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Ahn YA, Moffitt JM, Tao Y, Custode S, Parlade M, Beaumont A, Cardona S, Hale M, Durocher J, Alessandri M, Shyu ML, Perry LK, Messinger DS. Objective Measurement of Social Gaze and Smile Behaviors in Children with Suspected Autism Spectrum Disorder During Administration of the Autism Diagnostic Observation Schedule, 2nd Edition. J Autism Dev Disord 2024; 54:2124-2137. [PMID: 37103660 DOI: 10.1007/s10803-023-05990-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/09/2023] [Indexed: 04/28/2023]
Abstract
Best practice for the assessment of autism spectrum disorder (ASD) symptom severity relies on clinician ratings of the Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2), but the association of these ratings with objective measures of children's social gaze and smiling is unknown. Sixty-six preschool-age children (49 boys, M = 39.97 months, SD = 10.58) with suspected ASD (61 confirmed ASD) were administered the ADOS-2 and provided social affect calibrated severity scores (SA CSS). Children's social gaze and smiling during the ADOS-2, captured with a camera contained in eyeglasses worn by the examiner and parent, were obtained via a computer vision processing pipeline. Children who gazed more at their parents (p = .04) and whose gaze at their parents involved more smiling (p = .02) received lower social affect severity scores, indicating fewer social affect symptoms, adjusted R2 = .15, p = .003.
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Affiliation(s)
- Yeojin A Ahn
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | | | - Yudong Tao
- Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA
| | - Stephanie Custode
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Meaghan Parlade
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Amy Beaumont
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Sandra Cardona
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Melissa Hale
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Jennifer Durocher
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | | | - Mei-Ling Shyu
- Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA
| | - Lynn K Perry
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Daniel S Messinger
- Department of Psychology, University of Miami, Coral Gables, FL, USA.
- Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA.
- Departments of Pediatrics and Music Engineering, University of Miami, Coral Gables, FL, USA.
- Department of Psychology, University of Miami, 5665 Ponce de Leon Blvd., P.O. Box 248185, Coral Gables, FL, 33124, USA.
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Isaev DY, Sabatos-DeVito M, Di Martino JM, Carpenter K, Aiello R, Compton S, Davis N, Franz L, Sullivan C, Dawson G, Sapiro G. Computer Vision Analysis of Caregiver-Child Interactions in Children with Neurodevelopmental Disorders: A Preliminary Report. J Autism Dev Disord 2024; 54:2286-2297. [PMID: 37103659 PMCID: PMC10603206 DOI: 10.1007/s10803-023-05973-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/20/2023] [Indexed: 04/28/2023]
Abstract
We report preliminary results of computer vision analysis of caregiver-child interactions during free play with children diagnosed with autism (N = 29, 41-91 months), attention-deficit/hyperactivity disorder (ADHD, N = 22, 48-100 months), or combined autism + ADHD (N = 20, 56-98 months), and neurotypical children (NT, N = 7, 55-95 months). We conducted micro-analytic analysis of 'reaching to a toy,' as a proxy for initiating or responding to a toy play bout. Dyadic analysis revealed two clusters of interaction patterns, which differed in frequency of 'reaching to a toy' and caregivers' contingent responding to the child's reach for a toy by also reaching for a toy. Children in dyads with higher caregiver responsiveness had less developed language, communication, and socialization skills. Clusters were not associated with diagnostic groups. These results hold promise for automated methods of characterizing caregiver responsiveness in dyadic interactions for assessment and outcome monitoring in clinical trials.
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Affiliation(s)
- Dmitry Yu Isaev
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
| | - Maura Sabatos-DeVito
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - J Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Kimberly Carpenter
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Rachel Aiello
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Scott Compton
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Naomi Davis
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Lauren Franz
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Connor Sullivan
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
- Departments of Biomedical Engineering, Computer Science, and Mathematics, Duke University, Durham, NC, USA.
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3
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Jaiswal A, Kruiper R, Rasool A, Nandkeolyar A, Wall DP, Washington P. Digitally Diagnosing Multiple Developmental Delays Using Crowdsourcing Fused With Machine Learning: Protocol for a Human-in-the-Loop Machine Learning Study. JMIR Res Protoc 2024; 13:e52205. [PMID: 38329783 PMCID: PMC10884895 DOI: 10.2196/52205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/17/2023] [Accepted: 12/26/2023] [Indexed: 02/09/2024] Open
Abstract
BACKGROUND A considerable number of minors in the United States are diagnosed with developmental or psychiatric conditions, potentially influenced by underdiagnosis factors such as cost, distance, and clinician availability. Despite the potential of digital phenotyping tools with machine learning (ML) approaches to expedite diagnoses and enhance diagnostic services for pediatric psychiatric conditions, existing methods face limitations because they use a limited set of social features for prediction tasks and focus on a single binary prediction, resulting in uncertain accuracies. OBJECTIVE This study aims to propose the development of a gamified web system for data collection, followed by a fusion of novel crowdsourcing algorithms with ML behavioral feature extraction approaches to simultaneously predict diagnoses of autism spectrum disorder and attention-deficit/hyperactivity disorder in a precise and specific manner. METHODS The proposed pipeline will consist of (1) gamified web applications to curate videos of social interactions adaptively based on the needs of the diagnostic system, (2) behavioral feature extraction techniques consisting of automated ML methods and novel crowdsourcing algorithms, and (3) the development of ML models that classify several conditions simultaneously and that adaptively request additional information based on uncertainties about the data. RESULTS A preliminary version of the web interface has been implemented, and a prior feature selection method has highlighted a core set of behavioral features that can be targeted through the proposed gamified approach. CONCLUSIONS The prospect for high reward stems from the possibility of creating the first artificial intelligence-powered tool that can identify complex social behaviors well enough to distinguish conditions with nuanced differentiators such as autism spectrum disorder and attention-deficit/hyperactivity disorder. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/52205.
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Affiliation(s)
- Aditi Jaiswal
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Ruben Kruiper
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Abdur Rasool
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Aayush Nandkeolyar
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Dennis P Wall
- Department of Pediatrics (Systems Medicine), Stanford University School of Medicine, Stanford, CA, United States
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, United States
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Peter Washington
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
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Mukherjee D, Bhavnani S, Lockwood Estrin G, Rao V, Dasgupta J, Irfan H, Chakrabarti B, Patel V, Belmonte MK. Digital tools for direct assessment of autism risk during early childhood: A systematic review. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2024; 28:6-31. [PMID: 36336996 PMCID: PMC10771029 DOI: 10.1177/13623613221133176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
LAY ABSTRACT The challenge of finding autistic children, and finding them early enough to make a difference for them and their families, becomes all the greater in parts of the world where human and material resources are in short supply. Poverty of resources delays interventions, translating into a poverty of outcomes. Digital tools carry potential to lessen this delay because they can be administered by non-specialists in children's homes, schools or other everyday environments, they can measure a wide range of autistic behaviours objectively and they can automate analysis without requiring an expert in computers or statistics. This literature review aimed to identify and describe digital tools for screening children who may be at risk for autism. These tools are predominantly at the 'proof-of-concept' stage. Both portable (laptops, mobile phones, smart toys) and fixed (desktop computers, virtual-reality platforms) technologies are used to present computerised games, or to record children's behaviours or speech. Computerised analysis of children's interactions with these technologies differentiates children with and without autism, with promising results. Tasks assessing social responses and hand and body movements are the most reliable in distinguishing autistic from typically developing children. Such digital tools hold immense potential for early identification of autism spectrum disorder risk at a large scale. Next steps should be to further validate these tools and to evaluate their applicability in a variety of settings. Crucially, stakeholders from underserved communities globally must be involved in this research, lest it fail to capture the issues that these stakeholders are facing.
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Affiliation(s)
- Debarati Mukherjee
- Indian Institute of Public Health - Bengaluru, Public Health Foundation of India, India
| | | | | | - Vaisnavi Rao
- Institute for Democracy and Economic Affairs (IDEAS), Malaysia
| | | | | | | | - Vikram Patel
- Child Development Group, Sangath, India
- Harvard Medical School, USA
- Harvard T.H. Chan School of Public Health, USA
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Stern E, Micoulaud Franchi JA, Dumas G, Moreira J, Mouchabac S, Maruani J, Philip P, Lejoyeux M, Geoffroy PA. How Can Digital Mental Health Enhance Psychiatry? Neuroscientist 2023; 29:681-693. [PMID: 35658666 DOI: 10.1177/10738584221098603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The use of digital technologies is constantly growing around the world. The wider-spread adoption of digital technologies and solutions in the daily clinical practice in psychiatry seems to be a question of when, not if. We propose a synthesis of the scientific literature on digital technologies in psychiatry and discuss the main aspects of its possible uses and interests in psychiatry according to three domains of influence that appeared to us: 1) assist and improve current care: digital psychiatry allows for more people to have access to care by simply being more accessible but also by being less stigmatized and more convenient; 2) develop new treatments: digital psychiatry allows for new treatments to be distributed via apps, and practical guidelines can reduce ethical challenges and increase the efficacy of digital tools; and 3) produce scientific and medical knowledge: digital technologies offer larger and more objective data collection, allowing for more detection and prevention of symptoms. Finally, ethical and efficacy issues remain, and some guidelines have been put forth on how to safely use these solutions and prepare for the future.
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Affiliation(s)
- Emilie Stern
- GHU Paris-Psychiatrie & Neurosciences, Paris, France
| | - Jean-Arthur Micoulaud Franchi
- University of Bordeaux, SANPSY, USR 3413, F-33000, Bordeaux, France
- CNRS, SANPSY, USR 3413, F-33000, Bordeaux, France
- CHU Bordeaux, Service Universitaire de Médecine Du sommeil, F-33000, Bordeaux, France
| | - Guillaume Dumas
- CHU Sainte-Justine Research Center, Department of Psychiatry, University of Montreal, Quebec, Canada
- Mila-Quebec Artificial Intelligence Institute, University of Montreal, Quebec, Canada
| | | | - Stephane Mouchabac
- Department of Psychiatry, Department of Psychiatry Hôpital Saint Antoine-APHP, Sorbonne University, Paris, France
- Infrastructure of Clinical Research in Neurosciences-Psychiatry, Brain and Spine Institute (ICM), Inserm, Sorbonne University, Paris, France
| | - Julia Maruani
- Département de psychiatrie et d'addictologie, AP-HP, GHU Paris Nord, DMU Neurosciences, Hôpital Bichat-Claude Bernard, F-75018, Paris, France
- Université de Paris, NeuroDiderot, Inserm U1141, F-75019, Paris, France
| | - Pierre Philip
- University of Bordeaux, SANPSY, USR 3413, F-33000, Bordeaux, France
- CNRS, SANPSY, USR 3413, F-33000, Bordeaux, France
- CHU Bordeaux, Service Universitaire de Médecine Du sommeil, F-33000, Bordeaux, France
| | - Michel Lejoyeux
- GHU Paris-Psychiatrie & Neurosciences, Paris, France
- Département de psychiatrie et d'addictologie, AP-HP, GHU Paris Nord, DMU Neurosciences, Hôpital Bichat-Claude Bernard, F-75018, Paris, France
- Université de Paris, NeuroDiderot, Inserm U1141, F-75019, Paris, France
| | - Pierre A Geoffroy
- GHU Paris-Psychiatrie & Neurosciences, Paris, France
- Département de psychiatrie et d'addictologie, AP-HP, GHU Paris Nord, DMU Neurosciences, Hôpital Bichat-Claude Bernard, F-75018, Paris, France
- Université de Paris, NeuroDiderot, Inserm U1141, F-75019, Paris, France
- CNRS UPR 3212, Institute for Cellular and Integrative Neurosciences, Strasbourg, France
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6
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Wedasingha N, Samarasinghe P, Senevirathna L, Papandrea M, Puiatti A, Rankin D. Automated anomalous child repetitive head movement identification through transformer networks. Phys Eng Sci Med 2023; 46:1427-1445. [PMID: 37814077 DOI: 10.1007/s13246-023-01309-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 07/24/2023] [Indexed: 10/11/2023]
Abstract
The increasing prevalence of behavioral disorders in children is of growing concern within the medical community. Recognising the significance of early identification and intervention for atypical behaviors, there is a consensus on their pivotal role in improving outcomes. Due to inadequate facilities and a shortage of medical professionals with specialized expertise, traditional diagnostic methods have been unable to effectively address the rising incidence of behavioral disorders. Hence, there is a need to develop automated approaches for the diagnosis of behavioral disorders in children, to overcome the challenges with traditional methods. The purpose of this study is to develop an automated model capable of analyzing videos to differentiate between typical and atypical repetitive head movements in. To address problems resulting from the limited availability of child datasets, various learning methods are employed to mitigate these issues. In this work, we present a fusion of transformer networks, and Non-deterministic Finite Automata (NFA) techniques, which classify repetitive head movements of a child as typical or atypical based on an analysis of gender, age, and type of repetitive head movement, along with count, duration, and frequency of each repetitive head movement. Experimentation was carried out with different transfer learning methods to enhance the performance of the model. The experimental results on five datasets: NIR face dataset, Bosphorus 3D face dataset, ASD dataset, SSBD dataset, and the Head Movements in the Wild dataset, indicate that our proposed model has outperformed many state-of-the-art frameworks when distinguishing typical and atypical repetitive head movements in children.
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Affiliation(s)
- Nushara Wedasingha
- Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe, 10115, Colombo, Sri Lanka.
| | - Pradeepa Samarasinghe
- Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe, 10115, Colombo, Sri Lanka
| | - Lasantha Senevirathna
- Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe, 10115, Colombo, Sri Lanka
| | - Michela Papandrea
- Information Systems and Networking Institute (ISIN), University of Applied Sciences and Arts of Southern Switzerland, Via Pobiette, Manno, 6928, Switzerland
| | - Alessandro Puiatti
- Institute of Digital Technologies for Personalized Healthcare (MeDiTech), University of Applied Sciences and Arts of Southern Switzerland, Via Pobiette, Manno, 6928, Switzerland
| | - Debbie Rankin
- School of Computing, Engineering and Intelligent Systems, Ulster University, Northland Road, Derry-Londonderry, BT48 7JL, Northern Ireland, UK
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7
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李 翔, 马 昕, 李 贻. [A review of studies on visual behavior analysis aided diagnosis of autism spectrum disorders]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:812-819. [PMID: 37666774 PMCID: PMC10477381 DOI: 10.7507/1001-5515.202204038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/27/2023] [Indexed: 09/06/2023]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and repetitive behaviors. With the rapid development of computer vision, visual behavior analysis aided diagnosis of ASD has got more and more attention. This paper reviews the research on visual behavior analysis aided diagnosis of ASD. First, the core symptoms and clinical diagnostic criteria of ASD are introduced briefly. Secondly, according to clinical diagnostic criteria, the interaction scenes are classified and introduced. Then, the existing relevant datasets are discussed. Finally, we analyze and compare the advantages and disadvantages of visual behavior analysis aided diagnosis methods for ASD in different interactive scenarios. The challenges in this research field are summarized and the prospects of related research are presented to promote the clinical application of visual behavior analysis in ASD diagnosis.
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Affiliation(s)
- 翔 李
- 山东大学 控制科学与工程学院 (济南 250061)School of Control Science and Engineering, Shandong University, Jinan 250061, P. R. China
| | - 昕 马
- 山东大学 控制科学与工程学院 (济南 250061)School of Control Science and Engineering, Shandong University, Jinan 250061, P. R. China
| | - 贻斌 李
- 山东大学 控制科学与工程学院 (济南 250061)School of Control Science and Engineering, Shandong University, Jinan 250061, P. R. China
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8
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Washington P, Wall DP. A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism. Annu Rev Biomed Data Sci 2023; 6:211-228. [PMID: 37137169 PMCID: PMC11093217 DOI: 10.1146/annurev-biodatasci-020722-125454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic features are observable, can be tracked over time, and can be managed or even eliminated through proper therapy and treatments. However, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related neurodevelopmental delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide increased access to services for affected families. Several efforts previously conducted by a multitude of research labs have spawned great progress toward improved digital diagnostics and digital therapies for children with autism. We review the literature on digital health methods for autism behavior quantification and beneficial therapies using data science. We describe both case-control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics that integrate machine learning models of autism-related behaviors, including the factors that must be addressed for translational use. Finally, we describe ongoing challenges and potential opportunities for the field of autism data science. Given the heterogeneous nature of autism and the complexities of the relevant behaviors, this review contains insights that are relevant to neurological behavior analysis and digital psychiatry more broadly.
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Affiliation(s)
- Peter Washington
- Department of Information and Computer Sciences, University of Hawai'i at Mānoa, Honolulu, Hawai'i, USA
| | - Dennis P Wall
- Departments of Pediatrics (Systems Medicine), Biomedical Data Science, and Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA;
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9
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Coffman M, Di Martino JM, Aiello R, Carpenter KL, Chang Z, Compton S, Eichner B, Espinosa S, Flowers J, Franz L, Perochon S, Krishnappa Babu PR, Sapiro G, Dawson G. Relationship between quantitative digital behavioral features and clinical profiles in young autistic children. Autism Res 2023; 16:1360-1374. [PMID: 37259909 PMCID: PMC10524806 DOI: 10.1002/aur.2955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 05/06/2023] [Indexed: 06/02/2023]
Abstract
Early behavioral markers for autism include differences in social attention and orienting in response to one's name when called, and differences in body movements and motor abilities. More efficient, scalable, objective, and reliable measures of these behaviors could improve early screening for autism. This study evaluated whether objective and quantitative measures of autism-related behaviors elicited from an app (SenseToKnow) administered on a smartphone or tablet and measured via computer vision analysis (CVA) are correlated with standardized caregiver-report and clinician administered measures of autism-related behaviors and cognitive, language, and motor abilities. This is an essential step in establishing the concurrent validity of a digital phenotyping approach. In a sample of 485 toddlers, 43 of whom were diagnosed with autism, we found that CVA-based gaze variables related to social attention were associated with the level of autism-related behaviors. Two language-related behaviors measured via the app, attention to people during a conversation and responding to one's name being called, were associated with children's language skills. Finally, performance during a bubble popping game was associated with fine motor skills. These findings provide initial support for the concurrent validity of the SenseToKnow app and its potential utility in identifying clinical profiles associated with autism. Future research is needed to determine whether the app can be used as an autism screening tool, can reliably stratify autism-related behaviors, and measure changes in autism-related behaviors over time.
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Affiliation(s)
- Marika Coffman
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - J. Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Rachel Aiello
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Kimberly L.H. Carpenter
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Zhuoqing Chang
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Scott Compton
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Brian Eichner
- Department of Pediatrics, Duke University, Durham, NC, USA
| | - Steve Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Jacqueline Flowers
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Lauren Franz
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Ecole Normale Superieure Paris-Saclay, Gif-Sur-Yvette, France
| | | | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Department of Biomedical Engineering, Mathematics, and Computer Sciences, Duke University, Durham, NC, USA
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
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10
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Leland E, Fasano RM, Moffitt JM, Romero C, Cepero C, Messinger DS, Perry LK. Automated measurement: The need for a more objective view of the speech and language of autistic children. Front Hum Neurosci 2023; 17:1124273. [PMID: 37091813 PMCID: PMC10117873 DOI: 10.3389/fnhum.2023.1124273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 03/24/2023] [Indexed: 04/09/2023] Open
Affiliation(s)
- Eraine Leland
- Department of Psychology, University of Miami, Coral Gables, FL, United States
- *Correspondence: Eraine Leland
| | - Regina M. Fasano
- Department of Psychology, University of Miami, Coral Gables, FL, United States
| | | | - Celia Romero
- Department of Psychology, University of Miami, Coral Gables, FL, United States
| | - Catalina Cepero
- Department of Psychology, University of Miami, Coral Gables, FL, United States
| | - Daniel S. Messinger
- Department of Psychology, University of Miami, Coral Gables, FL, United States
- Department of Pediatrics, University of Miami, Miami, FL, United States
- Department of Electrical and Computational Engineering, University of Miami, Coral Gables, FL, United States
- Department of Music Engineering, University of Miami, Coral Gables, FL, United States
| | - Lynn K. Perry
- Department of Psychology, University of Miami, Coral Gables, FL, United States
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11
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Washington P. Digitally Diagnosing Multiple Developmental Delays using Crowdsourcing fused with Machine Learning: A Research Protocol. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.05.23286817. [PMID: 36945467 PMCID: PMC10029023 DOI: 10.1101/2023.03.05.23286817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Background Roughly 17% percent of minors in the United States aged 3 through 17 years have a diagnosis of one or more developmental or psychiatric conditions, with the true prevalence likely being higher due to underdiagnosis in rural areas and for minority populations. Unfortunately, timely diagnostic services are inaccessible to a large portion of the United States and global population due to cost, distance, and clinician availability. Digital phenotyping tools have the potential to shorten the time-to-diagnosis and to bring diagnostic services to more people by enabling accessible evaluations. While automated machine learning (ML) approaches for detection of pediatric psychiatry conditions have garnered increased research attention in recent years, existing approaches use a limited set of social features for the prediction task and focus on a single binary prediction. Objective I propose the development of a gamified web system for data collection followed by a fusion of novel crowdsourcing algorithms with machine learning behavioral feature extraction approaches to simultaneously predict diagnoses of Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) in a precise and specific manner. Methods The proposed pipeline will consist of: (1) a gamified web applications to curate videos of social interactions adaptively based on needs of the diagnostic system, (2) behavioral feature extraction techniques consisting of automated ML methods and novel crowdsourcing algorithms, and (3) development of ML models which classify several conditions simultaneously and which adaptively request additional information based on uncertainties about the data. Conclusions The prospective for high reward stems from the possibility of creating the first AI-powered tool which can identify complex social behaviors well enough to distinguish conditions with nuanced differentiators such as ASD and ADHD.
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12
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Dawson G, Rieder AD, Johnson MH. Prediction of autism in infants: progress and challenges. Lancet Neurol 2023; 22:244-254. [PMID: 36427512 PMCID: PMC10100853 DOI: 10.1016/s1474-4422(22)00407-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/17/2022] [Accepted: 09/27/2022] [Indexed: 11/24/2022]
Abstract
Autism spectrum disorder (henceforth autism) is a neurodevelopmental condition that can be reliably diagnosed in children by age 18-24 months. Prospective longitudinal studies of infants aged 1 year and younger who are later diagnosed with autism are elucidating the early developmental course of autism and identifying ways of predicting autism before diagnosis is possible. Studies that use MRI, EEG, and near-infrared spectroscopy have identified differences in brain development in infants later diagnosed with autism compared with infants without autism. Retrospective studies of infants younger than 1 year who received a later diagnosis of autism have also showed an increased prevalence of health conditions, such as sleep disorders, gastrointestinal disorders, and vision problems. Behavioural features of infants later diagnosed with autism include differences in attention, vocalisations, gestures, affect, temperament, social engagement, sensory processing, and motor abilities. Although research findings offer insight on promising screening approaches for predicting autism in infants, individual-level predictions remain a future goal. Multiple scientific challenges and ethical questions remain to be addressed to translate research on early brain-based and behavioural predictors of autism into feasible and reliable screening tools for clinical practice.
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Affiliation(s)
- Geraldine Dawson
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA.
| | - Amber D Rieder
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Mark H Johnson
- Department of Psychology, University of Cambridge, Cambridge, UK; Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK
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13
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Jin X, Zhu H, Cao W, Zou X, Chen J. Identifying activity level related movement features of children with ASD based on ADOS videos. Sci Rep 2023; 13:3471. [PMID: 36859661 PMCID: PMC9975881 DOI: 10.1038/s41598-023-30628-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 02/27/2023] [Indexed: 03/03/2023] Open
Abstract
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.
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Affiliation(s)
- Xuemei Jin
- South China Academy of Advanced Optoelectronics, South China Normal University (SCNU), Guangzhou, 510006, China
| | - Huilin Zhu
- Child Development and Behavior Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China.
| | - Wei Cao
- South China Academy of Advanced Optoelectronics, South China Normal University (SCNU), Guangzhou, 510006, China
| | - Xiaobing Zou
- Child Development and Behavior Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China
| | - Jiajia Chen
- South China Academy of Advanced Optoelectronics, South China Normal University (SCNU), Guangzhou, 510006, China.
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14
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Perochon S, Matias Di Martino J, Carpenter KLH, Compton S, Davis N, Espinosa S, Franz L, Rieder AD, Sullivan C, Sapiro G, Dawson G. A tablet-based game for the assessment of visual motor skills in autistic children. NPJ Digit Med 2023; 6:17. [PMID: 36737475 PMCID: PMC9898502 DOI: 10.1038/s41746-023-00762-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 01/21/2023] [Indexed: 02/05/2023] Open
Abstract
Increasing evidence suggests that early motor impairments are a common feature of autism. Thus, scalable, quantitative methods for measuring motor behavior in young autistic children are needed. This work presents an engaging and scalable assessment of visual-motor abilities based on a bubble-popping game administered on a tablet. Participants are 233 children ranging from 1.5 to 10 years of age (147 neurotypical children and 86 children diagnosed with autism spectrum disorder [autistic], of which 32 are also diagnosed with co-occurring attention-deficit/hyperactivity disorder [autistic+ADHD]). Computer vision analyses are used to extract several game-based touch features, which are compared across autistic, autistic+ADHD, and neurotypical participants. Results show that younger (1.5-3 years) autistic children pop the bubbles at a lower rate, and their ability to touch the bubble's center is less accurate compared to neurotypical children. When they pop a bubble, their finger lingers for a longer period, and they show more variability in their performance. In older children (3-10-years), consistent with previous research, the presence of co-occurring ADHD is associated with greater motor impairment, reflected in lower accuracy and more variable performance. Several motor features are correlated with standardized assessments of fine motor and cognitive abilities, as evaluated by an independent clinical assessment. These results highlight the potential of touch-based games as an efficient and scalable approach for assessing children's visual-motor skills, which can be part of a broader screening tool for identifying early signs associated with autism.
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Affiliation(s)
- Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Ecole Normale Supérieure Paris-Saclay, Gif-Sur-Yvette, France
| | - J Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Kimberly L H Carpenter
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Scott Compton
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Naomi Davis
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Steven Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Lauren Franz
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Amber D Rieder
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Connor Sullivan
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
| | - Geraldine Dawson
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA.
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15
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Pierce K, Wen TH, Zahiri J, Andreason C, Courchesne E, Barnes CC, Lopez L, Arias SJ, Esquivel A, Cheng A. Level of Attention to Motherese Speech as an Early Marker of Autism Spectrum Disorder. JAMA Netw Open 2023; 6:e2255125. [PMID: 36753277 PMCID: PMC9909502 DOI: 10.1001/jamanetworkopen.2022.55125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 12/19/2022] [Indexed: 02/09/2023] Open
Abstract
Importance Caregivers have long captured the attention of their infants by speaking in motherese, a playful speech style characterized by heightened affect. Reduced attention to motherese in toddlers with autism spectrum disorder (ASD) may be a contributor to downstream language and social challenges and could be diagnostically revealing. Objective To investigate whether attention toward motherese speech can be used as a diagnostic classifier of ASD and is associated with language and social ability. Design, Setting, and Participants This diagnostic study included toddlers aged 12 to 48 months, spanning ASD and non-ASD diagnostic groups, at a research center. Data were collected from February 2018 to April 2021 and analyzed from April 2021 to March 2022. Exposures Gaze-contingent eye-tracking test. Main Outcomes and Measures Using gaze-contingent eye tracking wherein the location of a toddler's fixation triggered a specific movie file, toddlers participated in 1 or more 1-minute eye-tracking tests designed to quantify attention to motherese speech, including motherese vs traffic (ie, noisy vehicles on a highway) and motherese vs techno (ie, abstract shapes with music). Toddlers were also diagnostically and psychometrically evaluated by psychologists. Levels of fixation within motherese and nonmotherese movies and mean number of saccades per second were calculated. Receiver operating characteristic (ROC) curves were used to evaluate optimal fixation cutoff values and associated sensitivity, specificity, positive predictive value (PPV), and negative predictive value. Within the ASD group, toddlers were stratified based on low, middle, or high levels of interest in motherese speech, and associations with social and language abilities were examined. Results A total of 653 toddlers were included (mean [SD] age, 26.45 [8.37] months; 480 males [73.51%]). Unlike toddlers without ASD, who almost uniformly attended to motherese speech with a median level of 82.25% and 80.75% across the 2 tests, among toddlers with ASD, there was a wide range, spanning 0% to 100%. Both the traffic and techno paradigms were effective diagnostic classifiers, with large between-group effect sizes (eg, ASD vs typical development: Cohen d, 1.0 in the techno paradigm). Across both paradigms, a cutoff value of 30% or less fixation on motherese resulted in an area under the ROC curve (AUC) of 0.733 (95% CI, 0.693-0.773) and 0.761 (95% CI, 0.717-0.804), respectively; specificity of 98% (95% CI, 95%-99%) and 96% (95% CI, 92%-98%), respectively; and PPV of 94% (95% CI, 86%-98%). Reflective of heterogeneity and expected subtypes in ASD, sensitivity was lower at 18% (95% CI, 14%-22%) and 29% (95% CI, 24%-34%), respectively. Combining metrics increased the AUC to 0.841 (95% CI, 0.805-0.877). Toddlers with ASD who showed the lowest levels of attention to motherese speech had weaker social and language abilities. Conclusions and Relevance In this diagnostic study, a subset of toddlers showed low levels of attention toward motherese speech. When a cutoff level of 30% or less fixation on motherese speech was used, toddlers in this range were diagnostically classified as having ASD with high accuracy. Insight into which toddlers show unusually low levels of attention to motherese may be beneficial not only for early ASD diagnosis and prognosis but also as a possible therapeutic target.
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Affiliation(s)
- Karen Pierce
- Autism Center of Excellence, Department of Neurosciences, University of California San Diego, La Jolla
| | - Teresa H. Wen
- Autism Center of Excellence, Department of Neurosciences, University of California San Diego, La Jolla
| | - Javad Zahiri
- Autism Center of Excellence, Department of Neurosciences, University of California San Diego, La Jolla
| | - Charlene Andreason
- Autism Center of Excellence, Department of Neurosciences, University of California San Diego, La Jolla
| | - Eric Courchesne
- Autism Center of Excellence, Department of Neurosciences, University of California San Diego, La Jolla
| | - Cynthia C. Barnes
- Autism Center of Excellence, Department of Neurosciences, University of California San Diego, La Jolla
| | - Linda Lopez
- Autism Center of Excellence, Department of Neurosciences, University of California San Diego, La Jolla
| | - Steven J. Arias
- Autism Center of Excellence, Department of Neurosciences, University of California San Diego, La Jolla
| | - Ahtziry Esquivel
- Autism Center of Excellence, Department of Neurosciences, University of California San Diego, La Jolla
| | - Amanda Cheng
- Autism Center of Excellence, Department of Neurosciences, University of California San Diego, La Jolla
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16
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Weichselbaum C, Hendrix N, Albright J, Dougherty JD, Botteron KN, Constantino JN, Marrus N. Social attention during object engagement: toward a cross-species measure of preferential social orienting. J Neurodev Disord 2022; 14:58. [PMID: 36517753 PMCID: PMC9749210 DOI: 10.1186/s11689-022-09467-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 11/15/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND A central challenge in preclinical research investigating the biology of autism spectrum disorder (ASD) is the translation of ASD-related social phenotypes across humans and animal models. Social orienting, an observable, evolutionarily conserved behavior, represents a promising cross-species ASD phenotype given that disrupted social orienting is an early-emerging ASD feature with evidence for predicting familial recurrence. Here, we adapt a competing-stimulus social orienting task from domesticated dogs to naturalistic play behavior in human toddlers and test whether this approach indexes decreased social orienting in ASD. METHODS Play behavior was coded from the Autism Diagnostic Observation Schedule (ADOS) in two samples of toddlers, each with and without ASD. Sample 1 (n = 16) consisted of community-ascertained research participants, while Sample 2 involved a prospective study of infants at a high or low familial liability for ASD (n = 67). Coding quantified the child's looks towards the experimenter and caregiver, a social stimulus, while playing with high-interest toys, a non-social stimulus. A competing-stimulus measure of "Social Attention During Object Engagement" (SADOE) was calculated by dividing the number of social looks by total time spent playing with toys. SADOE was compared based on ASD diagnosis and differing familial liability for ASD. RESULTS In both samples, toddlers with ASD exhibited significantly lower SADOE compared to toddlers without ASD, with large effect sizes (Hedges' g ≥ 0.92) driven by a lower frequency of child-initiated spontaneous looks. Among toddlers at high familial likelihood of ASD, toddlers with ASD showed lower SADOE than toddlers without ASD, while SADOE did not differ based on presence or absence of familial ASD risk alone. SADOE correlated negatively with ADOS social affect calibrated severity scores and positively with the Communication and Symbolic Behavior Scales social subscale. In a binary logistic regression model, SADOE alone correctly classified 74.1% of cases, which rose to 85.2% when combined with cognitive development. CONCLUSIONS This work suggests that a brief behavioral measure pitting a high-interest nonsocial stimulus against the innate draw of social partners can serve as a feasible cross-species measure of social orienting, with implications for genetically informative behavioral phenotyping of social deficits in ASD and other neurodevelopmental disorders.
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Affiliation(s)
- Claire Weichselbaum
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Box 8504, St Louis, MO, 63110, USA
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Box 8232, St Louis, MO, 63110, USA
| | - Nicole Hendrix
- Department of Pediatrics, Marcus Autism Center, Emory University Pediatric Institute, 1920 Briarcliff Rd, Atlanta, GA, 30329, USA
| | - Jordan Albright
- Virginia Tech Autism Clinic & Center for Autism Research, Virginia Polytechnic Institute and State University, 3110 Prices Fork Rd, Blacksburg, VA, 24060, USA
| | - Joseph D Dougherty
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Box 8504, St Louis, MO, 63110, USA
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Box 8232, St Louis, MO, 63110, USA
| | - Kelly N Botteron
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Box 8504, St Louis, MO, 63110, USA
- Department of Radiology, Washington University School of Medicine, 660 S. Euclid, 35 Ave, St Louis, MO, 63110, USA
| | - John N Constantino
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Box 8504, St Louis, MO, 63110, USA
| | - Natasha Marrus
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Box 8504, St Louis, MO, 63110, USA.
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17
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Meimei L, Zenghui M. A systematic review of telehealth screening, assessment, and diagnosis of autism spectrum disorder. Child Adolesc Psychiatry Ment Health 2022; 16:79. [PMID: 36209100 PMCID: PMC9547568 DOI: 10.1186/s13034-022-00514-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/21/2022] [Indexed: 11/18/2022] Open
Abstract
There is a significant delay between parents having concerns and receiving a formal assessment and Autism Spectrum Disorder (ASD) diagnosis. Telemedicine could be an effective alternative that shortens the waiting time for parents and primary health providers in ASD screening and diagnosis. We conducted a systematic review examining the uses of telemedicine technology for ASD screening, assessment, or diagnostic purposes and to what extent sample characteristics and psychometric properties were reported. This study searched four databases from 2000 to 2022 and obtained 26 studies that met the inclusion criteria. The 17 applications used in these 26 studies were divided into three categories based on their purpose: screening, diagnostic, and assessment. The results described the data extracted, including study characteristics, applied methods, indicators seen, and psychometric properties. Among the 15 applications with psychometric properties reported, the sensitivity ranged from 0.70 to 1, and the specificity ranged from 0.38 to 1. The present study highlights the strengths and weaknesses of current telemedicine approaches and provides a basis for future research. More rigorous empirical studies with larger sample sizes are needed to understand the feasibility, strengths, and limitations of telehealth technologies for screening, assessing, and diagnosing ASD.
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Affiliation(s)
- Liu Meimei
- grid.12380.380000 0004 1754 9227Vrije University Amsterdam, Amsterdam, The Netherlands
| | - Ma Zenghui
- Beijing ALSOABA Technology Co. LTD, ALSOLIFE, Beijing, China
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18
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Zhao J, Zhang X, Lu Y, Wu X, Zhou F, Yang S, Wang L, Wu X, Fei F. Virtual reality technology enhances the cognitive and social communication of children with autism spectrum disorder. Front Public Health 2022; 10:1029392. [PMID: 36276341 PMCID: PMC9582941 DOI: 10.3389/fpubh.2022.1029392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 09/15/2022] [Indexed: 01/29/2023] Open
Abstract
Objective We aimed to explore the impact of using virtual reality technology to intervene in and encourage the developmental behavior areas of cognition, imitation, and social interaction in children with autism spectrum disorder. Methods Forty-four children with autism spectrum disorder were divided randomly into an intervention group and a control group, with each group consisting of 22 participants. Incorporating conventional rehabilitation strategies, virtual reality technology was used with the intervention group to conduct rehabilitation training in areas including cognition, imitation, and social interaction. The control group was provided conventional/routine clinical rehabilitation training. The children's cognitive development was evaluated before and 3 months after intervention. Results After intervention, the developmental abilities of both groups of children in the areas of cognition, imitation, and social interaction were improved over their abilities measured before the intervention (P < 0.05). However, post-intervention score differences between the two groups demonstrated that the intervention group levels were better than the control group levels only in the areas of cognition and social interaction (P < 0.05). Conclusion Combining virtual reality with conventional rehabilitation training improved the cognitive and social development of children with autism spectrum disorder and supported the goal of improving the rehabilitation effect.
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Affiliation(s)
- Junqiang Zhao
- Department of Children Rehabilitation, First Affiliated Hospital of Xinxiang Medical University, Xinxiang, China,Xinxiang Key Laboratory of Medical Virtual Reality and Augmented Reality, Xinxiang, China,Xinxiang Intelligent Image Diagnosis Engineering Technology Research Center, Xinxiang, China
| | - Xinxin Zhang
- Xinxiang Key Laboratory of Medical Virtual Reality and Augmented Reality, Xinxiang, China,Xinxiang Intelligent Image Diagnosis Engineering Technology Research Center, Xinxiang, China,Department of Nursing, Xinxiang Medical University, Xinxiang, China
| | - Yi Lu
- Xinxiang Key Laboratory of Medical Virtual Reality and Augmented Reality, Xinxiang, China,Xinxiang Intelligent Image Diagnosis Engineering Technology Research Center, Xinxiang, China,Department of Nursing, Xinxiang Medical University, Xinxiang, China
| | - Xingyang Wu
- Xinxiang Key Laboratory of Medical Virtual Reality and Augmented Reality, Xinxiang, China,Xinxiang Intelligent Image Diagnosis Engineering Technology Research Center, Xinxiang, China,Department of Medical Engineering, Xinxiang Medical University, Xinxiang, China
| | - Fujun Zhou
- Department of Children Rehabilitation, First Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Shichang Yang
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Luping Wang
- Department of Children Rehabilitation, First Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Xiaoyan Wu
- Department of Nursing, Huzhou Maternal and Child Health Care Hospital, Huzhou, China,*Correspondence: Xiaoyan Wu
| | - Fangrong Fei
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China,Fangrong Fei
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19
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Hong J, Gandhi J, Mensah EE, Zeraati FZ, Jarjue EH, Lee K, Kacorri H. Blind Users Accessing Their Training Images in Teachable Object Recognizers. ASSETS. ANNUAL ACM CONFERENCE ON ASSISTIVE TECHNOLOGIES 2022; 2022:14. [PMID: 36916963 PMCID: PMC10008526 DOI: 10.1145/3517428.3544824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Teachable object recognizers provide a solution for a very practical need for blind people - instance level object recognition. They assume one can visually inspect the photos they provide for training, a critical and inaccessible step for those who are blind. In this work, we engineer data descriptors that address this challenge. They indicate in real time whether the object in the photo is cropped or too small, a hand is included, the photos is blurred, and how much photos vary from each other. Our descriptors are built into open source testbed iOS app, called MYCam. In a remote user study in (N = 12) blind participants' homes, we show how descriptors, even when error-prone, support experimentation and have a positive impact in the quality of training set that can translate to model performance though this gain is not uniform. Participants found the app simple to use indicating that they could effectively train it and that the descriptors were useful. However, many found the training being tedious, opening discussions around the need for balance between information, time, and cognitive load.
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Affiliation(s)
- Jonggi Hong
- Smith-Kettlewell Eye Research Institute, San Francisco, United States
| | - Jaina Gandhi
- University of Maryland, College Park, United States
| | | | | | | | - Kyungjun Lee
- University of Maryland, College Park, United States
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20
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Liu J, Wang Z, Xu K, Ji B, Zhang G, Wang Y, Deng J, Xu Q, Xu X, Liu H. Early Screening of Autism in Toddlers via Response-To-Instructions Protocol. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:3914-3924. [PMID: 32966227 DOI: 10.1109/tcyb.2020.3017866] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Early screening of autism spectrum disorder (ASD) is crucial since early intervention evidently confirms significant improvement of functional social behavior in toddlers. This article attempts to bootstrap the response-to-instructions (RTIs) protocol with vision-based solutions in order to assist professional clinicians with an automatic autism diagnosis. The correlation between detected objects and toddler's emotional features, such as gaze, is constructed to analyze their autistic symptoms. Twenty toddlers between 16-32 months of age, 15 of whom diagnosed with ASD, participated in this study. The RTI method is validated against human codings, and group differences between ASD and typically developing (TD) toddlers are analyzed. The results suggest that the agreement between clinical diagnosis and the RTI method achieves 95% for all 20 subjects, which indicates vision-based solutions are highly feasible for automatic autistic diagnosis.
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21
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Abstract
Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.
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Affiliation(s)
- Anoopum S. Gupta
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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22
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Automatic Assessment of Motor Impairments in Autism Spectrum Disorders: A Systematic Review. Cognit Comput 2022. [DOI: 10.1007/s12559-021-09940-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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23
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Messinger DS, Perry LK, Mitsven SG, Tao Y, Moffitt J, Fasano RM, Custode SA, Jerry CM. Computational approaches to understanding interaction and development. ADVANCES IN CHILD DEVELOPMENT AND BEHAVIOR 2022; 62:191-230. [PMID: 35249682 PMCID: PMC9840818 DOI: 10.1016/bs.acdb.2021.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Audio-visual recording and location tracking produce enormous quantities of digital data with which researchers can document children's everyday interactions in naturalistic settings and assessment contexts. Machine learning and other computational approaches can produce replicable, automated measurements of these big behavioral data. The economies of scale afforded by repeated automated measurements offer a potent approach to investigating linkages between real-time behavior and developmental change. In our work, automated measurement of audio from child-worn recorders-which quantify the frequency of child and adult speech and index its phonemic complexity-are paired with ultrawide radio tracking of children's location and interpersonal orientation. Applications of objective measurement indicate the influence of adult behavior in both expert ratings of attachment behavior and ratings of autism severity, suggesting the role of dyadic factors in these "child" assessments. In the preschool classroom, location/orientation measures provide data-driven measures of children's social contact, fertile ground for vocal interactions. Both the velocity of children's movement toward one another and their social contact with one another evidence homophily: children with autism spectrum disorder, other developmental disabilities, and typically developing children were more likely to interact with children in the same group even in inclusive preschool classrooms designed to promote interchange between all children. In the vocal domain, the frequency of peer speech and the phonemic complexity of teacher speech predict the frequency and phonemic complexity of children's own speech over multiple timescales. Moreover, children's own speech predicts their assessed language abilities across disability groups, suggesting how everyday interactions facilitate development.
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Affiliation(s)
- Daniel S. Messinger
- Department of Psychology, University of Miami, Coral Gables, Florida,Department of Electrical and Computer Engineering, University of Miami, Coral Gables, Florida,Department of Pediatrics, Department of Music Engineering, University of Miami, Coral Gables, Florida,Departmetn of Music Engineering, University of Miami, Coral Gables, Florida
| | - Lynn K. Perry
- Department of Psychology, University of Miami, Coral Gables, Florida
| | | | - Yudong Tao
- Department of Pediatrics, Department of Music Engineering, University of Miami, Coral Gables, Florida
| | | | - Regina M. Fasano
- Department of Psychology, University of Miami, Coral Gables, Florida
| | | | - Christian M. Jerry
- Department of Psychology, University of Miami, Coral Gables, Florida,Department of Psychology, Indiana University, Bloomington, Indiana
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Peristeri E, Vogelzang M, Tsimpli IM. Bilingualism Effects on the Cognitive Flexibility of Autistic Children: Evidence From Verbal Dual-Task Paradigms. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2021; 2:558-585. [PMID: 37214625 PMCID: PMC10198706 DOI: 10.1162/nol_a_00055] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 09/03/2021] [Indexed: 05/24/2023]
Abstract
The deficit in cognitive flexibility (i.e., the ability to adapt cognitive behavior to changing contexts) is one of the most prominent characteristics of autistic individuals. Inflexibility may manifest in restricted interests and increased susceptibility to the effects of misinformation either through inefficient inhibition of non-target information or deficient recall of correct information. Bilingualism has been shown to enhance executive functions in both typically developing children and autistic children; yet, the effect of bilingualism on cognitive flexibility in autism remains underexplored. In this study, we used verbal dual-tasks to compare cognitive flexibility across 50 monolingual autistic and 50 bilingual autistic children, and 50 monolingual and 50 bilingual typically developing children. The children were also administered language ability tests and a nonverbal global-local cognitive flexibility task, in order to investigate whether performance in the dual-tasks would be modulated by the children's language and executive function skills. The bilingual autistic children outperformed their monolingual autistic peers in the dual-tasks. The strength of the bilingualism effect, however, was modulated by the type of language processing that interfered with the target information in each dual-task, which suggests that the bilingual autistic children calibrated their processing resources and efficiently adapted them to the changing demands of the dual-task only to the extent that the task did not exceed their language abilities. Bilingual autistic children relied on their executive functions rather than on their language abilities while performing in the dual-tasks. The overall results show that bilingualism compensates for the reduced cognitive flexibility in autism.
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Affiliation(s)
- Eleni Peristeri
- Department of Neurology, University Hospital of Larissa, Faculty of Medicine, University of Thessaly, Larissa, Greece
| | - Margreet Vogelzang
- Faculty of Modern and Medieval Languages and Linguistics, University of Cambridge, Cambridge, UK
| | - Ianthi Maria Tsimpli
- Faculty of Modern and Medieval Languages and Linguistics, University of Cambridge, Cambridge, UK
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25
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Alvari G, Coviello L, Furlanello C. EYE-C: Eye-Contact Robust Detection and Analysis during Unconstrained Child-Therapist Interactions in the Clinical Setting of Autism Spectrum Disorders. Brain Sci 2021; 11:1555. [PMID: 34942856 PMCID: PMC8699076 DOI: 10.3390/brainsci11121555] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 11/04/2021] [Accepted: 11/19/2021] [Indexed: 12/26/2022] Open
Abstract
The high level of heterogeneity in Autism Spectrum Disorder (ASD) and the lack of systematic measurements complicate predicting outcomes of early intervention and the identification of better-tailored treatment programs. Computational phenotyping may assist therapists in monitoring child behavior through quantitative measures and personalizing the intervention based on individual characteristics; still, real-world behavioral analysis is an ongoing challenge. For this purpose, we designed EYE-C, a system based on OpenPose and Gaze360 for fine-grained analysis of eye-contact episodes in unconstrained therapist-child interactions via a single video camera. The model was validated on video data varying in resolution and setting, achieving promising performance. We further tested EYE-C on a clinical sample of 62 preschoolers with ASD for spectrum stratification based on eye-contact features and age. By unsupervised clustering, three distinct sub-groups were identified, differentiated by eye-contact dynamics and a specific clinical phenotype. Overall, this study highlights the potential of Artificial Intelligence in categorizing atypical behavior and providing translational solutions that might assist clinical practice.
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Affiliation(s)
- Gianpaolo Alvari
- Department of Psychology and Cognitive Sciences, University of Trento, Corso Bettini 84, 38068 Rovereto, Italy
- DSH Research Unit, Bruno Kessler Foundation, Via Sommarive 8, 38123 Trento, Italy
| | - Luca Coviello
- University of Trento, 38122 Trento, Italy;
- Enogis, Via al Maso Visintainer 8, 38122 Trento, Italy
| | - Cesare Furlanello
- HK3 Lab, Piazza Manifatture 1, 38068 Rovereto, Italy;
- Orobix Life, Via Camozzi 145, 24121 Bergamo, Italy
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26
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Perochon S, Di Martino M, Aiello R, Baker J, Carpenter K, Chang Z, Compton S, Davis N, Eichner B, Espinosa S, Flowers J, Franz L, Gagliano M, Harris A, Howard J, Kollins SH, Perrin EM, Raj P, Spanos M, Walter B, Sapiro G, Dawson G. A scalable computational approach to assessing response to name in toddlers with autism. J Child Psychol Psychiatry 2021; 62:1120-1131. [PMID: 33641216 PMCID: PMC8397798 DOI: 10.1111/jcpp.13381] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 08/15/2020] [Accepted: 12/04/2020] [Indexed: 12/26/2022]
Abstract
BACKGROUND This study is part of a larger research program focused on developing objective, scalable tools for digital behavioral phenotyping. We evaluated whether a digital app delivered on a smartphone or tablet using computer vision analysis (CVA) can elicit and accurately measure one of the most common early autism symptoms, namely failure to respond to a name call. METHODS During a pediatric primary care well-child visit, 910 toddlers, 17-37 months old, were administered an app on an iPhone or iPad consisting of brief movies during which the child's name was called three times by an examiner standing behind them. Thirty-seven toddlers were subsequently diagnosed with autism spectrum disorder (ASD). Name calls and children's behavior were recorded by the camera embedded in the device, and children's head turns were coded by both CVA and a human. RESULTS CVA coding of response to name was found to be comparable to human coding. Based on CVA, children with ASD responded to their name significantly less frequently than children without ASD. CVA also revealed that children with ASD who did orient to their name exhibited a longer latency before turning their head. Combining information about both the frequency and the delay in response to name improved the ability to distinguish toddlers with and without ASD. CONCLUSIONS A digital app delivered on an iPhone or iPad in real-world settings using computer vision analysis to quantify behavior can reliably detect a key early autism symptom-failure to respond to name. Moreover, the higher resolution offered by CVA identified a delay in head turn in toddlers with ASD who did respond to their name. Digital phenotyping is a promising methodology for early assessment of ASD symptoms.
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Affiliation(s)
- Sam Perochon
- Department of Electrical and Computer Engineering, Duke University
| | | | - Rachel Aiello
- Department of Psychiatry and Behavioral Sciences, Duke University
| | | | | | - Zhuoqing Chang
- Department of Electrical and Computer Engineering, Duke University
| | - Scott Compton
- Department of Psychiatry and Behavioral Sciences, Duke University
| | - Naomi Davis
- Department of Psychiatry and Behavioral Sciences, Duke University
| | | | | | | | - Lauren Franz
- Department of Psychiatry and Behavioral Sciences, Duke University
| | | | - Adrianne Harris
- Department of Psychiatry and Behavioral Sciences, Duke University.; Department of Psychology & Neuroscience, Duke University
| | - Jill Howard
- Department of Psychiatry and Behavioral Sciences, Duke University
| | - Scott H. Kollins
- Department of Psychiatry and Behavioral Sciences, Duke University
| | - Eliana M. Perrin
- Department of Pediatrics, Duke University.; Duke Center for Childhood Obesity Research
| | - Pradeep Raj
- Department of Electrical and Computer Engineering, Duke University
| | - Marina Spanos
- Department of Psychiatry and Behavioral Sciences, Duke University
| | - Barbara Walter
- Department of Psychiatry and Behavioral Sciences, Duke University
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27
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Zampella CJ, Wang LAL, Haley M, Hutchinson AG, de Marchena A. Motor Skill Differences in Autism Spectrum Disorder: a Clinically Focused Review. Curr Psychiatry Rep 2021; 23:64. [PMID: 34387753 DOI: 10.1007/s11920-021-01280-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/12/2021] [Indexed: 12/26/2022]
Abstract
PURPOSE OF REVIEW This review synthesizes recent, clinically relevant findings on the scope, significance, and centrality of motor skill differences in autism spectrum disorder (ASD). RECENT FINDINGS Motor challenges in ASD are pervasive, clinically meaningful, and highly underrecognized, with up to 87% of the autistic population affected but only a small percentage receiving motor-focused clinical care. Across development, motor differences are associated with both core autism symptoms and broader functioning, though the precise nature of those associations and the specificity of motor profiles to ASD remain unestablished. Findings suggest that motor difficulties in ASD are quantifiable and treatable, and that detection and intervention efforts targeting motor function may also positively influence social communication. Recent evidence supports a need for explicit recognition of motor impairment within the diagnostic framework of ASD as a clinical specifier. Motor differences in ASD warrant greater clinical attention and routine incorporation into screening, evaluation, and treatment planning.
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Affiliation(s)
- Casey J Zampella
- Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
| | - Leah A L Wang
- Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Margaret Haley
- Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Anne G Hutchinson
- Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ashley de Marchena
- Department of Behavioral and Social Sciences, University of the Sciences, Philadelphia, PA, USA
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28
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Chang Z, Di Martino JM, Aiello R, Baker J, Carpenter K, Compton S, Davis N, Eichner B, Espinosa S, Flowers J, Franz L, Harris A, Howard J, Perochon S, Perrin EM, Krishnappa Babu PR, Spanos M, Sullivan C, Walter BK, Kollins SH, Dawson G, Sapiro G. Computational Methods to Measure Patterns of Gaze in Toddlers With Autism Spectrum Disorder. JAMA Pediatr 2021; 175:827-836. [PMID: 33900383 PMCID: PMC8077044 DOI: 10.1001/jamapediatrics.2021.0530] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 02/05/2021] [Indexed: 12/18/2022]
Abstract
Importance Atypical eye gaze is an early-emerging symptom of autism spectrum disorder (ASD) and holds promise for autism screening. Current eye-tracking methods are expensive and require special equipment and calibration. There is a need for scalable, feasible methods for measuring eye gaze. Objective Using computational methods based on computer vision analysis, we evaluated whether an app deployed on an iPhone or iPad that displayed strategically designed brief movies could elicit and quantify differences in eye-gaze patterns of toddlers with ASD vs typical development. Design, Setting, and Participants A prospective study in pediatric primary care clinics was conducted from December 2018 to March 2020, comparing toddlers with and without ASD. Caregivers of 1564 toddlers were invited to participate during a well-child visit. A total of 993 toddlers (63%) completed study measures. Enrollment criteria were aged 16 to 38 months, healthy, English- or Spanish-speaking caregiver, and toddler able to sit and view the app. Participants were screened with the Modified Checklist for Autism in Toddlers-Revised With Follow-up during routine care. Children were referred by their pediatrician for diagnostic evaluation based on results of the checklist or if the caregiver or pediatrician was concerned. Forty toddlers subsequently were diagnosed with ASD. Exposures A mobile app displayed on a smartphone or tablet. Main Outcomes and Measures Computer vision analysis quantified eye-gaze patterns elicited by the app, which were compared between toddlers with ASD vs typical development. Results Mean age of the sample was 21.1 months (range, 17.1-36.9 months), and 50.6% were boys, 59.8% White individuals, 16.5% Black individuals, 23.7% other race, and 16.9% Hispanic/Latino individuals. Distinctive eye-gaze patterns were detected in toddlers with ASD, characterized by reduced gaze to social stimuli and to salient social moments during the movies, and previously unknown deficits in coordination of gaze with speech sounds. The area under the receiver operating characteristic curve discriminating ASD vs non-ASD using multiple gaze features was 0.90 (95% CI, 0.82-0.97). Conclusions and Relevance The app reliably measured both known and new gaze biomarkers that distinguished toddlers with ASD vs typical development. These novel results may have potential for developing scalable autism screening tools, exportable to natural settings, and enabling data sets amenable to machine learning.
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Affiliation(s)
- Zhuoqing Chang
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
| | - J. Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
| | - Rachel Aiello
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Jeffrey Baker
- Department of Pediatrics, Duke University, Durham, North Carolina
| | - Kimberly Carpenter
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Scott Compton
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Naomi Davis
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Brian Eichner
- Department of Pediatrics, Duke University, Durham, North Carolina
| | - Steven Espinosa
- Office of Information Technology, Duke University, Durham, North Carolina
| | - Jacqueline Flowers
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Lauren Franz
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
- Duke Global Health Institute, Duke University, Durham, North Carolina
| | - Adrianne Harris
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
- Department of Psychology & Neuroscience, Duke University, Durham, North Carolina
| | - Jill Howard
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- Ecole Normale Supérieure Paris-Saclay, Cachan, France
| | - Eliana M. Perrin
- Department of Pediatrics, Duke University, Durham, North Carolina
- Duke Center for Childhood Obesity Research, Duke University, Durham, North Carolina
| | | | - Marina Spanos
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Connor Sullivan
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | | | - Scott H. Kollins
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
| | - Geraldine Dawson
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
- Duke Global Health Institute, Duke University, Durham, North Carolina
- Duke Institute for Brain Sciences, Duke University, Durham, North Carolina
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
- Department of Biomedical Engineering, Mathematics, and Computer Sciences, Duke University, Durham, North Carolina
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Using 2D video-based pose estimation for automated prediction of autism spectrum disorders in young children. Sci Rep 2021; 11:15069. [PMID: 34301963 PMCID: PMC8302646 DOI: 10.1038/s41598-021-94378-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 07/09/2021] [Indexed: 11/10/2022] Open
Abstract
Clinical research in autism has recently witnessed promising digital phenotyping results, mainly focused on single feature extraction, such as gaze, head turn on name-calling or visual tracking of the moving object. The main drawback of these studies is the focus on relatively isolated behaviors elicited by largely controlled prompts. We recognize that while the diagnosis process understands the indexing of the specific behaviors, ASD also comes with broad impairments that often transcend single behavioral acts. For instance, the atypical nonverbal behaviors manifest through global patterns of atypical postures and movements, fewer gestures used and often decoupled from visual contact, facial affect, speech. Here, we tested the hypothesis that a deep neural network trained on the non-verbal aspects of social interaction can effectively differentiate between children with ASD and their typically developing peers. Our model achieves an accuracy of 80.9% (F1 score: 0.818; precision: 0.784; recall: 0.854) with the prediction probability positively correlated to the overall level of symptoms of autism in social affect and repetitive and restricted behaviors domain. Provided the non-invasive and affordable nature of computer vision, our approach carries reasonable promises that a reliable machine-learning-based ASD screening may become a reality not too far in the future.
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30
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Bovery M, Dawson G, Hashemi J, Sapiro G. A Scalable Off-the-Shelf Framework for Measuring Patterns of Attention in Young Children and its Application in Autism Spectrum Disorder. IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2021; 12:722-731. [PMID: 35450132 PMCID: PMC9017594 DOI: 10.1109/taffc.2018.2890610] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Autism spectrum disorder (ASD) is associated with deficits in the processing of social information and difficulties in social interaction, and individuals with ASD exhibit atypical attention and gaze. Traditionally, gaze studies have relied upon precise and constrained means of monitoring attention using expensive equipment in laboratories. In this work we develop a low-cost off-the-shelf alternative for measuring attention that can be used in natural settings. The head and iris positions of 104 16-31 months children, an age range appropriate for ASD screening and diagnosis, 22 of them diagnosed with ASD, were recorded using the front facing camera in an iPad while they watched on the device screen a movie displaying dynamic stimuli, social stimuli on the left and nonsocial stimuli on the right. The head and iris position were then automatically analyzed via computer vision algorithms to detect the direction of attention. Children in the ASD group paid less attention to the movie, showed less attention to the social as compared to the nonsocial stimuli, and often fixated their attention to one side of the screen. The proposed method provides a low-cost means of monitoring attention to properly designed stimuli, demonstrating that the integration of stimuli design and automatic response analysis results in the opportunity to use off-the-shelf cameras to assess behavioral biomarkers.
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Affiliation(s)
- Matthieu Bovery
- EEA Department, ENS Paris-Saclay, Cachan, FRANCE. He performed this work while visiting Duke University
| | - Geraldine Dawson
- Department of Psychiatry and Behavioral Sciences, Duke Center for Autism and Brain Development, and the Duke Institute for Brain Sciences, Durham, NC
| | - Jordan Hashemi
- Department of Electrical and Computer Engineering, Duke University, Durham, NC
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC.; BME, CS, and Math at Duke University
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31
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Alvari G, Furlanello C, Venuti P. Is Smiling the Key? Machine Learning Analytics Detect Subtle Patterns in Micro-Expressions of Infants with ASD. J Clin Med 2021; 10:1776. [PMID: 33921756 PMCID: PMC8073678 DOI: 10.3390/jcm10081776] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/15/2021] [Accepted: 04/16/2021] [Indexed: 01/01/2023] Open
Abstract
Time is a key factor to consider in Autism Spectrum Disorder. Detecting the condition as early as possible is crucial in terms of treatment success. Despite advances in the literature, it is still difficult to identify early markers able to effectively forecast the manifestation of symptoms. Artificial intelligence (AI) provides effective alternatives for behavior screening. To this end, we investigated facial expressions in 18 autistic and 15 typical infants during their first ecological interactions, between 6 and 12 months of age. We employed Openface, an AI-based software designed to systematically analyze facial micro-movements in images in order to extract the subtle dynamics of Social Smiles in unconstrained Home Videos. Reduced frequency and activation intensity of Social Smiles was computed for children with autism. Machine Learning models enabled us to map facial behavior consistently, exposing early differences hardly detectable by non-expert naked eye. This outcome contributes to enhancing the potential of AI as a supportive tool for the clinical framework.
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Affiliation(s)
- Gianpaolo Alvari
- Department of Psychology and Cognitive Sciences, University of Trento, 38068 Rovereto, Italy;
- Data Science for Health (DSH) Research Unit, Bruno Kessler Foundation (FBK), 38123 Trento, Italy
| | | | - Paola Venuti
- Department of Psychology and Cognitive Sciences, University of Trento, 38068 Rovereto, Italy;
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32
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Wu C, Liaqat S, Helvaci H, Cheung SCS, Chuah CN, Ozonoff S, Young G. Machine Learning Based Autism Spectrum Disorder Detection from Videos. HEALTHCOM. INTERNATIONAL CONFERENCE ON E-HEALTH NETWORKING, APPLICATIONS AND SERVICES 2021; 2020:10.1109/healthcom49281.2021.9398924. [PMID: 34693405 PMCID: PMC8528233 DOI: 10.1109/healthcom49281.2021.9398924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Early diagnosis of Autism Spectrum Disorder (ASD) is crucial for best outcomes to interventions. In this paper, we present a machine learning (ML) approach to ASD diagnosis based on identifying specific behaviors from videos of infants of ages 6 through 36 months. The behaviors of interest include directed gaze towards faces or objects of interest, positive affect, and vocalization. The dataset consists of 2000 videos of 3-minute duration with these behaviors manually coded by expert raters. Moreover, the dataset has statistical features including duration and frequency of the above mentioned behaviors in the video collection as well as independent ASD diagnosis by clinicians. We tackle the ML problem in a two-stage approach. Firstly, we develop deep learning models for automatic identification of clinically relevant behaviors exhibited by infants in a one-on-one interaction setting with parents or expert clinicians. We report baseline results of behavior classification using two methods: (1) image based model (2) facial behavior features based model. We achieve 70% accuracy for smile, 68% accuracy for look face, 67% for look object and 53% accuracy for vocalization. Secondly, we focus on ASD diagnosis prediction by applying a feature selection process to identify the most significant statistical behavioral features and a over and under sampling process to mitigate the class imbalance, followed by developing a baseline ML classifier to achieve an accuracy of 82% for ASD diagnosis.
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Affiliation(s)
- Chongruo Wu
- Department of Computer Science, University of California, Davis, CA, US
| | - Sidrah Liaqat
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY, US
| | - Halil Helvaci
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY, US
| | - Sen-Ching Samson Cheung
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY, US
- Department of Electrical and Computer Engineering, University of California, Davis, CA, US
| | - Chen-Nee Chuah
- Department of Electrical and Computer Engineering, University of California, Davis, CA, US
| | - Sally Ozonoff
- UC Davis MIND Institute, University of California, Davis, CA, US
| | - Gregory Young
- UC Davis MIND Institute, University of California, Davis, CA, US
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33
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Hatch B, Iosif AM, Chuang A, de la Paz L, Ozonoff S, Miller M. Longitudinal Differences in Response to Name Among Infants Developing ASD and Risk for ADHD. J Autism Dev Disord 2021; 51:827-836. [PMID: 31974800 PMCID: PMC7375942 DOI: 10.1007/s10803-020-04369-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Diminished response to name, a potential early marker of autism spectrum disorder (ASD), may also indicate risk for other disorders characterized by attention problems, including attention-deficit/hyperactivity disorder (ADHD). Using a familial risk design, we examined whether response to name ability at 6, 12, 18, 24, and 36 months of age differed between three 36-month outcome groups: ASD, ADHD Concerns, or a Comparison group. Persistent differences between the ASD and Comparison groups were evident beginning at 12 months; differences between the ADHD Concerns and Comparison groups were evident between 12 and 18 months only. Results suggest that response to name may be a general marker for ASD and ADHD risk in infancy but a specific indicator of ASD by 24-months.
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Affiliation(s)
- Burt Hatch
- Department of Psychiatry & Behavioral Science, MIND Institute, University of California, Davis, Sacramento, CA, USA.
| | - Ana-Maria Iosif
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, Sacramento, CA, USA
| | - Annie Chuang
- Department of Psychiatry & Behavioral Science, MIND Institute, University of California, Davis, Sacramento, CA, USA
| | - Leiana de la Paz
- Department of Psychiatry & Behavioral Science, MIND Institute, University of California, Davis, Sacramento, CA, USA
| | - Sally Ozonoff
- Department of Psychiatry & Behavioral Science, MIND Institute, University of California, Davis, Sacramento, CA, USA
| | - Meghan Miller
- Department of Psychiatry & Behavioral Science, MIND Institute, University of California, Davis, Sacramento, CA, USA
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34
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Carpenter KLH, Hahemi J, Campbell K, Lippmann SJ, Baker JP, Egger HL, Espinosa S, Vermeer S, Sapiro G, Dawson G. Digital Behavioral Phenotyping Detects Atypical Pattern of Facial Expression in Toddlers with Autism. Autism Res 2021; 14:488-499. [PMID: 32924332 PMCID: PMC7920907 DOI: 10.1002/aur.2391] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 08/16/2020] [Accepted: 08/24/2020] [Indexed: 12/21/2022]
Abstract
Commonly used screening tools for autism spectrum disorder (ASD) generally rely on subjective caregiver questionnaires. While behavioral observation is more objective, it is also expensive, time-consuming, and requires significant expertise to perform. As such, there remains a critical need to develop feasible, scalable, and reliable tools that can characterize ASD risk behaviors. This study assessed the utility of a tablet-based behavioral assessment for eliciting and detecting one type of risk behavior, namely, patterns of facial expression, in 104 toddlers (ASD N = 22) and evaluated whether such patterns differentiated toddlers with and without ASD. The assessment consisted of the child sitting on his/her caregiver's lap and watching brief movies shown on a smart tablet while the embedded camera recorded the child's facial expressions. Computer vision analysis (CVA) automatically detected and tracked facial landmarks, which were used to estimate head position and facial expressions (Positive, Neutral, All Other). Using CVA, specific points throughout the movies were identified that reliably differentiate between children with and without ASD based on their patterns of facial movement and expressions (area under the curves for individual movies ranging from 0.62 to 0.73). During these instances, children with ASD more frequently displayed Neutral expressions compared to children without ASD, who had more All Other expressions. The frequency of All Other expressions was driven by non-ASD children more often displaying raised eyebrows and an open mouth, characteristic of engagement/interest. Preliminary results suggest computational coding of facial movements and expressions via a tablet-based assessment can detect differences in affective expression, one of the early, core features of ASD. LAY SUMMARY: This study tested the use of a tablet in the behavioral assessment of young children with autism. Children watched a series of developmentally appropriate movies and their facial expressions were recorded using the camera embedded in the tablet. Results suggest that computational assessments of facial expressions may be useful in early detection of symptoms of autism.
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Affiliation(s)
- Kimberly L H Carpenter
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jordan Hahemi
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA
| | - Kathleen Campbell
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA
| | - Steven J Lippmann
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jeffrey P Baker
- Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA
| | - Helen L Egger
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- NYU Langone Child Study Center, New York University, New York, New York, USA
| | - Steven Espinosa
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA
| | - Saritha Vermeer
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Guillermo Sapiro
- Departments of Biomedical Engineering Computer Science, and Mathematics, Duke University, Durham, North Carolina, USA
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- Duke Institute for Brain Sciences, Duke University, Durham, North Carolina, USA
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Zhao Z, Zhu Z, Zhang X, Tang H, Xing J, Hu X, Lu J, Peng Q, Qu X. Atypical Head Movement during Face-to-Face Interaction in Children with Autism Spectrum Disorder. Autism Res 2021; 14:1197-1208. [PMID: 33529500 DOI: 10.1002/aur.2478] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 12/30/2020] [Accepted: 01/07/2021] [Indexed: 11/11/2022]
Abstract
The present study implemented an objective head pose tracking technique-OpenFace 2.0 to quantify the three dimensional head movement. Children with autism spectrum disorder (ASD) and typical development (TD) were engaged in a structured conversation with an interlocutress while wearing an eye tracker. We computed the head movement stereotypy with multiscale entropy analysis. In addition, the head rotation range (RR) and the amount of rotation per minute (ARPM) were calculated to quantify the extent of head movement. Results demonstrated that the ASD group had significantly higher level of movement stereotypy, RR and ARPM in all the three directions of head movement. Further analyses revealed that the extent of head movement could be significantly explained by movement stereotypy, but not by the amount of visual fixation to the interlocutress. These results demonstrated the atypical head movement dynamics in children with ASD during live interaction. It is proposed that head movement might potentially provide novel objective biomarkers of ASD. LAY SUMMARY: Our study used an objective tool to quantify head movement in children with autism. Results showed that children with autism had more stereotyped and greater head movement. We suggest that head movement tracking technique be widely used in autism research.
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Affiliation(s)
- Zhong Zhao
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Zhipeng Zhu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Xiaobin Zhang
- Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, China
| | - Haiming Tang
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Jiayi Xing
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Xinyao Hu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Jianping Lu
- Department of Child Psychiatry of Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China
| | - Qiongling Peng
- Developmental Behavioral Pediatric Department, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
| | - Xingda Qu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
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Vivanti G, Messinger DS. Theories of Autism and Autism Treatment from the DSM III Through the Present and Beyond: Impact on Research and Practice. J Autism Dev Disord 2021; 51:4309-4320. [PMID: 33491120 DOI: 10.1007/s10803-021-04887-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/13/2021] [Indexed: 12/14/2022]
Abstract
The purely descriptive definition of autism introduced by the DSM III in 1980 marked a departure from previous DSM editions, which mixed phenomenological descriptions with psychoanalytic theories of etiology. This provided a blank slate upon which a variety of novel theories emerged to conceptualize autism and its treatment in the following four decades. In this article we examine the contribution of these different theoretical orientations with a focus on their impact on research and practice, areas of overlap and conflict between current theories, and their relevance in the context of the evolving landscape of scientific knowledge and societal views of autism.
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Affiliation(s)
- Giacomo Vivanti
- A.J. Drexel Autism Institute, Drexel University, 3020 Market Street, Suite 560, Philadelphia, PA, 19104, USA.
| | - Daniel S Messinger
- Departments of Psychology, Pediatrics, Music Engineering, Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA
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Stewart SL, Celebre A, Iantosca JA, Poss JW. Autism Spectrum Screening Checklist (ASSC): The Development of a Scale to Identify High-Risk Individuals Within the Children's Mental Health System. Front Psychiatry 2021; 12:709491. [PMID: 34552515 PMCID: PMC8451328 DOI: 10.3389/fpsyt.2021.709491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/10/2021] [Indexed: 11/30/2022] Open
Abstract
Autism Spectrum Disorder (ASD) is a complex childhood onset neurodevelopmental disorder that has become the fastest growing developmental disability. Due to the increased demand for diagnostic assessments and subsequent increased wait times, standardized screening as part of regular clinical practice is needed. More specifically, there is an important need for the development of a more streamlined screening tool within an existing assessment system to identify those at greatest risk of having ASD. The current study utilized data from ~17,000 assessments obtained within the province of Ontario, based on the interRAI Child and Youth Mental Health (ChYMH) and Child and Youth Mental Health and Developmental Disability (ChYMH-DD), to develop a scale to identify children who have a higher likelihood of having autism. The scale was then tested on a trial population with data from the interRAI Early Years instrument. Further analyses examined the predictive validity of the scale. The Autism Spectrum Screening Checklist (ASSC) was found to be a good predictor of ASD with a sensitivity of 0.73 and specificity of 0.62, at the recommended cut-point of 2+. The results were consistent across several age ranges, specifically from 2 to 21 years of age. The ASSC scale provides an initial screen to help identify children and youth at heightened risk for autism within larger populations being assessed as part of routine practice. The main goal for the development and implementation of the ASSC scale is to harness the power of the existing interRAI assessment system to provide a more efficient, effective screening and referral process. This will ultimately help improve patient outcomes through needs-based care.
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Affiliation(s)
| | - Angela Celebre
- Faculty of Education, Western University, London, ON, Canada
| | - Jo Ann Iantosca
- Faculty of Applied Arts and Health Sciences, Seneca College, Toronto, ON, Canada
| | - Jeffrey W Poss
- Faculty of Applied Health Sciences, University of Waterloo, Waterloo, ON, Canada
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Germine L, Strong RW, Singh S, Sliwinski MJ. Toward dynamic phenotypes and the scalable measurement of human behavior. Neuropsychopharmacology 2021; 46:209-216. [PMID: 32629456 PMCID: PMC7689489 DOI: 10.1038/s41386-020-0757-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 06/18/2020] [Accepted: 06/25/2020] [Indexed: 12/24/2022]
Abstract
Precision psychiatry demands the rapid, efficient, and temporally dense collection of large scale and multi-omic data across diverse samples, for better diagnosis and treatment of dynamic clinical phenomena. To achieve this, we need approaches for measuring behavior that are readily scalable, both across participants and over time. Efforts to quantify behavior at scale are impeded by the fact that our methods for measuring human behavior are typically developed and validated for single time-point assessment, in highly controlled settings, and with relatively homogeneous samples. As a result, when taken to scale, these measures often suffer from poor reliability, generalizability, and participant engagement. In this review, we attempt to bridge the gap between gold standard behavioral measurements in the lab or clinic and the large-scale, high frequency assessments needed for precision psychiatry. To do this, we introduce and integrate two frameworks for the translation and validation of behavioral measurements. First, borrowing principles from computer science, we lay out an approach for iterative task development that can optimize behavioral measures based on psychometric, accessibility, and engagement criteria. Second, we advocate for a participatory research framework (e.g., citizen science) that can accelerate task development as well as make large-scale behavioral research more equitable and feasible. Finally, we suggest opportunities enabled by scalable behavioral research to move beyond single time-point assessment and toward dynamic models of behavior that more closely match clinical phenomena.
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Affiliation(s)
- Laura Germine
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Roger W Strong
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Shifali Singh
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Martin J Sliwinski
- Center for Healthy Aging, Pennsylvania State University, State College, PA, USA
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Hashemi J, Dawson G, Carpenter KLH, Campbell K, Qiu Q, Espinosa S, Marsan S, Baker JP, Egger HL, Sapiro G. Computer Vision Analysis for Quantification of Autism Risk Behaviors. IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2021; 12:215-226. [PMID: 35401938 PMCID: PMC8993160 DOI: 10.1109/taffc.2018.2868196] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Observational behavior analysis plays a key role for the discovery and evaluation of risk markers for many neurodevelopmental disorders. Research on autism spectrum disorder (ASD) suggests that behavioral risk markers can be observed at 12 months of age or earlier, with diagnosis possible at 18 months. To date, these studies and evaluations involving observational analysis tend to rely heavily on clinical practitioners and specialists who have undergone intensive training to be able to reliably administer carefully designed behavioural-eliciting tasks, code the resulting behaviors, and interpret such behaviors. These methods are therefore extremely expensive, time-intensive, and are not easily scalable for large population or longitudinal observational analysis. We developed a self-contained, closed-loop, mobile application with movie stimuli designed to engage the child's attention and elicit specific behavioral and social responses, which are recorded with a mobile device camera and then analyzed via computer vision algorithms. Here, in addition to presenting this paradigm, we validate the system to measure engagement, name-call responses, and emotional responses of toddlers with and without ASD who were presented with the application. Additionally, we show examples of how the proposed framework can further risk marker research with fine-grained quantification of behaviors. The results suggest these objective and automatic methods can be considered to aid behavioral analysis, and can be suited for objective automatic analysis for future studies.
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Affiliation(s)
- Jordan Hashemi
- Department of Electrical and Computer Engineering, Duke University, Durham, NC
| | - Geraldine Dawson
- Department of Psychiatry and Behavioral Sciences, Duke Center for Autism and Brain Development, and the Duke Institute for Brain Sciences, Durham, NC
| | - Kimberly L H Carpenter
- Department of Psychiatry and Behavioral Sciences, Duke Center for Autism and Brain Development, and the Duke Institute for Brain Sciences, Durham, NC
| | | | - Qiang Qiu
- Department of Electrical and Computer Engineering, Duke University, Durham, NC
| | - Steven Espinosa
- Department of Electrical and Computer Engineering, Duke University, Durham, NC
| | - Samuel Marsan
- Department of Psychiatry and Behavioral Sciences, Durham, NC
| | | | - Helen L Egger
- Department of Child and Adolescent Psychiatry, NYU Langone Health, New York, NY. She performed this work while at Duke University
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC
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40
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Chong E, Clark-Whitney E, Southerland A, Stubbs E, Miller C, Ajodan EL, Silverman MR, Lord C, Rozga A, Jones RM, Rehg JM. Detection of eye contact with deep neural networks is as accurate as human experts. Nat Commun 2020; 11:6386. [PMID: 33318484 PMCID: PMC7736573 DOI: 10.1038/s41467-020-19712-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 10/14/2020] [Indexed: 01/10/2023] Open
Abstract
Eye contact is among the most primary means of social communication used by humans. Quantification of eye contact is valuable as a part of the analysis of social roles and communication skills, and for clinical screening. Estimating a subject's looking direction is a challenging task, but eye contact can be effectively captured by a wearable point-of-view camera which provides a unique viewpoint. While moments of eye contact from this viewpoint can be hand-coded, such a process tends to be laborious and subjective. In this work, we develop a deep neural network model to automatically detect eye contact in egocentric video. It is the first to achieve accuracy equivalent to that of human experts. We train a deep convolutional network using a dataset of 4,339,879 annotated images, consisting of 103 subjects with diverse demographic backgrounds. 57 subjects have a diagnosis of Autism Spectrum Disorder. The network achieves overall precision of 0.936 and recall of 0.943 on 18 validation subjects, and its performance is on par with 10 trained human coders with a mean precision 0.918 and recall 0.946. Our method will be instrumental in gaze behavior analysis by serving as a scalable, objective, and accessible tool for clinicians and researchers.
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Affiliation(s)
- Eunji Chong
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, USA.
| | - Elysha Clark-Whitney
- Center for Autism and the Developing Brain, Weill Cornell Medicine, New York, USA
| | - Audrey Southerland
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, USA
| | - Elizabeth Stubbs
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, USA
| | - Chanel Miller
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, USA
| | - Eliana L Ajodan
- Center for Autism and the Developing Brain, Weill Cornell Medicine, New York, USA
| | - Melanie R Silverman
- Center for Autism and the Developing Brain, Weill Cornell Medicine, New York, USA
| | - Catherine Lord
- School of Medicine, University of California, Los Angeles, USA
| | - Agata Rozga
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, USA
| | - Rebecca M Jones
- Center for Autism and the Developing Brain, Weill Cornell Medicine, New York, USA
| | - James M Rehg
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, USA
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41
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Xu J, Wang X, Feng B, Liu W. Deep multi-metric learning for text-independent speaker verification. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.06.045] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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de Belen RAJ, Bednarz T, Sowmya A, Del Favero D. Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019. Transl Psychiatry 2020; 10:333. [PMID: 32999273 PMCID: PMC7528087 DOI: 10.1038/s41398-020-01015-w] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 09/04/2020] [Accepted: 09/09/2020] [Indexed: 11/29/2022] Open
Abstract
The current state of computer vision methods applied to autism spectrum disorder (ASD) research has not been well established. Increasing evidence suggests that computer vision techniques have a strong impact on autism research. The primary objective of this systematic review is to examine how computer vision analysis has been useful in ASD diagnosis, therapy and autism research in general. A systematic review of publications indexed on PubMed, IEEE Xplore and ACM Digital Library was conducted from 2009 to 2019. Search terms included ['autis*' AND ('computer vision' OR 'behavio* imaging' OR 'behavio* analysis' OR 'affective computing')]. Results are reported according to PRISMA statement. A total of 94 studies are included in the analysis. Eligible papers are categorised based on the potential biological/behavioural markers quantified in each study. Then, different computer vision approaches that were employed in the included papers are described. Different publicly available datasets are also reviewed in order to rapidly familiarise researchers with datasets applicable to their field and to accelerate both new behavioural and technological work on autism research. Finally, future research directions are outlined. The findings in this review suggest that computer vision analysis is useful for the quantification of behavioural/biological markers which can further lead to a more objective analysis in autism research.
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Affiliation(s)
| | - Tomasz Bednarz
- School of Art & Design, University of New South Wales, Sydney, NSW, Australia
| | - Arcot Sowmya
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
| | - Dennis Del Favero
- School of Art & Design, University of New South Wales, Sydney, NSW, Australia
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Tenenbaum EJ, Carpenter KLH, Sabatos-DeVito M, Hashemi J, Vermeer S, Sapiro G, Dawson G. A Six-Minute Measure of Vocalizations in Toddlers with Autism Spectrum Disorder. Autism Res 2020; 13:1373-1382. [PMID: 32212384 PMCID: PMC7881362 DOI: 10.1002/aur.2293] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 09/24/2019] [Accepted: 03/03/2020] [Indexed: 01/08/2023]
Abstract
To improve early identification of autism spectrum disorder (ASD), we need objective, reliable, and accessible measures. To that end, a previous study demonstrated that a tablet-based application (app) that assessed several autism risk behaviors distinguished between toddlers with ASD and non-ASD toddlers. Using vocal data collected during this study, we investigated whether vocalizations uttered during administration of this app can distinguish among toddlers aged 16-31 months with typical development (TD), language or developmental delay (DLD), and ASD. Participant's visual and vocal responses were recorded using the camera and microphone in a tablet while toddlers watched movies designed to elicit behaviors associated with risk for ASD. Vocalizations were then coded offline. Results showed that (a) children with ASD and DLD were less likely to produce words during app administration than TD participants; (b) the ratio of syllabic vocalizations to all vocalizations was higher among TD than ASD or DLD participants; and (c) the rates of nonsyllabic vocalizations were higher in the ASD group than in either the TD or DLD groups. Those producing more nonsyllabic vocalizations were 24 times more likely to be diagnosed with ASD. These results lend support to previous findings that early vocalizations might be useful in identifying risk for ASD in toddlers and demonstrate the feasibility of using a scalable tablet-based app for assessing vocalizations in the context of a routine pediatric visit. LAY SUMMARY: Although parents often report symptoms of autism spectrum disorder (ASD) in infancy, we are not yet reliably diagnosing ASD until much later in development. A previous study tested a tablet-based application (app) that recorded behaviors we know are associated with ASD to help identify children at risk for the disorder. Here we measured how children vocalize while they watched the movies presented on the tablet. Children with ASD were less likely to produce words, less likely to produce speechlike sounds, and more likely to produce atypical sounds while watching these movies. These measures, combined with other behaviors measured by the app, might help identify which children should be evaluated for ASD. Autism Res 2020, 13: 1373-1382. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.
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Affiliation(s)
- Elena J Tenenbaum
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA
| | - Kimberly L H Carpenter
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA
| | - Maura Sabatos-DeVito
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA
| | - Jordan Hashemi
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA
- Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA
| | - Saritha Vermeer
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA
| | - Guillermo Sapiro
- Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
- Department of Computer Science, Duke University, Durham, North Carolina, USA
- Department of Mathematics, Duke University, Durham, North Carolina, USA
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA
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Vargason T, Grivas G, Hollowood-Jones KL, Hahn J. Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements. Semin Pediatr Neurol 2020; 34:100803. [PMID: 32446437 PMCID: PMC7248126 DOI: 10.1016/j.spen.2020.100803] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.
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Affiliation(s)
- Troy Vargason
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Genevieve Grivas
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Kathryn L Hollowood-Jones
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Juergen Hahn
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY; Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY.
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Hagihara H, Ienaga N, Enomoto D, Takahata S, Ishihara H, Noda H, Tsuda K, Terayama K. Computer Vision-Based Approach for Quantifying Occupational Therapists' Qualitative Evaluations of Postural Control. Occup Ther Int 2020; 2020:8542191. [PMID: 32410925 PMCID: PMC7201486 DOI: 10.1155/2020/8542191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 03/03/2020] [Indexed: 12/02/2022] Open
Abstract
This study aimed to leverage computer vision (CV) technology to develop a technique for quantifying postural control. A conventional quantitative index, occupational therapists' qualitative clinical evaluations, and CV-based quantitative indices using an image analysis algorithm were applied to evaluate the postural control of 34 typically developed preschoolers. The effectiveness of the CV-based indices was investigated relative to current methods to explore the clinical applicability of the proposed method. The capacity of the CV-based indices to reflect therapists' qualitative evaluations was confirmed. Furthermore, compared to the conventional quantitative index, the CV-based indices provided more detailed quantitative information with lower costs. CV-based evaluations enable therapists to quantify details of motor performance that are currently observed qualitatively. The development of such precise quantification methods will improve the science and practice of occupational therapy and allow therapists to perform to their full potential.
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Affiliation(s)
- Hiromichi Hagihara
- Graduate School of Human and Environmental Studies, Kyoto University, Kyoto, Japan
- Research Fellow of Japan Society for the Promotion of Science, Tokyo, Japan
| | - Naoto Ienaga
- Research Fellow of Japan Society for the Promotion of Science, Tokyo, Japan
- Graduate School of Science and Technology, Keio University, Yokohama, Japan
| | | | | | | | - Haruka Noda
- Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki, Japan
| | - Koji Tsuda
- Graduate School of Frontier Science, University of Tokyo, Tokyo, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Kei Terayama
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Medical Sciences Innovation Hub Program, RIKEN Cluster for Science, Technology and Innovation Hub, Kanagawa, Japan
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Kowallik AE, Schweinberger SR. Sensor-Based Technology for Social Information Processing in Autism: A Review. SENSORS (BASEL, SWITZERLAND) 2019; 19:E4787. [PMID: 31689906 PMCID: PMC6864871 DOI: 10.3390/s19214787] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 10/29/2019] [Accepted: 10/30/2019] [Indexed: 11/16/2022]
Abstract
The prevalence of autism spectrum disorders (ASD) has increased strongly over the past decades, and so has the demand for adequate behavioral assessment and support for persons affected by ASD. Here we provide a review on original research that used sensor technology for an objective assessment of social behavior, either with the aim to assist the assessment of autism or with the aim to use this technology for intervention and support of people with autism. Considering rapid technological progress, we focus (1) on studies published within the last 10 years (2009-2019), (2) on contact- and irritation-free sensor technology that does not constrain natural movement and interaction, and (3) on sensory input from the face, the voice, or body movements. We conclude that sensor technology has already demonstrated its great potential for improving both behavioral assessment and interventions in autism spectrum disorders. We also discuss selected examples for recent theoretical questions related to the understanding of psychological changes and potentials in autism. In addition to its applied potential, we argue that sensor technology-when implemented by appropriate interdisciplinary teams-may even contribute to such theoretical issues in understanding autism.
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Affiliation(s)
- Andrea E Kowallik
- Early Support and Counselling Center Jena, Herbert Feuchte Stiftungsverbund, 07743 Jena, Germany.
- Social Potential in Autism Research Unit, Friedrich Schiller University, 07743 Jena, Germany.
- Department of General Psychology and Cognitive Neuroscience, Friedrich Schiller University Jena, Am Steiger 3/Haus 1, 07743 Jena, Germany.
| | - Stefan R Schweinberger
- Early Support and Counselling Center Jena, Herbert Feuchte Stiftungsverbund, 07743 Jena, Germany.
- Social Potential in Autism Research Unit, Friedrich Schiller University, 07743 Jena, Germany.
- Department of General Psychology and Cognitive Neuroscience, Friedrich Schiller University Jena, Am Steiger 3/Haus 1, 07743 Jena, Germany.
- Michael Stifel Center Jena for Data-Driven and Simulation Science, Friedrich Schiller University, 07743 Jena, Germany.
- Swiss Center for Affective Science, University of Geneva, 1202 Geneva, Switzerland.
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Computational Analysis of Deep Visual Data for Quantifying Facial Expression Production. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9214542] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The computational analysis of facial expressions is an emerging research topic that could overcome the limitations of human perception and get quick and objective outcomes in the assessment of neurodevelopmental disorders (e.g., Autism Spectrum Disorders, ASD). Unfortunately, there have been only a few attempts to quantify facial expression production and most of the scientific literature aims at the easier task of recognizing if either a facial expression is present or not. Some attempts to face this challenging task exist but they do not provide a comprehensive study based on the comparison between human and automatic outcomes in quantifying children’s ability to produce basic emotions. Furthermore, these works do not exploit the latest solutions in computer vision and machine learning. Finally, they generally focus only on a homogeneous (in terms of cognitive capabilities) group of individuals. To fill this gap, in this paper some advanced computer vision and machine learning strategies are integrated into a framework aimed to computationally analyze how both ASD and typically developing children produce facial expressions. The framework locates and tracks a number of landmarks (virtual electromyography sensors) with the aim of monitoring facial muscle movements involved in facial expression production. The output of these virtual sensors is then fused to model the individual ability to produce facial expressions. Gathered computational outcomes have been correlated with the evaluation provided by psychologists and evidence has been given that shows how the proposed framework could be effectively exploited to deeply analyze the emotional competence of ASD children to produce facial expressions.
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Guthrie W, Wallis K, Bennett A, Brooks E, Dudley J, Gerdes M, Pandey J, Levy SE, Schultz RT, Miller JS. Accuracy of Autism Screening in a Large Pediatric Network. Pediatrics 2019; 144:peds.2018-3963. [PMID: 31562252 DOI: 10.1542/peds.2018-3963] [Citation(s) in RCA: 141] [Impact Index Per Article: 28.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/24/2019] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Universal screening is recommended to reduce the age of diagnosis for autism spectrum disorder (ASD). However, there are insufficient data on children who screen negative and no study of outcomes from truly universal screening. With this study, we filled these gaps by examining the accuracy of universal screening with systematic follow-up through 4 to 8 years. METHODS Universal, primary care-based screening was conducted using the Modified Checklist for Autism in Toddlers with Follow-Up (M-CHAT/F) and supported by electronic administration and integration into electronic health records. All children with a well-child visit (1) between 16 and 26 months, (2) at a Children's Hospital of Philadelphia site after universal electronic screening was initiated, and (3) between January 2011 and July 2015 were included (N = 25 999). RESULTS Nearly universal screening was achieved (91%), and ASD prevalence was 2.2%. Overall, the M-CHAT/F's sensitivity was 38.8%, and its positive predictive value (PPV) was 14.6%. Sensitivity was higher in older toddlers and with repeated screenings, whereas PPV was lower in girls. Finally, the M-CHAT/F's specificity and PPV were lower in children of color and those from lower-income households. CONCLUSIONS Universal screening in primary care is possible when supported by electronic administration. In this "real-world" cohort that was systematically followed, the M-CHAT/F was less accurate in detecting ASD than in previous studies. Disparities in screening rates and accuracy were evident in traditionally underrepresented groups. Future research should focus on the development of new methods that detect a greater proportion of children with ASD and reduce disparities in the screening process.
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Affiliation(s)
- Whitney Guthrie
- Center for Pediatric Clinical Effectiveness, .,Center for Autism Research, and
| | - Kate Wallis
- Division of Developmental and Behavioral Pediatrics.,PolicyLab, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; and
| | | | - Elizabeth Brooks
- PolicyLab, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; and
| | | | - Marsha Gerdes
- PolicyLab, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; and.,Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Juhi Pandey
- Center for Autism Research, and.,Departments of Psychiatry and
| | - Susan E Levy
- Division of Developmental and Behavioral Pediatrics.,Center for Autism Research, and.,Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Robert T Schultz
- Center for Autism Research, and.,Departments of Psychiatry and.,Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Judith S Miller
- Center for Autism Research, and.,Departments of Psychiatry and.,Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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Graciarena M. Cytokines and Chemokines in Novel Roles: Exploring Their Potential as Predictors of Autism Spectrum Disorder. Biol Psychiatry 2019; 86:e11-e12. [PMID: 31370965 DOI: 10.1016/j.biopsych.2019.06.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 06/19/2019] [Indexed: 11/28/2022]
Affiliation(s)
- Mariana Graciarena
- Department of Physiology, Molecular and Cellular Biology, Faculty of Exact and Natural Sciences, University of Buenos Aires, Buenos Aires, Argentina; Institute of Physiology, Molecular Biology, and Neurosciences, National Council of Scientific Research, University of Buenos Aires, Buenos Aires, Argentina.
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Guimarães-Souza EM, Joselevitch C, Britto LRG, Chiavegatto S. Retinal alterations in a pre-clinical model of an autism spectrum disorder. Mol Autism 2019; 10:19. [PMID: 31011411 PMCID: PMC6466731 DOI: 10.1186/s13229-019-0270-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 03/25/2019] [Indexed: 11/22/2022] Open
Abstract
Background Autism spectrum disorders (ASD) affect around 1.5% of people worldwide. Symptoms start around age 2, when children fail to maintain eye contact and to develop speech and other forms of communication. Disturbances in glutamatergic and GABAergic signaling that lead to synaptic changes and alter the balance between excitation and inhibition in the developing brain are consistently found in ASD. One of the hallmarks of these disorders is hypersensitivity to sensory stimuli; however, little is known about its underlying causes. Since the retina is the part of the CNS that converts light into a neuronal signal, we set out to study how it is affected in adolescent mice prenatally exposed to valproic acid (VPA), a useful tool to study ASD endophenotypes. Methods Pregnant female mice received VPA (600 mg/kg, ip) or saline at gestational day 11. Their male adolescent pups (P29–35) were behaviorally tested for anxiety and social interaction. Proteins known to be related with ASD were quantified and visualized in their retinas by immunoassays, and retinal function was assessed by full-field scotopic electroretinograms (ERGs). Results Early adolescent mice prenatally exposed to VPA displayed impaired social interest and increased anxiety-like behaviors consistent with an ASD phenotype. The expression of GABA, GAD, synapsin-1, and FMRP proteins were reduced in their retinas, while mGluR5 was increased. The a-wave amplitudes of VPA-exposed were smaller than those of CTR animals, whereas the b-wave and oscillatory potentials were normal. Conclusions This study establishes that adolescent male mice of the VPA-induced ASD model have alterations in retinal function and protein expression compatible with those found in several brain areas of other autism models. These results support the view that synaptic disturbances with excitatory/inhibitory imbalance early in life are associated with ASD and point to the retina as a window to understand their subjacent mechanisms. Electronic supplementary material The online version of this article (10.1186/s13229-019-0270-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Elisa Maria Guimarães-Souza
- 1Department of Physiology and Biophysics, Biomedical Sciences Institute, University of São Paulo, Av. Prof. Lineu Prestes, 1524, São Paulo, SP 05508-000 Brazil
| | - Christina Joselevitch
- 2Department of Experimental Psychology, Psychology Institute, University of São Paulo, Av. Prof. Mello Moraes, 1721, São Paulo, SP 05508-030 Brazil
| | - Luiz Roberto G Britto
- 1Department of Physiology and Biophysics, Biomedical Sciences Institute, University of São Paulo, Av. Prof. Lineu Prestes, 1524, São Paulo, SP 05508-000 Brazil
| | - Silvana Chiavegatto
- 3Department of Pharmacology, Biomedical Sciences Institute, University of São Paulo, Av. Prof. Lineu Prestes, 1524, São Paulo, SP 05508-000 Brazil.,4Department and Institute of Psychiatry, Clinics Hospital (HCFMUSP), University of São Paulo Medical School, Rua Dr. Ovidio Pires de Campos, 785, São Paulo, SP 05403-903 Brazil
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