LOCALIZING MOMENTS OF ACTIONS IN UNTRIMMED VIDEOS OF INFANTS WITH AUTISM SPECTRUM DISORDER.

The prevalence of ASD is estimated at 1 per 68 based on US surveillance data. In many cases, ASD can be accurately diagnosed at two to three years old and high risk children can be identified earlier than 24 months, but Canadian data shows a median age at diagnosis of 39 to 55 months. Despite advances in diagnosis of ASD, there is little data on ASD screening among Canadian general paediatricians. The objectives of this study were to examine community paediatricians' ASD screening practices, obtain quantitative date on the use of ASD and general developmental screening tools, and identify factors influencing paediatricians' use of ASD screening tools. A questionnaire was designed base on a survey by dosReis et al to assess developmental and ASD screening among general paediatricians (dosReis 2006) with questions added based on themes drawn from the investigator's qualitative data from focus groups with community paediatricians on ASD screening. Main focus areas were quantitative data on developmental and ASD screening tools use, facilitators and barriers of ASD screening, tool characteristics that encourage their use, and collection of demographic data. After revision by community (n=5) and developmental (n=5) paediatricians, the questionnaire was piloted to n=30 general paediatricians, further refined, and distributed to all general paediatricians in a large multicultural urban centre. Descriptive statistics was used. Results from ongoing data collection showed that most respondents routinely used a developmental screening tool (95%) with the majority using the Nipissing Developmental Screen (95%) and the Rourke Baby Record (42%). 15% routinely using an ASD screening tool while most used an ASD specific screening tool (most commonly the M-CHAT) when they suspected ASD on exam, history, or if there was parental concern. Respondents were divided 50–50 in making the diagnosis of ASD in their practice while 90% agreed or strongly agreed to feeling confident in their ability to identify children with signs and symptoms of ASD. 70% of respondents felt there should be clearer guidelines on how and when to screen for ASD. As for specific ASD screening tool characteristics, the most valued were brevity, ability to be completed by a parent, and availability in multiple languages. This study provides critical data on developmental and ASD screening practices and identifies factors influencing physician decision making and clinical practice. Assessing factors affecting screening in primary care is vital to the future design and implementation of sustainable programs for early detection of ASD.

Early Signs

ObjectivesAlthough the American Academy of Pediatrics recommends screening for autism spectrum disorder (ASD) for all young children, disparities in ASD diagnosis and intervention in minority children persist. One potential contributor to disparities could be whether physicians take different actions after an initial positive screen based on patient demographics. This study estimated factors associated with physicians completing the follow-up interview for the Modified Checklist for Autism in Toddlers with Follow-up (M-CHAT-F), and referring children to diagnostic services, audiology, and Early Intervention (EI) immediately after a positive screen.MethodsChildren seen in a large primary care network that has implemented universal ASD screening were included if they screened positive on the M-CHAT parent questionnaire during a 16–30 month well child visit (N = 2882). Demographics, screening results, and referrals were extracted from the electronic health record.ResultsChildren from lower-income families or on public insurance were more likely to have been administered the follow-up interview. Among children who screened positive, 26% were already in EI, 31% were newly referred to EI, 11% were referred each to audiology and for comprehensive ASD evaluation. 40.2% received at least one recommended referral; 3.7% received all recommended referrals. In adjusted multivariable models, male sex, white versus black race, living in an English-speaking household, and having public insurance were associated with new EI referral. Male sex, black versus white race, and lower household income were associated with referral to audiology. Being from an English-speaking family, white versus Asian race, and lower household income were associated with referral for ASD evaluation. A concurrent positive screen for general developmental concerns was associated with each referral.ConclusionsWe found low rates of follow-up interview completion and referral after positive ASD screen, with variations in referral by sex, language, socio-economic status, and race. Understanding pediatrician decision-making about ASD screening is critical to improving care and reducing disparities.

The prevalence of autism spectrum disorder (ASD) is continuously rising worldwide, with remarkable differences in ASD rates being reported across ethnic and socioeconomic groups. We conducted a prospective cohort study to identify the reasons for differences in ASD rates between the Bedouin and Jewish populations in southern Israel. Screening, referral, and diagnosis of toddlers aged 16-36 months were compared between Bedouin and Jewish populations. ASD screening was conducted at 35 randomly selected mother and child health centers (MCHCs) by trained nurses using the Modified Checklist for Autism in Toddlers with follow-up (M-CHAT/F) instrument. Toddlers screened positive at the MCHCs were monitored throughout the referral and diagnosis process at a single medical center until a diagnosis was determined by a physician specialist using DSM-5 criteria. The study cohort comprised 3,343 toddlers (996 Jewish and 2,347 Bedouin). Bedouin toddlers, compared to Jewish toddlers, were less likely to screen positive with M-CHAT/F (3.0% vs. 3.9%; P = 0.165), were significantly less likely to begin the hospital diagnosis process (HR = 0.38, 95% CI: 0.14-1.08; P = 0.068), and had a higher rates of loss-to-follow-up during the hospital diagnosis process (42.9% vs. 15.6%, respectively; P = 0.001). The results suggest that ethnic-specific barriers in the diagnosis process of ASD contribute to under-diagnosis of ASD in the Bedouin population. Facilitating the diagnosis process for Bedouin families will help to identify more children with ASD at earlier ages and consequently close the ethnic gap in ASD rates. LAY SUMMARY: We followed Bedouin and Jewish toddlers aged 16-36 months from southern Israel through their autism spectrum disorder (ASD) screening referral and diagnosis to identify the reasons for the differences in ASD prevalence between these ethnic groups. Jewish and Bedouin toddlers were equally identified in the ASD screening. However, Bedouin toddlers were less likely to complete the diagnosis process due to higher rates of loss-to-follow-up and slower diagnosis process. Facilitating ASD diagnosis for the Bedouin population will help identifying more toddlers with ASD.

The intersection of autism spectrum disorder and intellectual disability

Screening is important for early identification of children with autism spectrum disorder (ASD), potentially leading to earlier intervention. Research has identified some barriers to early identification of ASD, however, information about ASD screening in Canadian general paediatric practice is lacking. The aim of the study is to better understand ASD screening practice patterns by examining the use of ASD and general developmental screening tools by general paediatricians. The research team conducted a cross-sectional survey of general paediatricians. Two-hundred and sixty-seven paediatricians responded and 132 were eligible for the study. Ninety-three per cent of the responders used a developmental screening tool. Eighty-five per cent of the responders used an ASD screening tool when there were concerns for ASD, and 15% never used one. The most commonly used ASD screening tool was the M-CHAT. Children suspected of having ASD were referred to specialists not only to confirm the diagnosis but also to facilitate access to resources. General paediatricians were keen to incorporate formal ASD screening tools in their practice but identified the need for clearer guidelines. Previous studies have shown that children at risk of ASD continue to be missed through developmental surveillance and targeted screening. Paediatricians are interested in implementing an ASD screening tool and cite brevity and forms that can be completed by parents as factors that would support the use of a screening tool. Clearer guidelines and tools to support ASD screening and access to resources are needed.

An early diagnosis of autism spectrum disorder (ASD) can improve outcomes for children and assist families in accessing services. Part C providers are often tasked with screening for ASD. The purpose of this study was to survey Part C providers nationwide to understand their ASD screening practices and training needs and extend a survey conducted by Tomlin and colleagues. A total of 327 providers participated. Results indicated a majority of respondents (75.8%) screen for ASD using a variety of different measures, while only 26.9% are required by their program to conduct universal screenings. Most participants reported feeling confident in screening for ASD, discussing a child’s red flags with a family, referring a child for further evaluation, and discussing recent evidence surrounding ASD. An area where providers were less confident was working with interpreters to screen for ASD or discuss screening results and ASD with families with cultural or linguistic diversity. Despite high confidence levels in screening for ASD, a vast majority of participants reported they would be interested in attending a training on screening for ASD. Professional development surrounding screening for ASD and discussing ASD with families from diverse backgrounds may assist in improving Part C provider screening practices.

Autism Spectrum Disorder (ASD) is a neuro developmental condition that significantly impacts the daily lives of those affected. While complete eradication remains challenging, early interventions can help alleviate its severity. This study presents a novel framework for assessing various Machine Learning (ML) techniques to detect ASD early. The framework incorporates four Feature Scaling (FS) methods—Quantile Transformer (QT), Power Transformer (PT), Normalizer, and Max Abs Scaler (MAS). Subsequently, the scaled datasets are subjected to classification using eight ML algorithms: Ada Boost (AB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA). Experiments are conducted on four established ASD datasets categorizing individuals by age groups—Toddlers, Adolescents, Children, and Adults. By evaluating classification outcomes through diverse statistical metrics such as Accuracy, Receiver Operating Characteristic (ROC) curve, F1-score, Precision, Recall, Mathews Correlation Coefficient (MCC), Kappa score, and Log loss, optimal classification methods and FS techniques are determined for each dataset. Results show AB achieving the highest accuracy of 99.25% for Toddlers and 97.95% for Children, while LDA achieves 97.12% for Adolescents and 99.03% for Adults. Notably, using normalizer FS for Toddlers and Children, and QT FS for Adolescents and Adults yield the best accuracies. Furthermore, ASD risk factors are quantified, and attribute importance is ranked employing four Feature Selection Techniques (FSTs)—Info Gain Attribute Evaluator (IGAE), Gain Ratio Attribute Evaluator (GRAE), Relief F Attribute Evaluator (RFAE), and Correlation Attribute Evaluator (CAE). These comprehensive evaluations underscore the significance of fine-tuning ML methodologies for accurate ASD prediction across different age groups. The detailed analysis of feature importance presented herein can aid healthcare practitioners in ASD screening, offering promising advancements compared to existing detection approaches.

American Academy of Pediatrics (AAP) recommendations for Autism Spectrum Disorder (ASD) screening do not specifically address safety-net clinics, which provide multidisciplinary healthcare services to underserved patients. This project explored the potential for ASD screening in safety-net clinics by assessing parental perceived knowledge of ASD at JayDoc Free Clinic, a student-run safety-net clinic in Wyandotte County, Kansas. May through December 2022, patients who reported to be the parent of a minor received a demographic survey and a Likert-style questionnaire assessing perceived knowledge of ASD, including understanding the importance of ASD screening and ASD signs and symptoms. Responses were categorized into positive, negative, and unsure. Demographic variables included the minor’s primary care provider (PCP) status. Results were analyzed using bivariate analysis, with chi-square tests for significance (p-value ≤ 0.05). Of the 52 participants who completed at least one Likert response, 55.8% reported their child had a PCP. Responses were somewhat balanced with 44.2% positive for understanding the importance of ASD screening and 53.8% positive for understanding ASD signs and symptoms. For understanding the signs and symptoms of ASD, an unsure response (32.7% of responses) was statistically associated with a lack of PCP (p = 0.017). The balance of positive with negative and unsure responses could reflect lack of ASD knowledge and may relate to healthcare inaccessibility. This is consistent with the significant association between lack of PCP and unsure responses for understanding ASD signs and symptoms. ASD screening and education in safety-net clinics like JayDoc could be valuable, particularly for children without a PCP.

The American Academy of Pediatrics recommends referring children at elevated risk of autism spectrum disorder (ASD) for Part C early intervention (EI) services, but notes that EI services often fail to provide ASD screening. To evaluate the hypothesis that a multistage screening protocol for ASD implemented in 3 EI settings will increase autism detection, especially among Spanish-speaking families. Difference-in-differences analyses with propensity score weighting of a quasi-experimental design using administrative data on 3 implementation EI agencies and 9 comparison EI agencies from 2012 to 2018 provided by the Massachusetts Department of Public Health. Eligible children were aged 14 to 36 months, enrolled in EI, had no prior ASD diagnosis or medical condition precluding participation, and had parents who spoke English or Spanish. The final analytic sample included 33 326 unique patients assessed across 150 200 person-quarters. Multistage ASD assessment protocol including ASD screening questionnaires, observational screener, and diagnostic evaluation. Increased incidence of ASD diagnoses as documented in Department of Public Health records and reductions in language-associated health care disparities. Implementation of screening at 3 EI sites was associated with a significant increase in the rate of ASD diagnoses (incidence rate ratios [IRR], 1.6; 95% CI, 1.3-2.1; P < .001), representing an additional 8.1 diagnoses per 1000 children per quarter. Among Spanish-speaking families, screening was also associated with a significant increase in the rate of ASD diagnoses (IRR, 2.6; 95% CI, 1.6-4.3; P < .001), representing 15.4 additional diagnoses per 1000 children per quarter-a larger increase than for non-Spanish-speaking families (interaction IRR, 1.8; 95% CI, 1.0-3.1; P = .005). Exploratory analyses revealed that screening was associated with a larger increase in the rate of ASD diagnoses among boys (IRR, 1.8; 95% CI, 1.4-2.3; P < .001) than among girls (IRR, 1.1; 95% CI, 0.6-1.7; P = .84). In this study, associations between increased rates of ASD diagnoses and reductions in disparities for Spanish-speaking households support the effectiveness of multistage screening in EI. This study provides a comprehensive evaluation of ASD screening in EI settings as well as a rigorous evaluation of ASD screening in any setting with a no-screening comparison condition. Given that the intervention included multiple components, mechanisms of action warrant further research, as do disparities by child sex.

Part I: Assessment and Diagnosis. Wetherby, Understanding and Measuring Social Communication in Children with Autism Spectrum Disorders. Lord, Richler, Early Diagnosis of Children with Autism Spectrum Disorders. Part II: Screening and Surveillance. Charman, Baron-Cohen, Screening for Autism Spectrum Disorders in Populations: Progress, Challenges, and Questions for Future Research and Practice. Zwaigenbaum, Stone, Early Screening for Autism Spectrum Disorders in Clinical Practice Settings. Part III: Evidence-based Interventions. Yoder, McDuffie, Treatment of Responding To and Initiating Joint Attention. Rogers, Evidence-based Interventions for Language Development in Young Children with Autism. Wolfberg, Schuler, Promoting Social Reciprocity and Symbolic Representation in Children with Autism Spectrum Disorders: Designing Quality Peer Play Interventions. Nadel, Aouka, Imitation: Some Cues for Intervention Approaches in Autism Spectrum Disorders. Howlin, Augmentative and Alternative Communication Systems for Children with Autism. Part IV: Developmental and Neurobiological Issues. Walden, Hurley, A Developmental Approach to Understanding Atypical Development. Mundy, Thorp, The Neural Basis of Early Joint Attention Behavior.

Autism spectrum disorder (ASD) screening completion rates are often low despite their validity and influence on earlier intervention and positive treatment outcomes. This study sought to examine the use of one ASD screening tool, the Modified Checklist for Autism in Toddlers-Revised (MCHAT-R), in a racially and ethnically diverse urban pediatric clinic to review potential disparities within screening rates and referral practices. A retrospective chart review was conducted for children (N = 999) within the ages of 17 to 34 months seen for a well-child appointment at one of 3 pediatric clinics: a general pediatric clinic, resident pediatric clinic, and Hispanic pediatric clinic. MCHAT-R screening completion rates were low for all clinics. There were no significant differences in MCHAT-R screening completion based on ethnicity; however, the percentage of children screening positive on the MCHAT-R was significantly higher for Hispanic versus non-Hispanic children. Referral practices were highly variable across positive screenings, and few children received the appropriate combination of referrals. Ethnic disparities in ASD positive screening rates and inconsistent referrals represent a critical issue in current pediatric practice. There is a great need for the development of more culturally sensitive ASD screening instruments. Additionally, to help increase ASD screening rate and accuracy, as well as consistency in referrals, greater emphasis is needed on professional training, parental education, and technology use within pediatric clinics.

Recently, remarkable progress has been achieved in human action recognition and detection by using deep learning techniques. However, for action detection in real-world untrimmed videos, the accuracies of most existing approaches are still far from satisfactory, due to the difficulties in temporal action localization. On the other hand, the spatiotempoal features are not well utilized in recent work for video analysis. To tackle these problems, we propose a spatiotemporal, multi-task, 3D deep convolutional neural network to detect (including temporally localize and recognition) actions in untrimmed videos. First, we introduce a fusion framework which aims to extract video-level spatiotemporal features in the training phase. And we demonstrate the effectiveness of video-level features by evaluating our model on human action recognition task. Then, under the fusion framework, we propose a spatiotemporal multi-task network, which has two sibling output layers for action classification and temporal localization, respectively. To obtain precise temporal locations, we present a novel temporal regression method to revise the proposal window which contains an action. Meanwhile, in order to better utilize the rich motion information in videos, we introduce a novel video representation, interlaced images, as an additional network input stream. As a result, our model outperforms state-of-the-art methods for both action recognition and detection on standard benchmarks.

Research Article| December 01 2019 Diagnosis of Autism Spectrum Disorders Requires More Than Screening AAP Grand Rounds (2019) 42 (6): 66. https://doi.org/10.1542/gr.42-6-66 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Diagnosis of Autism Spectrum Disorders Requires More Than Screening. AAP Grand Rounds December 2019; 42 (6): 66. https://doi.org/10.1542/gr.42-6-66 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search toolbar search search input Search input auto suggest filter your search All PublicationsAll JournalsAAP Grand RoundsPediatricsHospital PediatricsPediatrics In ReviewNeoReviewsAAP NewsAll AAP Sites Search Advanced Search Topics: autism spectrum disorder Source: Monteiro SA, Dempsey J, Berry LN, et al. Screening and referral practices for autism spectrum disorder in primary pediatric care. Pediatrics. 2019; 144(4): e20183326; doi: https://doi.org/10.1542/peds.2018-3326Google Scholar Investigators from Baylor College of Medicine, Houston, TX, and the University of Colorado School of Medicine, Aurora, CO, conducted a retrospective study to determine rates of (a) screening for autism spectrum disorder (ASD) at 18- and 24-month well-child visits to primary care pediatricians using the Modified Checklist for Autism in Toddlers (M-CHAT); (b) referral for those failing screening; and (c) diagnosis of ASD in children who failed the screen. For the study, the authors abstracted data on children having an 18-month and/or 24-month well-child visit between 2014 and 2016 to one of 290 pediatricians who were part of a hospital-owned network of 59 practices that shared a centralized electronic medical record (EMR) system. Within the network, use of M-CHAT at the 18- and 24-month visit is a standard practice. The rate of screening with M-CHAT at each of the visits was determined. The EMR of children who failed the M-CHAT screen (defined as a score ≥3) was reviewed to determine whether a referral to an autism specialist was made and completed, and subsequent diagnoses of ASD or other neurodevelopmental conditions, with a follow-up period of 23–46 months in these patients. Among 13,417 children eligible for screening at 18 months, the M-CHAT was completed for 12,531 (93%), while the M-CHAT was completed for 10,983 of 13,328 (82%) at 24 months. A total of 532 unique children failed 648 M-CHAT screens (3%). Of these 532 children, 2 died. For the remaining 530 patients, 165 (31%) were referred for additional evaluation by a subspecialist, including 122 at or before the 18- or 24-month visit (mean age at referral 20.8 months) and 69 later than the 24-month visit (mean age at referral 36 months). Visits with autism specialists were completed by 68 children (59%) who were referred at or before the 18- or 24-month visit, and 42 were diagnosed with an ASD at a mean age of 27 months. Among those referred later than the 24-month visit, 39 (57%) completed the subspecialist evaluation, and 31 were diagnosed with an ASD. Overall, including 17 children diagnosed through another mechanism, 96 of the 530 children (18%) who failed an M-CHAT screening were diagnosed with an ASD. An additional 314 patients (59%) were diagnosed with other neurodevelopmental conditions, including delayed speech, language disorders, and global delay by their primary care pediatrician. The authors conclude that despite high rates of screening for ASD by primary care pediatricians, rates of referral for further ASD evaluation for children who failed an M-CHAT screen were suboptimal. Dr Doherty has disclosed no financial relationship relevant to this commentary. This commentary does not contain a discussion of an unapproved/investigative use of a commercial product/device. The term ASDs encompasses a broad clinical category comprised of many biologically distinct... You do not currently have access to this content.

We investigate the feature design and classification architectures in temporal action localization. This application focuses on detecting and labeling actions in untrimmed videos, which brings more challenge than classifying presegmented videos. The major difficulty for action localization is the uncertainty of action occurrence and utilization of information from different scales. Two innovations are proposed to address this issue. First, we propose a Pyramid of Score Distribution Feature (PSDF) to capture the motion information at multiple resolutions centered at each detection window. This novel feature mitigates the influence of unknown action position and duration, and shows significant performance gain over previous detection approaches. Second, inter-frame consistency is further explored by incorporating PSDF into the state-of-the-art Recurrent Neural Networks, which gives additional performance gain in detecting actions in temporally untrimmed videos. We tested our action localization framework on the THUMOS'15 and MPII Cooking Activities Dataset, both of which show a large performance improvement over previous attempts.