in patients who were subjected to a labial or parotid gland biopsy. For SGUS, the average score for hypoechogenic areas in the parotid and submandibular glands on one side was applied (range 0–3)1. The optimal cut-off value for our SGUS score was determined using ROC analysis. Clinical diagnosis by the treating physician was used as gold standard. Area under the curve (AUC), absolute agreement, sensitivity and specificity of the original and adjusted ACR-EULAR criteria sets were determined.ResultsOf the 363 consecutive patients, 243 patients had a complete data-set, of whom 147 patients were diagnosed with pSS. Accuracy of SGUS to predict clinical diagnosis was good, with an AUC of 0.860 and optimal cut-off value of ≥1.5. When applying a weight of 1 point for a positive SGUS, the cut-off value of the ACR-EULAR criteria to discriminate between pSS and non-pSS remained 4, irrespective of the type of biopsy used.In patients who underwent a labial gland biopsy (n=124), the original ACR-EULAR criteria showed an AUC of 0.965 (figure 1A). Absolute agreement with clinical diagnosis was 94.4%, sensitivity was 95.9% and specificity was 92.2%. When SGUS replaced the labial gland biopsy, absolute agreement was 87.9%, sensitivity was 82.2% and specificity was 96.1%. When SGUS replaced anti-SSA antibody status, absolute agreement was 89.5%, sensitivity was 86.3% and specificity was 94.1%. When SGUS replaced the ocular staining score (OSS), Schirmer’s test or unstimulated whole saliva flow (UWS), absolute agreement varied between 89.5%–93.5%, sensitivity varied between 90.4%–95.9% and specificity varied between 88.2%–92.2%. In patients who underwent a parotid gland biopsy (n=198), similar results were found (figure 1B).Abstract SAT0431 – Figure 1Ultrasound replacing current ACR-EULAR items in patients who underwent a labial gland biopsy (A) or a parotid gland biopsy (B).ConclusionsSGUS cannot be used as a replacement for salivary gland biopsy or anti-SSA antibody status in the ACR-EULAR criteria because of a substantial reduction in sensitivity. For diagnostic purposes, a high sensitivity is preferred over a high specificity.Replacement of the OSS, Schirmer’s test or UWS by SGUS only resulted in negligible changes in accuracy of the ACR-EULAR criteria. With SGUS being able to replace one of these function tests, clinicians are offered more options that could lead to fulfilment of the ACR-EULAR criteria.Reference[1] Mossel, et al. Ann Rheum Dis2017: Published online first at December 12.Disclosure of InterestNone declared

Countries seek to implement sustainable policies for supporting professionals working with students with an autism spectrum disorder. These policies can advance more slowly in developing states like Romania and Greece. As such, this study aimed to investigate the reported knowledge and training needs of professionals working with ASD students to inform policymakers. Using a cross-sectional design, 475 Romanian and 211 Greek specialists completed an online questionnaire on the following dimensions: diagnosis and assessment of ASD, management of behavioural problems in ASD students, communication skills, technology, teaching, and e-learning platforms. The results showed that Greek professionals have higher levels of ASD knowledge compared to Romanian respondents (MGreece = 15.2, SDGreece = 4.22; MRomania = 13.7, SDRomania = 3.88; U = 39703, p < 0.001). There is also a significant need for training on all the investigated dimensions in both countries, with greater training needs in Romania than in Greece (MGreece = 26, SDGreece = 2.98; MRomania = 27.2, SDRomania = 1.84; U = 35556, p < 0.001). Both countries reported the lowest level of knowledge in innovative teaching technologies and high training needs using an e-learning platform. The results emphasise important gaps in the educational programmes for ASD professionals.

Individuals with high-functioning Autism Spectrum Disorder (ASD), lacking intellectual disabilities and exhibiting subtle functional, not easily conspicuous deficits, are frequently diagnosed with autism during adulthood. Early and objective autism screening and intervention can enhance many aspects of their and their families’ lives as it is the first vital step in tackling with this situation. Current ASD research fosters the implementation of cutting-edge assessment tools, i.e., Machine Learning (ML), eye-tracking technology, robotics, Internet of Things (IoT) etc., rather than relying primarily on subjective behavioural assessment instruments. The present study employed a dataset from a prior study, comprising eye-tracking data from high-functioning autistic adults engaged in a Browse and a Search web-related tasks. MATLAB and various Machine Learning classification algorithms, such as Neural Networks, Decision Trees, Support Vector Machines, Logistic Regression, Naive Bayes, and an Ensemble model were utilised. The highest ASD classification test process results were 82.4% in the Browse task and 85.1% in the Search one, when Neural Networks were implemented. The high classification accuracy achieved underscores the potential of applying Machine Learning on eye-tracking data for early and objective autism detection.

BackgroundPrimary Sjögren's syndrome (pSS) is an autoimmune inflammatory disease predominantly affecting the salivary and lacrimal glands. The main symptoms are dryness of the mouth and eyes. The diagnosis of pSS is based on 6 items according to the American-European consensus group (AECG) classification criteria. Interest regarding ultrasonography (US) as a diagnostic tool for pSS has increased over the last years. However, the applied scoring-systems vary and as of yet international consensus on how to perform the evaluation are lacking. Consequently, the examination and evaluation depends on the examiners skill and experience. In both clinical work and a scientific setting there is a need to use objective methods with as little inter- and intra-examiner variation as possible.ObjectivesThe aim of this study was to develop a software able to analyze changes in digitally stored US images of the major salivary glands.MethodsDigitally stored US images of glandula parotis (n=94) were blindly evaluated and scored as “normal” or “SS-like” by three independent clinical researchers. At least 2/3 evaluations had to be in agreement to classify the image. All images were from patients fulfilling the AECG criteria. Images had been obtained by six clinical investigators using similar protocols on different US machines. For the analysis, images were divided into databases of SS-like changes and normal-appearing morphology. The image classification and analysis was performed using the ScatNet (v.02) algorithm (Ref: http://www.di.ens.fr/data/software/)for MatLab, which is an algorithm for advanced pattern recognition. Images from the databases were randomly selected to be used as either “training” images or “test” images. Each database of “training” images was analysed, and features of pathological and non-pathological morphology were identified by the software. For the software test, the algorithm analyses which of the databases of training images are most similar to the test images and decides in which group the test images belong. This selection was then compared to the manual scoring.ResultsOut of the 94 images evaluated, 40 were classified as normal-appearing and 54 as corresponding to SS-like pathological changes. In the preliminary simulations we have used the following training: test ratios 84:10 (90%), 70:24 (75%) and 47:47 (50%), to respectively train the classification algorithm, and then test the algorithm. The best result with 9/10 correctly classified (92% accuracy) was obtained using 90% of the images to train the software and 10% to test the software. Using 75% or 50% of the images to train the software, the accuracy was reduced to 21/24 (88%) and 25/36 (78%), respectively.ConclusionsPreliminary results indicate that the success rate of the algorithm is closely dependent on the number of images used to train the algorithm. The results are promising and indicate possible clinical use in the evaluation of SGUS images. We will further focus on expanding the image database to achieve more precise results.Disclosure of InterestNone declared