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Mandelli V, Landi I, Ceccarelli SB, Molteni M, Nobile M, D'Ausilio A, Fadiga L, Crippa A, Lombardo MV. Enhanced motor noise in an autism subtype with poor motor skills. Mol Autism 2024; 15:36. [PMID: 39228000 PMCID: PMC11370061 DOI: 10.1186/s13229-024-00618-0] [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] [Received: 06/19/2024] [Accepted: 08/27/2024] [Indexed: 09/05/2024] Open
Abstract
BACKGROUND Motor difficulties are common in many, but not all, autistic individuals. These difficulties can co-occur with other problems, such as delays in language, intellectual, and adaptive functioning. Biological mechanisms underpinning such difficulties are less well understood. Poor motor skills tend to be more common in individuals carrying highly penetrant rare genetic mutations. Such mechanisms may have downstream consequences of altering neurophysiological excitation-inhibition balance and lead to enhanced behavioral motor noise. METHODS This study combined publicly available and in-house datasets of autistic (n = 156), typically-developing (TD, n = 149), and developmental coordination disorder (DCD, n = 23) children (age 3-16 years). Autism motor subtypes were identified based on patterns of motor abilities measured from the Movement Assessment Battery for Children 2nd edition. Stability-based relative clustering validation was used to identify autism motor subtypes and evaluate generalization accuracy in held-out data. Autism motor subtypes were tested for differences in motor noise, operationalized as the degree of dissimilarity between repeated motor kinematic trajectories recorded during a simple reach-to-drop task. RESULTS Relatively 'high' (n = 87) versus 'low' (n = 69) autism motor subtypes could be detected and which generalize with 89% accuracy in held-out data. The relatively 'low' subtype was lower in general intellectual ability and older at age of independent walking, but did not differ in age at first words or autistic traits or symptomatology. Motor noise was considerably higher in the 'low' subtype compared to 'high' (Cohen's d = 0.77) or TD children (Cohen's d = 0.85), but similar between autism 'high' and TD children (Cohen's d = 0.08). Enhanced motor noise in the 'low' subtype was also most pronounced during the feedforward phase of reaching actions. LIMITATIONS The sample size of this work is limited. Future work in larger samples along with independent replication is important. Motor noise was measured only on one specific motor task. Thus, a more comprehensive assessment of motor noise on many other motor tasks is needed. CONCLUSIONS Autism can be split into at least two discrete motor subtypes that are characterized by differing levels of motor noise. This suggests that autism motor subtypes may be underpinned by different biological mechanisms.
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Affiliation(s)
- Veronica Mandelli
- Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Isotta Landi
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | | | - Massimo Molteni
- Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Maria Nobile
- Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Alessandro D'Ausilio
- Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy
| | - Luciano Fadiga
- Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy
| | | | - Michael V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy.
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Grazioli S, Crippa A, Buo N, Busti Ceccarelli S, Molteni M, Nobile M, Salandi A, Trabattoni S, Caselli G, Colombo P. Use of Machine Learning Models to Differentiate Neurodevelopment Conditions Through Digitally Collected Data: Cross-Sectional Questionnaire Study. JMIR Form Res 2024; 8:e54577. [PMID: 39073858 PMCID: PMC11319882 DOI: 10.2196/54577] [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: 11/15/2023] [Revised: 03/27/2024] [Accepted: 04/25/2024] [Indexed: 07/30/2024] Open
Abstract
BACKGROUND Diagnosis of child and adolescent psychopathologies involves a multifaceted approach, integrating clinical observations, behavioral assessments, medical history, cognitive testing, and familial context information. Digital technologies, especially internet-based platforms for administering caregiver-rated questionnaires, are increasingly used in this field, particularly during the screening phase. The ascent of digital platforms for data collection has propelled advanced psychopathology classification methods such as supervised machine learning (ML) into the forefront of both research and clinical environments. This shift, recently called psycho-informatics, has been facilitated by gradually incorporating computational devices into clinical workflows. However, an actual integration between telemedicine and the ML approach has yet to be fulfilled. OBJECTIVE Under these premises, exploring the potential of ML applications for analyzing digitally collected data may have significant implications for supporting the clinical practice of diagnosing early psychopathology. The purpose of this study was, therefore, to exploit ML models for the classification of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) using internet-based parent-reported socio-anamnestic data, aiming at obtaining accurate predictive models for new help-seeking families. METHODS In this retrospective, single-center observational study, socio-anamnestic data were collected from 1688 children and adolescents referred for suspected neurodevelopmental conditions. The data included sociodemographic, clinical, environmental, and developmental factors, collected remotely through the first Italian internet-based screening tool for neurodevelopmental disorders, the Medea Information and Clinical Assessment On-Line (MedicalBIT). Random forest (RF), decision tree, and logistic regression models were developed and evaluated using classification accuracy, sensitivity, specificity, and importance of independent variables. RESULTS The RF model demonstrated robust accuracy, achieving 84% (95% CI 82-85; P<.001) for ADHD and 86% (95% CI 84-87; P<.001) for ASD classifications. Sensitivities were also high, with 93% for ADHD and 95% for ASD. In contrast, the DT and LR models exhibited lower accuracy (DT 74%, 95% CI 71-77; P<.001 for ADHD; DT 79%, 95% CI 77-82; P<.001 for ASD; LR 61%, 95% CI 57-64; P<.001 for ADHD; LR 63%, 95% CI 60-67; P<.001 for ASD) and sensitivities (DT: 82% for ADHD and 88% for ASD; LR: 62% for ADHD and 68% for ASD). The independent variables considered for classification differed in importance between the 2 models, reflecting the distinct characteristics of the 3 ML approaches. CONCLUSIONS This study highlights the potential of ML models, particularly RF, in enhancing the diagnostic process of child and adolescent psychopathology. Altogether, the current findings underscore the significance of leveraging digital platforms and computational techniques in the diagnostic process. While interpretability remains crucial, the developed approach might provide valuable screening tools for clinicians, highlighting the significance of embedding computational techniques in the diagnostic process.
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Affiliation(s)
- Silvia Grazioli
- Child Psychopathology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
- Department of Psychology, Sigmund Freud University, Milan, Italy
- Studi Cognitivi, Cognitive Psychotherapy School and Research Centre, Milan, Italy
| | - Alessandro Crippa
- Child Psychopathology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Noemi Buo
- Child Psychopathology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | | | - Massimo Molteni
- Child Psychopathology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Maria Nobile
- Child Psychopathology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Antonio Salandi
- Child Psychopathology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Sara Trabattoni
- Child Psychopathology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Gabriele Caselli
- Department of Psychology, Sigmund Freud University, Milan, Italy
- Studi Cognitivi, Cognitive Psychotherapy School and Research Centre, Milan, Italy
| | - Paola Colombo
- Child Psychopathology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
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Alsharif N, Al-Adhaileh MH, Al-Yaari M, Farhah N, Khan ZI. Utilizing deep learning models in an intelligent eye-tracking system for autism spectrum disorder diagnosis. Front Med (Lausanne) 2024; 11:1436646. [PMID: 39099594 PMCID: PMC11294196 DOI: 10.3389/fmed.2024.1436646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 07/05/2024] [Indexed: 08/06/2024] Open
Abstract
Timely and unbiased evaluation of Autism Spectrum Disorder (ASD) is essential for providing lasting benefits to affected individuals. However, conventional ASD assessment heavily relies on subjective criteria, lacking objectivity. Recent advancements propose the integration of modern processes, including artificial intelligence-based eye-tracking technology, for early ASD assessment. Nonetheless, the current diagnostic procedures for ASD often involve specialized investigations that are both time-consuming and costly, heavily reliant on the proficiency of specialists and employed techniques. To address the pressing need for prompt, efficient, and precise ASD diagnosis, an exploration of sophisticated intelligent techniques capable of automating disease categorization was presented. This study has utilized a freely accessible dataset comprising 547 eye-tracking systems that can be used to scan pathways obtained from 328 characteristically emerging children and 219 children with autism. To counter overfitting, state-of-the-art image resampling approaches to expand the training dataset were employed. Leveraging deep learning algorithms, specifically MobileNet, VGG19, DenseNet169, and a hybrid of MobileNet-VGG19, automated classifiers, that hold promise for enhancing diagnostic precision and effectiveness, was developed. The MobileNet model demonstrated superior performance compared to existing systems, achieving an impressive accuracy of 100%, while the VGG19 model achieved 92% accuracy. These findings demonstrate the potential of eye-tracking data to aid physicians in efficiently and accurately screening for autism. Moreover, the reported results suggest that deep learning approaches outperform existing event detection algorithms, achieving a similar level of accuracy as manual coding. Users and healthcare professionals can utilize these classifiers to enhance the accuracy rate of ASD diagnosis. The development of these automated classifiers based on deep learning algorithms holds promise for enhancing the diagnostic precision and effectiveness of ASD assessment, addressing the pressing need for prompt, efficient, and precise ASD diagnosis.
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Affiliation(s)
- Nizar Alsharif
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
- Department of Computer Engineering and Science, Albaha University, Al Bahah, Saudi Arabia
| | - Mosleh Hmoud Al-Adhaileh
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
- Deanship of E-learning and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Mohammed Al-Yaari
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
- Department of Chemical Engineering, College of Engineering, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Nesren Farhah
- Department of Health Informatics, College of Health Sciences, Saudi Electronic University, Riyadh, Saudi Arabia
| | - Zafar Iqbal Khan
- Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
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D’Anna C, Carlevaro F, Magno F, Vagnetti R, Limone P, Magistro D. Gross Motor Skills Are Associated with Symptoms of Attention Deficit Hyperactivity Disorder in School-Aged Children. CHILDREN (BASEL, SWITZERLAND) 2024; 11:757. [PMID: 39062207 PMCID: PMC11274859 DOI: 10.3390/children11070757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 07/28/2024]
Abstract
Attention deficit hyperactivity disorder (ADHD) is among the most prevalent disorders in children and is frequently linked with motor difficulties that can impact both daily motor tasks and overall developmental trajectories. The objective of this study was to analyse the association between gross motor skills and ADHD symptoms. Using a cross-sectional research design, data were collected from a sample of primary school children (N = 2677; mean age = 8.58 years, SD = 1.49 years). The Gross Motor Development-3 Test (TGMD-3) was employed to assess participants' gross motor skills, whereas the ADHD Rating Scale (SDAI), completed by teachers, evaluated ADHD symptoms through two subscales: inattention and impulsivity/hyperactivity. The results revealed an association between motor development and ADHD symptoms, with greater proficiency in gross motor skills correlating with lower symptoms reported on the SDAI. Logistic regression analyses indicated that the TGMD-3 was significantly associated with the risk of ADHD in matched samples of at-risk children and controls. The evaluation of gross motor development proves to be a useful tool for monitoring global development, paying attention to any critical issues, particularly in relation to the variables of inattention and hyperactivity.
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Affiliation(s)
- Cristiana D’Anna
- Department of Psychology and Education, Pegaso University, 80143 Napoli, Italy;
| | - Fabio Carlevaro
- Polo Universitario Asti Studi Superiori (Uni-Astiss), 14100 Asti, Italy
| | - Francesca Magno
- Polo Universitario Asti Studi Superiori (Uni-Astiss), 14100 Asti, Italy
- Dipartimento di Scienze della Vita e Biologia dei Sistemi, University of Torino, 10124 Torino, Italy
| | - Roberto Vagnetti
- Department of Sport Science, School of Science and Technology, Nottingham Trent University, Nottingham NG1 4FQ, UK
| | - Pierpaolo Limone
- Department of Psychology and Education, Pegaso University, 80143 Napoli, Italy;
| | - Daniele Magistro
- Department of Sport Science, School of Science and Technology, Nottingham Trent University, Nottingham NG1 4FQ, UK
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Gao L, Wang Z, Long Y, Zhang X, Su H, Yu Y, Hong J. Autism spectrum disorders detection based on multi-task transformer neural network. BMC Neurosci 2024; 25:27. [PMID: 38872076 DOI: 10.1186/s12868-024-00870-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 05/01/2024] [Indexed: 06/15/2024] Open
Abstract
Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in social interaction and communication. Identifying ASD patients based on resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising diagnostic tool, but challenging due to the complex and unclear etiology of autism. And it is difficult to effectively identify ASD patients with a single data source (single task). Therefore, to address this challenge, we propose a novel multi-task learning framework for ASD identification based on rs-fMRI data, which can leverage useful information from multiple related tasks to improve the generalization performance of the model. Meanwhile, we adopt an attention mechanism to extract ASD-related features from each rs-fMRI dataset, which can enhance the feature representation and interpretability of the model. The results show that our method outperforms state-of-the-art methods in terms of accuracy, sensitivity and specificity. This work provides a new perspective and solution for ASD identification based on rs-fMRI data using multi-task learning. It also demonstrates the potential and value of machine learning for advancing neuroscience research and clinical practice.
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Affiliation(s)
- Le Gao
- School of Computer Engineering, Guangzhou Huali College, Guangzhou, 511325, China
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, 529000, China
| | - Zhimin Wang
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, 529000, China
| | - Yun Long
- State Key Laboratory of Public Big Data, Guizhou University, Guizhou, 550025, China.
| | - Xin Zhang
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, 529000, China
| | - Hexing Su
- Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, 529000, China
| | - Yong Yu
- School of Computer Science, Shaanxi Normal University, Xi'an, 710062, China
| | - Jin Hong
- School of Information Engineering, Nanchang University, Nanchang, 330031, China.
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Jia SJ, Jing JQ, Yang CJ. A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods. J Autism Dev Disord 2024:10.1007/s10803-024-06429-9. [PMID: 38842671 DOI: 10.1007/s10803-024-06429-9] [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] [Accepted: 05/30/2024] [Indexed: 06/07/2024]
Abstract
PURPOSE With the increasing prevalence of autism spectrum disorders (ASD), the importance of early screening and diagnosis has been subject to considerable discussion. Given the subtle differences between ASD children and typically developing children during the early stages of development, it is imperative to investigate the utilization of automatic recognition methods powered by artificial intelligence. We aim to summarize the research work on this topic and sort out the markers that can be used for identification. METHODS We searched the papers published in the Web of Science, PubMed, Scopus, Medline, SpringerLink, Wiley Online Library, and EBSCO databases from 1st January 2013 to 13th November 2023, and 43 articles were included. RESULTS These articles mainly divided recognition markers into five categories: gaze behaviors, facial expressions, motor movements, voice features, and task performance. Based on the above markers, the accuracy of artificial intelligence screening ranged from 62.13 to 100%, the sensitivity ranged from 69.67 to 100%, the specificity ranged from 54 to 100%. CONCLUSION Therefore, artificial intelligence recognition holds promise as a tool for identifying children with ASD. However, it still needs to continually enhance the screening model and improve accuracy through multimodal screening, thereby facilitating timely intervention and treatment.
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Affiliation(s)
- Si-Jia Jia
- Faculty of Education, East China Normal University, Shanghai, China
| | - Jia-Qi Jing
- Faculty of Education, East China Normal University, Shanghai, China
| | - Chang-Jiang Yang
- Faculty of Education, East China Normal University, Shanghai, China.
- China Research Institute of Care and Education of Infants and Young, Shanghai, China.
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Liu F, Qiu K, Wang H, Dong Y, Yu D. Decreased wrist rotation imitation abilities in children with autism spectrum disorder. Front Psychiatry 2024; 15:1349879. [PMID: 38699453 PMCID: PMC11064792 DOI: 10.3389/fpsyt.2024.1349879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 03/25/2024] [Indexed: 05/05/2024] Open
Abstract
Introduction While meaningless gross motor imitation (GMI) is a common challenge for children diagnosed with autism spectrum disorder (ASD), this topic has not attracted much attention and few appropriate test paradigms have been developed. Methods The current study proposed a wrist rotation imitation (WRI) task (a meaningless GMI assignment), and established a WRI ability evaluation system using low-cost wearable inertial sensors, which acquired the simultaneous data of acceleration and angular acceleration during the WRI task. Three metrics (i.e., total rotation time, rotation amplitude, and symmetry) were extracted from those data of acceleration and angular acceleration, and then were adopted to construct classifiers based on five machine learning (ML) algorithms, including k-nearest neighbors, linear discriminant analysis, naive Bayes, support vector machines, and random forests. To illustrate our technique, this study recruited 49 ASD children (aged 3.5-6.5 years) and 59 age-matched typically developing (TD) children. Results Findings showed that compared with TD children, those with ASD may exhibit shorter total rotation time, lower rotation amplitude, and weaker symmetry. This implies that children with ASD might exhibit decreased WRI abilities. The classifier with the naive Bayes algorithm outperformed than other four algorithms, and achieved a maximal classification accuracy of 88% and a maximal AUC value of 0.91. Two metrics (i.e., rotation amplitude and symmetry) had high correlations with the gross and fine motor skills [evaluated by Gesell Developmental Schedules-Third Edition and Psychoeducational Profile-3 (PEP-3)]. While, the three metrics had no significant correlation with the visual-motor imitation abilities (evaluated by the subdomain of PEP-3) and the ASD symptom severity [evaluated by the Childhood Autism Rating Scale (CARS)] . Discussion The strengths of this study are associated with the low-cost measurement system, correlation between the WRI metrics and clinical measures, decreased WRI abilities in ASD, and high classification accuracy.
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Affiliation(s)
- Fulin Liu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Kai Qiu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Hongan Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yuhong Dong
- Henan Provincial Medical Key Lab of Language Rehabilitation for Children, Sanmenxia Center Hospital, Sanmenxia, Henan, China
| | - Dongchuan Yu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Henan Provincial Medical Key Lab of Child Developmental Behavior and Learning, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Henan Provincial Engineering Research Center of Children’s Digital Rehabilitation, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
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Ghasemzadeh H, Hillman RE, Mehta DD. Toward Generalizable Machine Learning Models in Speech, Language, and Hearing Sciences: Estimating Sample Size and Reducing Overfitting. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2024; 67:753-781. [PMID: 38386017 PMCID: PMC11005022 DOI: 10.1044/2023_jslhr-23-00273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/29/2023] [Accepted: 12/19/2023] [Indexed: 02/23/2024]
Abstract
PURPOSE Many studies using machine learning (ML) in speech, language, and hearing sciences rely upon cross-validations with single data splitting. This study's first purpose is to provide quantitative evidence that would incentivize researchers to instead use the more robust data splitting method of nested k-fold cross-validation. The second purpose is to present methods and MATLAB code to perform power analysis for ML-based analysis during the design of a study. METHOD First, the significant impact of different cross-validations on ML outcomes was demonstrated using real-world clinical data. Then, Monte Carlo simulations were used to quantify the interactions among the employed cross-validation method, the discriminative power of features, the dimensionality of the feature space, the dimensionality of the model, and the sample size. Four different cross-validation methods (single holdout, 10-fold, train-validation-test, and nested 10-fold) were compared based on the statistical power and confidence of the resulting ML models. Distributions of the null and alternative hypotheses were used to determine the minimum required sample size for obtaining a statistically significant outcome (5% significance) with 80% power. Statistical confidence of the model was defined as the probability of correct features being selected for inclusion in the final model. RESULTS ML models generated based on the single holdout method had very low statistical power and confidence, leading to overestimation of classification accuracy. Conversely, the nested 10-fold cross-validation method resulted in the highest statistical confidence and power while also providing an unbiased estimate of accuracy. The required sample size using the single holdout method could be 50% higher than what would be needed if nested k-fold cross-validation were used. Statistical confidence in the model based on nested k-fold cross-validation was as much as four times higher than the confidence obtained with the single holdout-based model. A computational model, MATLAB code, and lookup tables are provided to assist researchers with estimating the minimum sample size needed during study design. CONCLUSION The adoption of nested k-fold cross-validation is critical for unbiased and robust ML studies in the speech, language, and hearing sciences. SUPPLEMENTAL MATERIAL https://doi.org/10.23641/asha.25237045.
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Affiliation(s)
- Hamzeh Ghasemzadeh
- Center for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston
- Department of Surgery, Harvard Medical School, Boston, MA
- Department of Communicative Sciences and Disorders, Michigan State University, East Lansing
| | - Robert E. Hillman
- Center for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston
- Department of Surgery, Harvard Medical School, Boston, MA
- Speech and Hearing Bioscience and Technology, Division of Medical Sciences, Harvard Medical School, Boston, MA
- MGH Institute of Health Professions, Boston, MA
| | - Daryush D. Mehta
- Center for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston
- Department of Surgery, Harvard Medical School, Boston, MA
- Speech and Hearing Bioscience and Technology, Division of Medical Sciences, Harvard Medical School, Boston, MA
- MGH Institute of Health Professions, Boston, MA
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Cox DJ, Jennings AM. The Promises and Possibilities of Artificial Intelligence in the Delivery of Behavior Analytic Services. Behav Anal Pract 2024; 17:123-136. [PMID: 38405282 PMCID: PMC10890993 DOI: 10.1007/s40617-023-00864-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/22/2023] [Indexed: 02/27/2024] Open
Abstract
Artificial intelligence (AI) has begun to affect nearly every aspect of our daily lives and nearly every industry and profession. Many readers of this journal likely work in one or more areas of behavioral health. For readers who work in behavioral health and who are interested in AI, the purpose of this article is to highlight the pervasiveness of AI research being conducted around many facets of behavioral health service delivery. To do this, we first provide a brief overview of some of the areas within AI and the types of problems each area of AI attempts to solve. We then outline the prototypical client journey in behavioral healthcare beginning with diagnosis/assessment and ending with intervention withdrawal or ongoing monitoring. Next, for each stage in the client journey, we highlight several areas that parallel existing behavior analytic practice where researchers have begun to use AI, often to improve the efficiency of service delivery or to learn new things that improve the effectiveness of behavioral health services. Finally, for those whose appetite has been whet for getting involved with AI, we close by describing three roles they might consider trying out and that parallel the three main domains of behavior analysis. These three roles are an AI tool designer (akin to EAB), AI tool implementer (akin to ABA), or AI tool supporter (akin to practice).
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Affiliation(s)
- David J. Cox
- Department of Applied Behavior Analysis, Endicott College, Beverly, MA USA
| | - Adrienne M. Jennings
- Department of Behavioral Science, Daemen University, 4380 Main Street, Amherst, NY USA
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周 刚, 张 晓, 曲 行, 罗 美, 彭 琼, 马 丽, 赵 众. [Differential diagnosis of autism spectrum disorder and global developmental delay based on machine learning and Children Neuropsychological and Behavioral Scale]. ZHONGGUO DANG DAI ER KE ZA ZHI = CHINESE JOURNAL OF CONTEMPORARY PEDIATRICS 2023; 25:1028-1033. [PMID: 37905759 PMCID: PMC10621059 DOI: 10.7499/j.issn.1008-8830.2306024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 08/28/2023] [Indexed: 11/02/2023]
Abstract
OBJECTIVES To investigate the efficacy and required indicators of Children Neuropsychological and Behavioral Scale-Revision 2016 (CNBS-R2016) in the differential diagnosis of autism spectrum disorder (ASD) and global developmental delay (GDD). METHODS A total of 277 children with ASD and 415 children with GDD, aged 18-48 months, were enrolled as subjects. CNBS-R2016 was used to assess the developmental levels of six domains, i.e., gross motor, fine motor, adaptive ability, language, social behavior, and warning behavior, and a total of 13 indicators on intelligence age and developmental quotient (DQ) were obtained as the input features. Five commonly used machine learning classifiers were used for training to calculate the classification accuracy, sensitivity, and specificity of each classifier. RESULTS DQ of warning behavior was selected as the first feature in all five classifiers, and the use of this indicator alone had a classification accuracy of 78.90%. When the DQ of warning behavior was used in combination with the intelligence age of warning behavior, gross motor, and language, it had the highest classification accuracy of 86.71%. CONCLUSIONS Machine learning combined with CNBS-R2016 can effectively distinguish children with ASD from those with GDD. The DQ of warning behavior plays an important role in machine learning, and its combination with other features can improve classification accuracy, providing a basis for the efficient and accurate differential diagnosis of ASD and GDD in clinical practice.
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Cheng Y, Liu L, Gu X, Lu Z, Xia Y, Chen J, Tang L. Graph fusion prediction of autism based on attentional mechanisms. J Biomed Inform 2023; 146:104484. [PMID: 37659698 DOI: 10.1016/j.jbi.2023.104484] [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: 06/06/2023] [Revised: 08/16/2023] [Accepted: 08/31/2023] [Indexed: 09/04/2023]
Abstract
Autism spectrum disorder (ASD) is a pervasive developmental disorder, and the earlier detection and timely intervention for treatment positively affect the prognosis of patients. Deep learning algorithms based on graph structure have achieved good results in autism prediction in recent years. However, there are problems with standardized operations in extracting features and combining neighborhood node features with the structure of the graph dependent, which limits the generalization ability of the trained model to other graph structures. In this paper, we propose a graph fusion autism prediction model based on attentional mechanisms(AGF) to address the above problems. The AGF model represents the overall population (patients or healthy controls) as a sparse graph, where nodes are subjects, and non-imaging features are integrated as edge weights. Different weights can be defined for different nodes in the neighborhood through the attention mechanism without relying on prior knowledge of the graph structure. The model is also able to dynamically fuse multiple sparse graphs obtained from different non-imaging features by way of training weight assignment. Its performance is also compared with several other models (e.g., S-AGF, GCN, etc.), and the results show that it has superior prediction accuracy compared to the baseline model. The results show that this improvement of graph fusion works better on the ABIDE databases, and the classification accuracy can reach 73.9%. The datasets and source code are freely available at https://github.com/chengyu-github1012/Graph-Fusion.git. Strengths and limitations of this study: graph fusion; disease prediction; noise.
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Affiliation(s)
- Yu Cheng
- School of Information, Yunnan Normal University, Yunnan, China
| | - Lin Liu
- School of Information, Yunnan Normal University, Yunnan, China; Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education of Yunnan Province
| | - Xiaoai Gu
- School of Information, Yunnan Normal University, Yunnan, China
| | - Zhonghao Lu
- School of Information, Yunnan Normal University, Yunnan, China
| | - Yujing Xia
- School of Information, Yunnan Normal University, Yunnan, China
| | - Juan Chen
- School of Information, Yunnan Normal University, Yunnan, China
| | - Lin Tang
- Faculty Of Education, Yunnan Normal University, Yunnan, China; Key Laboratory of Educational Information for Nationalities Ministry of Education, Yunnan Normal University, Yunnan, China.
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12
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De Francesco S, Morello L, Fioravanti M, Cassaro C, Grazioli S, Busti Ceccarelli S, Nobile M, Molteni M, Crippa A. A multimodal approach can identify specific motor profiles in autism and attention-deficit/hyperactivity disorder. Autism Res 2023; 16:1550-1560. [PMID: 37530700 DOI: 10.1002/aur.2989] [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] [Received: 03/02/2023] [Accepted: 07/06/2023] [Indexed: 08/03/2023]
Abstract
It is still unclear whether and to what extent the motor difficulties are specific to autism. This study aimed to determine whether a multimodal assessment of motor skills could accurately discriminate autistic children from attention-deficit/hyperactivity disorder (ADHD) and typically developing (TD) peers. Seventy-five children, aged 7-13, equally divided into three groups, were assessed with the developmental coordination disorder questionnaire (DCDQ), the movement assessment battery for children 2 (MABC2), the sensorimotor subtests of NEPSY-II, and the kinematic analysis of a reach-to-drop task. Principal component analysis (PCA) on DCDQ subscales revealed one factor-Caregiver Report-, whereas MABC2/NEPSY-II scores identified three factors-namely, Object Interception and Balance, Motor Imitation, and Fine-Motor Skills-. Lastly, PCA on kinematic variables identified four factors: PC1, loaded by the parameters of velocity and acceleration throughout the task, PC2 and PC3 involved the temporal parameters of the two submovements, and PC4 accounted for the wrist inclination at ball drop. When comparing autistic and TD children, Caregiver Report and Motor Imitation factors predicted membership with 87.2% of accuracy. In the model comparing ADHD and TD groups, Caregiver Report and Fine-Motor Skills predicted membership with an accuracy of 73.5%. In the last model, the Object Interception and Balance factor differentiated autistic children from ADHD with an accuracy of 73.5%. In line with our previous findings, kinematics did not differentiate school-aged autistic children from ADHD and TD peers. The present findings show that specific motor profiles in autism and ADHD can be isolated with a multimodal investigation of motor skills.
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Affiliation(s)
- Stefano De Francesco
- Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
- Department of Psychology, Sigmund Freud University, Milan, Italy
| | - Luisa Morello
- Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
- Department of Psychology, Sigmund Freud University, Milan, Italy
| | - Mariachiara Fioravanti
- Department of Psychology, Sigmund Freud University, Milan, Italy
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
| | - Cristina Cassaro
- Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Silvia Grazioli
- Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
- Department of Psychology, Sigmund Freud University, Milan, Italy
| | | | - Maria Nobile
- Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Massimo Molteni
- Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy
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13
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Chen H, Wang Z, Lu C, Shu F, Chen C, Wang L, Chen W. Neonatal Seizure Detection Using a Wearable Multi-Sensor System. Bioengineering (Basel) 2023; 10:658. [PMID: 37370589 DOI: 10.3390/bioengineering10060658] [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: 03/02/2023] [Revised: 04/27/2023] [Accepted: 05/24/2023] [Indexed: 06/29/2023] Open
Abstract
Neonatal seizure is an important clinical symptom of brain dysfunction, which is more common in infancy than in childhood. At present, video electroencephalogram (VEEG) technology is widely used in clinical practice. However, video electroencephalogram technology has several disadvantages. For example, the wires connecting the medical instruments may interfere with the infant's movement and the gel patch electrode or disk electrode commonly used to monitor EEG may cause skin allergies or even tears. For the above reasons, we developed a wearable multi-sensor platform for newborns to collect physiological and movement signals. In this study, we designed a second-generation multi-sensor platform and developed an automatic detection algorithm for neonatal seizures based on ECG, respiration and acceleration. Data for 38 neonates were recorded at the Children's Hospital of Fudan University in Shanghai. The total recording time was approximately 300 h. Four of the patients had seizures during data collection. The total recording time for the four patients was approximately 34 h, with 30 seizure episodes recorded. These data were evaluated by the algorithm. To evaluate the effectiveness of combining ECG, respiration and movement, we compared the performance of three types of seizure detectors. The first detector included features from ECG, respiration and acceleration records; the second detector incorporated features based on respiratory movement from respiration and acceleration records; and the third detector used only ECG-based features from ECG records. Our study illustrated that, compared with the detector utilizing individual modal features, multi-modal feature detectors could achieve favorable overall performance, reduce false alarm rates and give higher F-measures.
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Affiliation(s)
- Hongyu Chen
- Greater Bay Area Institute of Precision Medicine, Guangzhou 511466, China
| | - Zaihao Wang
- School of Information Science and Technology, Fudan University, Shanghai 200438, China
| | - Chunmei Lu
- National Health Commission Key Laboratory of Neonatal Diseases, Department of Neonatology, Children's Hospital of Fudan University, Shanghai 200433, China
| | - Feng Shu
- Collaborative Innovation Center of Polymers and Polymer Composites, Department of Macromolecular Science, Fudan University, Shanghai 201203, China
| | - Chen Chen
- School of Information Science and Technology, Fudan University, Shanghai 200438, China
| | - Laishuan Wang
- National Health Commission Key Laboratory of Neonatal Diseases, Department of Neonatology, Children's Hospital of Fudan University, Shanghai 200433, China
| | - Wei Chen
- School of Information Science and Technology, Fudan University, Shanghai 200438, China
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14
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Minissi ME, Gómez-Zaragozá L, Marín-Morales J, Mantovani F, Sirera M, Abad L, Cervera-Torres S, Gómez-García S, Chicchi Giglioli IA, Alcañiz M. The whole-body motor skills of children with autism spectrum disorder taking goal-directed actions in virtual reality. Front Psychol 2023; 14:1140731. [PMID: 37089733 PMCID: PMC10117537 DOI: 10.3389/fpsyg.2023.1140731] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/13/2023] [Indexed: 04/08/2023] Open
Abstract
Many symptoms of the autism spectrum disorder (ASD) are evident in early infancy, but ASD is usually diagnosed much later by procedures lacking objective measurements. It is necessary to anticipate the identification of ASD by improving the objectivity of the procedure and the use of ecological settings. In this context, atypical motor skills are reaching consensus as a promising ASD biomarker, regardless of the level of symptom severity. This study aimed to assess differences in the whole-body motor skills between 20 children with ASD and 20 children with typical development during the execution of three tasks resembling regular activities presented in virtual reality. The virtual tasks asked to perform precise and goal-directed actions with different limbs vary in their degree of freedom of movement. Parametric and non-parametric statistical methods were applied to analyze differences in children's motor skills. The findings endorsed the hypothesis that when particular goal-directed movements are required, the type of action could modulate the presence of motor abnormalities in ASD. In particular, the ASD motor abnormalities emerged in the task requiring to take with the upper limbs goal-directed actions with low degree of freedom. The motor abnormalities covered (1) the body part mainly involved in the action, and (2) further body parts not directly involved in the movement. Findings were discussed against the background of atypical prospective control of movements and visuomotor discoordination in ASD. These findings contribute to advance the understanding of motor skills in ASD while deepening ecological and objective assessment procedures based on VR.
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Affiliation(s)
- Maria Eleonora Minissi
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain
| | - Lucía Gómez-Zaragozá
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain
| | - Javier Marín-Morales
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain
| | - Fabrizia Mantovani
- Centre for Studies in Communication Sciences “Luigi Anolli” (CESCOM), Department of Human Sciences for Education “Riccardo Massa”, University of Milano - Bicocca, Milan, Italy
| | - Marian Sirera
- Red Cenit, Centros de Desarrollo Cognitivo, Valencia, Spain
| | - Luis Abad
- Red Cenit, Centros de Desarrollo Cognitivo, Valencia, Spain
| | - Sergio Cervera-Torres
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain
| | - Soledad Gómez-García
- Facultad de Magisterio y Ciencias de la Educación, Universidad Católica de Valencia, Valencia, Spain
| | - Irene Alice Chicchi Giglioli
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain
| | - Mariano Alcañiz
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain
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15
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Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach. J Autism Dev Disord 2023; 53:934-946. [PMID: 35913654 DOI: 10.1007/s10803-022-05685-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2022] [Indexed: 10/16/2022]
Abstract
This study segmented the time series of gaze behavior from nineteen children with autism spectrum disorder (ASD) and 20 children with typical development in a face-to-face conversation. A machine learning approach showed that behavior segments produced by these two groups of participants could be classified with the highest accuracy of 74.15%. These results were further used to classify children using a threshold classifier. A maximum classification accuracy of 87.18% was achieved, under the condition that a participant was considered as 'ASD' if over 46% of the child's 7-s behavior segments were classified as ASD-like behaviors. The idea of combining the behavior segmentation technique and the threshold classifier could maximally preserve participants' data, and promote the automatic screening of ASD.
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16
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Demirci GM, DeIngeniis D, Wong WM, Shereen AD, Nomura Y, Tsai CL. Superstorm Sandy exposure in utero is associated with neurobehavioral phenotypes and brain structure alterations in childhood: A machine learning approach. Front Neurosci 2023; 17:1113927. [PMID: 36816117 PMCID: PMC9932505 DOI: 10.3389/fnins.2023.1113927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 01/12/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction Prenatal maternal stress (PNMS), including exposure to natural disasters, has been shown to serve as a risk factor for future child psychopathology and suboptimal brain development, particularly among brain regions shown to be sensitive to stress and trauma exposure. However, statistical approaches deployed in most studies are usually constrained by a limited number of variables for the sake of statistical power. Explainable machine learning, on the other hand, enables the study of high data dimension and offers novel insights into the prominent subset of behavioral phenotypes and brain regions most susceptible to PNMS. In the present study, we aimed to identify the most important child neurobehavioral and brain features associated with in utero exposure to Superstorm Sandy (SS). Methods By leveraging an explainable machine learning technique, the Shapley additive explanations method, we tested the marginal feature effect on SS exposures and examined the individual variable effects on disaster exposure. Results Results show that certain brain regions are especially sensitive to in utero exposure to SS. Specifically, in utero SS exposure was associated with larger gray matter volume (GMV) in the right caudate, right hippocampus, and left amygdala and smaller GMV in the right parahippocampal gyrus. Additionally, higher aggression scores at age 5 distinctly correlated with SS exposure. Discussion These findings suggest in utero SS exposure may be associated with greater aggression and suboptimal developmental alterations among various limbic and basal ganglia brain regions.
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Affiliation(s)
- Gozde M. Demirci
- The Graduate Center, City University of New York, New York, NY, United States
| | - Donato DeIngeniis
- Queens College, City University of New York, New York, NY, United States
| | - Wai Man Wong
- The Graduate Center, City University of New York, New York, NY, United States
- Queens College, City University of New York, New York, NY, United States
| | - A. Duke Shereen
- The Graduate Center, City University of New York, New York, NY, United States
| | - Yoko Nomura
- The Graduate Center, City University of New York, New York, NY, United States
- Queens College, City University of New York, New York, NY, United States
| | - Chia-Ling Tsai
- The Graduate Center, City University of New York, New York, NY, United States
- Queens College, City University of New York, New York, NY, United States
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17
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Koehler JC, Falter-Wagner CM. Digitally assisted diagnostics of autism spectrum disorder. Front Psychiatry 2023; 14:1066284. [PMID: 36816410 PMCID: PMC9928948 DOI: 10.3389/fpsyt.2023.1066284] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 01/11/2023] [Indexed: 02/04/2023] Open
Abstract
Digital technologies have the potential to support psychiatric diagnostics and, in particular, differential diagnostics of autism spectrum disorder in the near future, making clinical decisions more objective, reliable and evidence-based while reducing clinical resources. Multimodal automatized measurement of symptoms at cognitive, behavioral, and neuronal levels combined with artificial intelligence applications offer promising strides toward personalized prognostics and treatment strategies. In addition, these new technologies could enable systematic and continuous assessment of longitudinal symptom development, beyond the usual scope of clinical practice. Early recognition of exacerbation and simplified, as well as detailed, progression control would become possible. Ultimately, digitally assisted diagnostics will advance early recognition. Nonetheless, digital technologies cannot and should not substitute clinical decision making that takes the comprehensive complexity of individual longitudinal and cross-section presentation of autism spectrum disorder into account. Yet, they might aid the clinician by objectifying decision processes and provide a welcome relief to resources in the clinical setting.
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Affiliation(s)
- Jana Christina Koehler
- Department of Psychiatry and Psychotherapy, Medical Faculty, LMU Munich, Munich, Germany
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18
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Machine Learning-Based Blood RNA Signature for Diagnosis of Autism Spectrum Disorder. Int J Mol Sci 2023; 24:ijms24032082. [PMID: 36768401 PMCID: PMC9916487 DOI: 10.3390/ijms24032082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/15/2023] [Accepted: 01/17/2023] [Indexed: 01/21/2023] Open
Abstract
Early diagnosis of autism spectrum disorder (ASD) is crucial for providing appropriate treatments and parental guidance from an early age. Yet, ASD diagnosis is a lengthy process, in part due to the lack of reliable biomarkers. We recently applied RNA-sequencing of peripheral blood samples from 73 American and Israeli children with ASD and 26 neurotypically developing (NT) children to identify 10 genes with dysregulated blood expression levels in children with ASD. Machine learning (ML) analyzes data by computerized analytical model building and may be applied to building diagnostic tools based on the optimization of large datasets. Here, we present several ML-generated models, based on RNA expression datasets collected during our recently published RNA-seq study, as tentative tools for ASD diagnosis. Using the random forest classifier, two of our proposed models yield an accuracy of 82% in distinguishing children with ASD and NT children. Our proof-of-concept study requires refinement and independent validation by studies with far larger cohorts of children with ASD and NT children and should thus be perceived as starting point for building more accurate ML-based tools. Eventually, such tools may potentially provide an unbiased means to support the early diagnosis of ASD.
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19
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Zhu FL, Wang SH, Liu WB, Zhu HL, Li M, Zou XB. A multimodal machine learning system in early screening for toddlers with autism spectrum disorders based on the response to name. Front Psychiatry 2023; 14:1039293. [PMID: 36778637 PMCID: PMC9909188 DOI: 10.3389/fpsyt.2023.1039293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 01/05/2023] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Reduced or absence of the response to name (RTN) has been widely reported as an early specific indicator for autism spectrum disorder (ASD), while few studies have quantified the RTN of toddlers with ASD in an automatic way. The present study aims to apply a multimodal machine learning system (MMLS) in early screening for toddlers with ASD based on the RTN. METHODS A total of 125 toddlers were recruited, including ASD (n = 61), developmental delay (DD, n = 31), and typical developmental (TD, n = 33). Procedures of RTN were, respectively, performed by the evaluator and caregiver. Behavioral data were collected by eight-definition tripod-mounted cameras and coded by the MMLS. Response score, response time, and response duration time were accurately calculated to evaluate RTN. RESULTS Total accuracy of RTN scores rated by computers was 0.92. In both evaluator and caregiver procedures, toddlers with ASD had significant differences in response score, response time, and response duration time, compared to toddlers with DD and TD (all P-values < 0.05). The area under the curve (AUC) was 0.81 for the computer-rated results, and the AUC was 0.91 for the human-rated results. The accuracy in the identification of ASD based on the computer- and human-rated results was, respectively, 74.8 and 82.9%. There was a significant difference between the AUC of the human-rated results and computer-rated results (Z = 2.71, P-value = 0.007). CONCLUSION The multimodal machine learning system can accurately quantify behaviors in RTN procedures and may effectively distinguish toddlers with ASD from the non-ASD group. This novel system may provide a low-cost approach to early screening and identifying toddlers with ASD. However, machine learning is not as accurate as a human observer, and the detection of a single symptom like RTN is not sufficient enough to detect ASD.
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Affiliation(s)
- Feng-Lei Zhu
- Child Developmental and Behavioral Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shi-Huan Wang
- Child Developmental and Behavioral Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Wen-Bo Liu
- School of Electronics and Information Technology, Guangzhou Higher Education Mega Center, Sun Yat-sen University, Guangzhou, China
| | - Hui-Lin Zhu
- Child Developmental and Behavioral Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ming Li
- Data Science Research Center, Duke Kunshan University, Kunshan, China
| | - Xiao-Bing Zou
- Child Developmental and Behavioral Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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20
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Albahri AS, Hamid RA, Zaidan AA, Albahri OS. Early automated prediction model for the diagnosis and detection of children with autism spectrum disorders based on effective sociodemographic and family characteristic features. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07822-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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21
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Xie J, Wang L, Webster P, Yao Y, Sun J, Wang S, Zhou H. Identifying Visual Attention Features Accurately Discerning Between Autism and Typically Developing: a Deep Learning Framework. Interdiscip Sci 2022; 14:639-651. [PMID: 35415827 DOI: 10.1007/s12539-022-00510-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 03/10/2022] [Accepted: 03/15/2022] [Indexed: 06/14/2023]
Abstract
Atypical visual attention is a hallmark of autism spectrum disorder (ASD). Identifying the attention features accurately discerning between people with ASD and typically developing (TD) at the individual level remains a challenge. In this study, we developed a new systematic framework combining high accuracy deep learning classification, deep learning segmentation, image ablation and a direct measurement of classification ability to identify the discriminative features for autism identification. Our two-stream model achieved the state-of-the-art performance with a classification accuracy of 0.95. Using this framework, two new categories of features, Food & drink and Outdoor-objects, were identified as discriminative attention features, in addition to the previously reported features including Center-object and Human-faces, etc. Altered attention to the new categories helps to understand related atypical behaviors in ASD. Importantly, the area under curve (AUC) based on the combined top-9 features identified in this study was 0.92, allowing an accurate classification at the individual level. We also obtained a small but informative dataset of 12 images with an AUC of 0.86, suggesting a potentially efficient approach for the clinical diagnosis of ASD. Together, our deep learning framework based on VGG-16 provides a novel and powerful tool to recognize and understand abnormal visual attention in ASD, which will, in turn, facilitate the identification of biomarkers for ASD.
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Affiliation(s)
- Jin Xie
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Longfei Wang
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China
| | - Paula Webster
- Department of Chemical and Biomedical Engineering and Rockefeller Neuroscience Institute, West Virginia University, Morgantown, WV, 26506, USA
| | - Yang Yao
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China
| | - Jiayao Sun
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shuo Wang
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, 63130, USA.
| | - Huihui Zhou
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China.
- The Research Center for Artificial Intelligence, Peng Cheng Laboratory, No. 2 Xingke First Street, Nanshan District, Shenzhen, 518000, China.
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22
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Sleiman E, Mutlu OC, Surabhi S, Husic A, Kline A, Washington P, Wall DP. Deep Learning-Based Autism Spectrum Disorder Detection Using Emotion Features From Video Recordings (Preprint). JMIR BIOMEDICAL ENGINEERING 2022. [DOI: 10.2196/39982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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23
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Cho AB, Otte K, Baskow I, Ehlen F, Maslahati T, Mansow-Model S, Schmitz-Hübsch T, Behnia B, Roepke S. Motor signature of autism spectrum disorder in adults without intellectual impairment. Sci Rep 2022; 12:7670. [PMID: 35538115 PMCID: PMC9090847 DOI: 10.1038/s41598-022-10760-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 04/12/2022] [Indexed: 12/28/2022] Open
Abstract
Motor signs such as dyspraxia and abnormal gait are characteristic features of autism spectrum disorder (ASD). However, motor behavior in adults with ASD has scarcely been quantitatively characterized. In this pilot study, we aim to quantitatively examine motor signature of adults with ASD without intellectual impairment using marker-less visual-perceptive motion capture. 82 individuals (37 ASD and 45 healthy controls, HC) with an IQ > 85 and aged 18 to 65 years performed nine movement tasks and were filmed by a 3D-infrared camera. Anatomical models were quantified via custom-made software and resulting kinematic parameters were compared between individuals with ASD and HCs. Furthermore, the association between specific motor behaviour and severity of autistic symptoms (Autism Diagnostic Observation Schedule 2, Autism Spectrum Quotient) was explored. Adults with ASD showed a greater mediolateral deviation while walking, greater sway during normal, tandem and single leg stance, a reduced walking speed and cadence, a greater arrhythmicity during jumping jack tasks and an impaired manual dexterity during finger tapping tasks (p < 0.05 and |D|> 0.48) compared to HC. Furthermore, in the ASD group, some of these parameters correlated moderately to severity of ASD symptoms. Adults with ASD seem to display a specific motor signature in this disorder affecting movement timing and aspects of balance. The data appear to reinforce knowledge about motor signs reported in children and adolescents with ASD. Also, quantitative motor assessment via visual-perceptive computing may be a feasible instrument to detect subtle motor signs in ASD and perhaps suitable in the diagnosis of ASD in the future.
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Affiliation(s)
- An Bin Cho
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Karen Otte
- Motognosis GmbH, Schönhauser Allee 177, 10119, Berlin, Germany
- Experimental and Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Lindenberger Weg 80, 13125, Berlin, Germany
- Experimental and Clinical Research Center, a cooperation between the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association and the Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine, Helmholtz Association (MDC), Robert-Rössle-Straße 10, 13125, Berlin, Germany
| | - Irina Baskow
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Felicitas Ehlen
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Jüdisches Krankenhaus Berlin, Heinz-Galinski-Str. 1, 13347, Berlin, Germany
| | - Tolou Maslahati
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
| | | | - Tanja Schmitz-Hübsch
- Experimental and Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Lindenberger Weg 80, 13125, Berlin, Germany
- Experimental and Clinical Research Center, a cooperation between the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association and the Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine, Helmholtz Association (MDC), Robert-Rössle-Straße 10, 13125, Berlin, Germany
- NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität Zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Behnoush Behnia
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Stefan Roepke
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
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24
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Using a small dataset to classify strength-interactions with an elastic display: a case study for the screening of autism spectrum disorder. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01554-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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25
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Chi NA, Washington P, Kline A, Husic A, Hou C, He C, Dunlap K, Wall DP. Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study. JMIR Pediatr Parent 2022; 5:e35406. [PMID: 35436234 PMCID: PMC9052034 DOI: 10.2196/35406] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/18/2022] [Accepted: 01/25/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a neurodevelopmental disorder that results in altered behavior, social development, and communication patterns. In recent years, autism prevalence has tripled, with 1 in 44 children now affected. Given that traditional diagnosis is a lengthy, labor-intensive process that requires the work of trained physicians, significant attention has been given to developing systems that automatically detect autism. We work toward this goal by analyzing audio data, as prosody abnormalities are a signal of autism, with affected children displaying speech idiosyncrasies such as echolalia, monotonous intonation, atypical pitch, and irregular linguistic stress patterns. OBJECTIVE We aimed to test the ability for machine learning approaches to aid in detection of autism in self-recorded speech audio captured from children with ASD and neurotypical (NT) children in their home environments. METHODS We considered three methods to detect autism in child speech: (1) random forests trained on extracted audio features (including Mel-frequency cepstral coefficients); (2) convolutional neural networks trained on spectrograms; and (3) fine-tuned wav2vec 2.0-a state-of-the-art transformer-based speech recognition model. We trained our classifiers on our novel data set of cellphone-recorded child speech audio curated from the Guess What? mobile game, an app designed to crowdsource videos of children with ASD and NT children in a natural home environment. RESULTS The random forest classifier achieved 70% accuracy, the fine-tuned wav2vec 2.0 model achieved 77% accuracy, and the convolutional neural network achieved 79% accuracy when classifying children's audio as either ASD or NT. We used 5-fold cross-validation to evaluate model performance. CONCLUSIONS Our models were able to predict autism status when trained on a varied selection of home audio clips with inconsistent recording qualities, which may be more representative of real-world conditions. The results demonstrate that machine learning methods offer promise in detecting autism automatically from speech without specialized equipment.
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Affiliation(s)
- Nathan A Chi
- Division of Systems Medicine, Department of Pediatrics, Stanford University, Palo Alto, CA, United States
| | - Peter Washington
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Aaron Kline
- Division of Systems Medicine, Department of Pediatrics, Stanford University, Palo Alto, CA, United States
| | - Arman Husic
- Division of Systems Medicine, Department of Pediatrics, Stanford University, Palo Alto, CA, United States
| | - Cathy Hou
- Department of Computer Science, Stanford University, Stanford, CA, United States
| | - Chloe He
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Kaitlyn Dunlap
- Division of Systems Medicine, Department of Pediatrics, Stanford University, Palo Alto, CA, United States
| | - Dennis P Wall
- Division of Systems Medicine, Department of Pediatrics, Stanford University, Palo Alto, CA, United States
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
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26
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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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27
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Lu SC, Rowe P, Tachtatzis C, Andonovic I, Anzulewicz A, Sobota K, Delafield-Butt J. Swipe kinematic differences in young children with autism spectrum disorders are task- and age-dependent: A smart tablet game approach. BRAIN DISORDERS 2022. [DOI: 10.1016/j.dscb.2022.100032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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28
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Liu W, Li M, Zou X, Raj B. Discriminative Dictionary Learning for Autism Spectrum Disorder Identification. Front Comput Neurosci 2021; 15:662401. [PMID: 34819846 PMCID: PMC8606656 DOI: 10.3389/fncom.2021.662401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 09/20/2021] [Indexed: 12/02/2022] Open
Abstract
Autism Spectrum Disorder (ASD) is a group of lifelong neurodevelopmental disorders with complicated causes. A key symptom of ASD patients is their impaired interpersonal communication ability. Recent study shows that face scanning patterns of individuals with ASD are often different from those of typical developing (TD) ones. Such abnormality motivates us to study the feasibility of identifying ASD children based on their face scanning patterns with machine learning methods. In this paper, we consider using the bag-of-words (BoW) model to encode the face scanning patterns, and propose a novel dictionary learning method based on dual mode seeking for better BoW representation. Unlike k-means which is broadly used in conventional BoW models to learn dictionaries, the proposed method captures discriminative information by finding atoms which maximizes both the purity and coverage of belonging samples within one class. Compared to the rich literature of ASD studies from psychology and neural science, our work marks one of the relatively few attempts to directly identify high-functioning ASD children with machine learning methods. Experiments demonstrate the superior performance of our method with considerable gain over several baselines. Although the proposed work is yet too preliminary to directly replace existing autism diagnostic observation schedules in the clinical practice, it shed light on future applications of machine learning methods in early screening of ASD.
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Affiliation(s)
- Wenbo Liu
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China
| | - Ming Li
- Data Science Research Center, Duke Kunshan University, Suzhou, China
- School of Computer Science, Wuhan University, Wuhan, China
| | - Xiaobing Zou
- The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Bhiksha Raj
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
- Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, United States
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Stenum J, Cherry-Allen KM, Pyles CO, Reetzke RD, Vignos MF, Roemmich RT. Applications of Pose Estimation in Human Health and Performance across the Lifespan. SENSORS (BASEL, SWITZERLAND) 2021; 21:7315. [PMID: 34770620 PMCID: PMC8588262 DOI: 10.3390/s21217315] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/29/2021] [Accepted: 10/31/2021] [Indexed: 01/15/2023]
Abstract
The emergence of pose estimation algorithms represents a potential paradigm shift in the study and assessment of human movement. Human pose estimation algorithms leverage advances in computer vision to track human movement automatically from simple videos recorded using common household devices with relatively low-cost cameras (e.g., smartphones, tablets, laptop computers). In our view, these technologies offer clear and exciting potential to make measurement of human movement substantially more accessible; for example, a clinician could perform a quantitative motor assessment directly in a patient's home, a researcher without access to expensive motion capture equipment could analyze movement kinematics using a smartphone video, and a coach could evaluate player performance with video recordings directly from the field. In this review, we combine expertise and perspectives from physical therapy, speech-language pathology, movement science, and engineering to provide insight into applications of pose estimation in human health and performance. We focus specifically on applications in areas of human development, performance optimization, injury prevention, and motor assessment of persons with neurologic damage or disease. We review relevant literature, share interdisciplinary viewpoints on future applications of these technologies to improve human health and performance, and discuss perceived limitations.
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Affiliation(s)
- Jan Stenum
- Center for Movement Studies, Kennedy Krieger Institute, Baltimore, MD 21205, USA;
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA;
| | - Kendra M. Cherry-Allen
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA;
| | - Connor O. Pyles
- Johns Hopkins Applied Physics Laboratory, Laurel, MD 20723, USA; (C.O.P.); (M.F.V.)
| | - Rachel D. Reetzke
- Center for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, MD 21211, USA;
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Michael F. Vignos
- Johns Hopkins Applied Physics Laboratory, Laurel, MD 20723, USA; (C.O.P.); (M.F.V.)
| | - Ryan T. Roemmich
- Center for Movement Studies, Kennedy Krieger Institute, Baltimore, MD 21205, USA;
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA;
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Zhao Z, Tang H, Zhang X, Qu X, Hu X, Lu J. Classification of Children With Autism and Typical Development Using Eye-Tracking Data From Face-to-Face Conversations: Machine Learning Model Development and Performance Evaluation. J Med Internet Res 2021; 23:e29328. [PMID: 34435957 PMCID: PMC8440949 DOI: 10.2196/29328] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/30/2021] [Accepted: 07/05/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Previous studies have shown promising results in identifying individuals with autism spectrum disorder (ASD) by applying machine learning (ML) to eye-tracking data collected while participants viewed varying images (ie, pictures, videos, and web pages). Although gaze behavior is known to differ between face-to-face interaction and image-viewing tasks, no study has investigated whether eye-tracking data from face-to-face conversations can also accurately identify individuals with ASD. OBJECTIVE The objective of this study was to examine whether eye-tracking data from face-to-face conversations could classify children with ASD and typical development (TD). We further investigated whether combining features on visual fixation and length of conversation would achieve better classification performance. METHODS Eye tracking was performed on children with ASD and TD while they were engaged in face-to-face conversations (including 4 conversational sessions) with an interviewer. By implementing forward feature selection, four ML classifiers were used to determine the maximum classification accuracy and the corresponding features: support vector machine (SVM), linear discriminant analysis, decision tree, and random forest. RESULTS A maximum classification accuracy of 92.31% was achieved with the SVM classifier by combining features on both visual fixation and session length. The classification accuracy of combined features was higher than that obtained using visual fixation features (maximum classification accuracy 84.62%) or session length (maximum classification accuracy 84.62%) alone. CONCLUSIONS Eye-tracking data from face-to-face conversations could accurately classify children with ASD and TD, suggesting that ASD might be objectively screened in everyday social interactions. However, these results will need to be validated with a larger sample of individuals with ASD (varying in severity and balanced sex ratio) using data collected from different modalities (eg, eye tracking, kinematic, electroencephalogram, and neuroimaging). In addition, individuals with other clinical conditions (eg, developmental delay and attention deficit hyperactivity disorder) should be included in similar ML studies for detecting ASD.
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Affiliation(s)
- Zhong Zhao
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Haiming Tang
- 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
| | - Xingda Qu
- 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
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31
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Augmented Reality for Autistic Children to Enhance Their Understanding of Facial Expressions. MULTIMODAL TECHNOLOGIES AND INTERACTION 2021. [DOI: 10.3390/mti5080048] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Difficulty in understanding the feelings and behavior of other people is considered one of the main symptoms of autism. Computer technology has increasingly been used in interventions with Autism Spectrum Disorder (ASD), especially augmented reality, to either treat or alleviate ASD symptomatology. Augmented reality is an engaging type of technology that helps children interact easily and understand and remember information, and it is not limited to one age group or level of education. This study utilized AR to display faces with six different basic facial expressions—happiness, sadness, surprise, fear, disgust, and anger—to help children to recognize facial features and associate facial expressions with a simultaneous human condition. The most important point of this system is that children can interact with the system in a friendly and safe way. Additionally, our results showed the system enhanced social interactions, talking, and facial expressions for both autistic and typical children. Therefore, AR might have a significant upcoming role in talking about the therapeutic necessities of children with ASD. This paper presents evidence for the feasibility of one of the specialized AR systems.
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32
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Squarcina L, Nosari G, Marin R, Castellani U, Bellani M, Bonivento C, Fabbro F, Molteni M, Brambilla P. Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine. Brain Behav 2021; 11:e2238. [PMID: 34264004 PMCID: PMC8413814 DOI: 10.1002/brb3.2238] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 05/10/2021] [Accepted: 05/23/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE Autism spectrum disorder (ASD) is a neurodevelopmental condition with a heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; cortical thickness has proved to be involved in the etiopathogenesis of ASD core symptoms. We apply support vector machine, a supervised machine learning method, in order to identify specific cortical thickness alterations in ASD subjects. METHODS A sample of 76 subjects (9.5 ± 3.4 years old) has been selected, 40 diagnosed with ASD and 36 typically developed subjects. All children underwent a magnetic resonance imaging (MRI) examination; T1-MPRAGE sequences were analyzed to extract features for the characterization and parcellation of regions of interests (ROI); average cortical thickness (CT) has been measured for each ROI. For the classification process, the extracted features were used as input for a classifier to identify ASD subjects through a "learning by example" procedure; the features with best performance was then selected by "greedy forward-feature selection." Finally, this model underwent a leave-one-out cross-validation approach. RESULTS From the training set of 68 ROIs, five ROIs reached accuracies of over 70%. After this phase, we used a recursive feature selection process in order to identify the eight features with the best accuracy (84.2%). CT resulted higher in ASD compared to controls in all the ROIs identified at the end of the process. CONCLUSION We found increased CT in various brain regions in ASD subjects, confirming their role in the pathogenesis of this condition. Considering the brain development curve during ages, these changes in CT may normalize during development. Further validation on a larger sample is required.
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Affiliation(s)
- Letizia Squarcina
- Department of Pathophysiology and TransplantationUniversity of MilanVia Festa del Perdono, 7, 20122 MilanItaly
| | - Guido Nosari
- Department of Pathophysiology and TransplantationUniversity of MilanVia Festa del Perdono, 7, 20122 MilanItaly
| | - Riccardo Marin
- Department of InformaticsUniversity of VeronaVeronaItaly
| | | | - Marcella Bellani
- Department of NeurosciencesBiomedicine and Movement SciencesSection of PsychiatryUniversity of VeronaVeronaItaly
| | - Carolina Bonivento
- IRCCS “E. Medea”, Polo Friuli Venezia GiuliaSan Vito al Tagliamento (PN)Italy
| | | | - Massimo Molteni
- IRCCS “E. Medea”, Polo Friuli Venezia GiuliaSan Vito al Tagliamento (PN)Italy
| | - Paolo Brambilla
- Department of Pathophysiology and TransplantationUniversity of MilanVia Festa del Perdono, 7, 20122 MilanItaly
- Department of Neurosciences and Mental Health Department of Neurosciences and Mental HealthFondazione IRCCS Ca' Granda Ospedale Maggiore Policlinicovia Francesco Sforza 28, 20122 MilanItaly
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33
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Cavallo A, Foster NC, Balasubramanian KK, Merello A, Zini G, Crepaldi M, Becchio C. A low-cost stand-alone platform for measuring motor behavior across developmental applications. iScience 2021; 24:102742. [PMID: 34258565 PMCID: PMC8258968 DOI: 10.1016/j.isci.2021.102742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 05/13/2021] [Accepted: 06/15/2021] [Indexed: 12/01/2022] Open
Abstract
Motion tracking provides unique insights into motor, cognitive, and social development by capturing subtle variations into how movements are planned and controlled. Here, we present a low-cost, wearable movement measurement platform, KiD, specifically designed for tracking the movements of infants and children in a variety of natural settings. KiD consists of a small, lightweight sensor containing a nine-axis inertial measurement unit plus an integrated processor for computing rotations. Measurements of three-dimensional acceleration using KiD compare well with those of current state-of-the-art optical motion capture systems. As a proof of concept, we demonstrate successful classification of different types of sinusoidal right arm movements using KiD.
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Affiliation(s)
- Andrea Cavallo
- Cognition, Motion and Neuroscience Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy.,Department of Psychology, University of Turin, Torino, Italy
| | - Nathan C Foster
- Cognition, Motion and Neuroscience Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy
| | | | - Andrea Merello
- Electronic Design Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy
| | - Giorgio Zini
- Electronic Design Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy
| | - Marco Crepaldi
- Electronic Design Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy
| | - Cristina Becchio
- Cognition, Motion and Neuroscience Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy
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Zhao Z, Zhu Z, Zhang X, Tang H, Xing J, Hu X, Lu J, Qu X. Identifying Autism with Head Movement Features by Implementing Machine Learning Algorithms. J Autism Dev Disord 2021; 52:3038-3049. [PMID: 34250557 DOI: 10.1007/s10803-021-05179-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2021] [Indexed: 11/27/2022]
Abstract
Our study investigated the feasibility of using head movement features to identify individuals with autism spectrum disorder (ASD). Children with ASD and typical development (TD) were required to answer ten yes-no questions, and they were encouraged to nod/shake head while doing so. The head rotation range (RR) and the amount of rotation per minute (ARPM) in the pitch (head nodding direction), yaw (head shaking direction) and roll (lateral head inclination) directions were computed, and further fed into machine learning classifiers as the input features. The maximum classification accuracy of 92.11% was achieved with the decision tree classifier with two features (i.e., RR_Pitch and ARPM_Yaw). Our study suggests that head movement dynamics contain objective biomarkers that could identify ASD.
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Affiliation(s)
- Zhong Zhao
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China
| | - Zhipeng Zhu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, 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, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China
| | - Jiayi Xing
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China
| | - Xinyao Hu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China
| | - Jianping Lu
- Department of Child Psychiatry of Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China
| | - Xingda Qu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China.
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Assessment of the Autism Spectrum Disorder Based on Machine Learning and Social Visual Attention: A Systematic Review. J Autism Dev Disord 2021; 52:2187-2202. [PMID: 34101081 PMCID: PMC9021060 DOI: 10.1007/s10803-021-05106-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2021] [Indexed: 10/25/2022]
Abstract
The assessment of autism spectrum disorder (ASD) is based on semi-structured procedures addressed to children and caregivers. Such methods rely on the evaluation of behavioural symptoms rather than on the objective evaluation of psychophysiological underpinnings. Advances in research provided evidence of modern procedures for the early assessment of ASD, involving both machine learning (ML) techniques and biomarkers, as eye movements (EM) towards social stimuli. This systematic review provides a comprehensive discussion of 11 papers regarding the early assessment of ASD based on ML techniques and children's social visual attention (SVA). Evidences suggest ML as a relevant technique for the early assessment of ASD, which might represent a valid biomarker-based procedure to objectively make diagnosis. Limitations and future directions are discussed.
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36
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Simeoli R, Milano N, Rega A, Marocco D. Using Technology to Identify Children With Autism Through Motor Abnormalities. Front Psychol 2021; 12:635696. [PMID: 34113283 PMCID: PMC8186533 DOI: 10.3389/fpsyg.2021.635696] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 04/19/2021] [Indexed: 12/11/2022] Open
Abstract
Autism is a neurodevelopmental disorder typically assessed and diagnosed through observational analysis of behavior. Assessment exclusively based on behavioral observation sessions requires a lot of time for the diagnosis. In recent years, there is a growing need to make assessment processes more motivating and capable to provide objective measures of the disorder. New evidence showed that motor abnormalities may underpin the disorder and provide a computational marker to enhance assessment and diagnostic processes. Thus, a measure of motor patterns could provide a means to assess young children with autism and a new starting point for rehabilitation treatments. In this study, we propose to use a software tool that through a smart tablet device and touch screen sensor technologies could be able to capture detailed information about children's motor patterns. We compared movement trajectories of autistic children and typically developing children, with the aim to identify autism motor signatures analyzing their coordinates of movements. We used a smart tablet device to record coordinates of dragging movements carried out by 60 children (30 autistic children and 30 typically developing children) during a cognitive task. Machine learning analysis of children's motor patterns identified autism with 93% accuracy, demonstrating that autism can be computationally identified. The analysis of the features that most affect the prediction reveals and describes the differences between the groups, confirming that motor abnormalities are a core feature of autism.
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Affiliation(s)
- Roberta Simeoli
- Department of Humanistic Studies, University of Naples Federico II, Naples, Italy
| | - Nicola Milano
- Institute of Cognitive Sciences and Technologies, National Research Council (CNR), Rome, Italy
| | - Angelo Rega
- Department of Humanistic Studies, University of Naples Federico II, Naples, Italy
- Neapolisanit S.R.L. Rehabilitation Center, Ottaviano, Italy
| | - Davide Marocco
- Department of Humanistic Studies, University of Naples Federico II, Naples, Italy
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Romero-García R, Martínez-Tomás R, Pozo P, de la Paz F, Sarriá E. Q-CHAT-NAO: A robotic approach to autism screening in toddlers. J Biomed Inform 2021; 118:103797. [PMID: 33933653 DOI: 10.1016/j.jbi.2021.103797] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 04/24/2021] [Accepted: 04/26/2021] [Indexed: 10/21/2022]
Abstract
The use of humanoid robots as assistants in therapy processes is not new. Several projects in the past several years have achieved promising results when combining human-robot interaction with standard techniques. Moreover, there are multiple screening systems for autism; one of the most used systems is the Quantitative Checklist for Autism in Toddlers (Q-CHAT-10), which includes ten questions to be answered by the parents or caregivers of a child. We present Q-CHAT-NAO, an observation-based autism screening system supported by a NAO robot. It includes the six questions of the Q-CHAT-10 that can be adapted to work in a robotic context; unlike the original system, it obtains information from the toddler instead of from an indirect source. The detection results obtained after applying machine learning models to the six questions in the Autistic Spectrum Disorder Screening Data for Toddlers dataset were almost equivalent to those of the original version with ten questions. These findings indicate that the Q-CHAT-NAO could be a screening option that would exploit all the benefits related to human-robot interaction.
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Affiliation(s)
- Rubén Romero-García
- Department of Artificial Intelligence, School of Computer Science, UNED, 28040 Madrid, Spain.
| | - Rafael Martínez-Tomás
- Department of Artificial Intelligence, School of Computer Science, UNED, 28040 Madrid, Spain; Joint Research Institute UNED and Health Institute Carlos III (IMIENS), 28040 Madrid, Spain
| | - Pilar Pozo
- Department of Methodology of Behavioral Sciences, Faculty of Psychology, UNED, 28040 Madrid, Spain; Joint Research Institute UNED and Health Institute Carlos III (IMIENS), 28040 Madrid, Spain
| | - Félix de la Paz
- Department of Artificial Intelligence, School of Computer Science, UNED, 28040 Madrid, Spain; Joint Research Institute UNED and Health Institute Carlos III (IMIENS), 28040 Madrid, Spain
| | - Encarnación Sarriá
- Department of Methodology of Behavioral Sciences, Faculty of Psychology, UNED, 28040 Madrid, Spain; Joint Research Institute UNED and Health Institute Carlos III (IMIENS), 28040 Madrid, Spain
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Characterization of Autism Spectrum Disorder (ASD) subtypes based on the relationship between motor skills and social communication abilities. Hum Mov Sci 2021; 77:102802. [PMID: 33894651 DOI: 10.1016/j.humov.2021.102802] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 03/03/2021] [Accepted: 04/16/2021] [Indexed: 11/23/2022]
Abstract
Motor abnormalities are generally observed in autism spectrum disorder (ASD), and motor difficulties are certainly evident during the early years of life and may thus precede social-communication impairments. The main aim of the present study was to examine ASD subtypes based on the relationship between motor skills and social communication abilities. Motor skills and social communication abilities were evaluated through the Movement Assessment Battery for Children-Second Edition, the Autism Diagnostic Observation Schedule-Second Version and the Psychoeducational Profile-Third Edition. In addition, social communication abilities were classified according to the Autism Classification System of Functioning: Social Communication-ACSF:SC criteria. We found that children with ASD presented poorer motor skills than their TD peers, and motor impairments correlated with poorer social communication abilities in children with ASD. In addition, children with lower social and communication functioning showed a more prominent impairment in manual dexterity and fine motor skills than children with better social and communication functioning. In conclusion, we suggest that stratifying children with ASD based on motor and social endophenotypes may be useful to understand the neurobiological mechanisms of ASD and lead to new types of treatment.
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He Q, Wang Q, Wu Y, Yi L, Wei K. Automatic classification of children with autism spectrum disorder by using a computerized visual-orienting task. Psych J 2021; 10:550-565. [PMID: 33847077 DOI: 10.1002/pchj.447] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 12/04/2020] [Accepted: 03/09/2021] [Indexed: 11/09/2022]
Abstract
Early screening and diagnosis of autism spectrum disorder (ASD) primarily rely on behavioral observations by qualified clinicians whose decision process can benefit from the combination of machine learning algorithms and sensor data. We designed a computerized visual-orienting task with gaze-related or non-gaze-related directional cues, which triggered participants' gaze-following behavior. Based on their eye-movement data registered by an eye tracker, we applied the machine learning algorithms to classify high-functioning children with ASD (HFA), low-functioning children with ASD (LFA), and typically developing children (TD). We found that TD children had higher success rates in obtaining rewards than HFA children, and HFA children had higher rates than LFA children. Based on raw eye-tracking data, our machine learning algorithm could classify the three groups with an accuracy of 81.1% and relatively high sensitivity and specificity. Classification became worse if only data from the gaze or nongaze conditions were used, suggesting that "less-social" directional cues also carry useful information for distinguishing these groups. Our findings not only provide insights about visual-orienting deficits among children with ASD but also demonstrate the promise of combining classical behavioral paradigms with machine learning algorithms for aiding the screening for individuals with ASD.
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Affiliation(s)
- Qiao He
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Qiandong Wang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Yaxue Wu
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
| | - Li Yi
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
| | - Kunlin Wei
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
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40
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Jequier Gygax M, Maillard AM, Favre J. Could Gait Biomechanics Become a Marker of Atypical Neuronal Circuitry in Human Development?-The Example of Autism Spectrum Disorder. Front Bioeng Biotechnol 2021; 9:624522. [PMID: 33796508 PMCID: PMC8009281 DOI: 10.3389/fbioe.2021.624522] [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: 10/31/2020] [Accepted: 02/19/2021] [Indexed: 12/28/2022] Open
Abstract
This perspective paper presents converging recent knowledge in neurosciences (motor neurophysiology, neuroimaging and neuro cognition) and biomechanics to outline the relationships between maturing neuronal network, behavior, and gait in human development. Autism Spectrum Disorder (ASD) represents a particularly relevant neurodevelopmental disorder (NDD) to study these convergences, as an early life condition presenting with sensorimotor and social behavioral alterations. ASD diagnosis relies solely on behavioral criteria. The absence of biological marker in ASD is a main challenge, and hampers correlations between behavioral development and standardized data such as brain structure alterations, brain connectivity, or genetic profile. Gait, as a way to study motor system development, represents a well-studied, early life ability that can be characterized through standardized biomechanical analysis. Therefore, developmental gait biomechanics might appear as a possible motor phenotype and biomarker, solid enough to be correlated to neuronal network maturation, in normal and atypical developmental trajectories—like in ASD.
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Affiliation(s)
- Marine Jequier Gygax
- Service des Troubles du Spectre de l'Autisme, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland
| | - Anne M Maillard
- Service des Troubles du Spectre de l'Autisme, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland
| | - Julien Favre
- Swiss BioMotion Lab, Department of Musculoskeletal Medicine, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
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41
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Tunçgenç B, Pacheco C, Rochowiak R, Nicholas R, Rengarajan S, Zou E, Messenger B, Vidal R, Mostofsky SH. Computerized Assessment of Motor Imitation as a Scalable Method for Distinguishing Children With Autism. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:321-328. [PMID: 33229247 PMCID: PMC7943651 DOI: 10.1016/j.bpsc.2020.09.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 09/02/2020] [Accepted: 09/02/2020] [Indexed: 11/29/2022]
Abstract
BACKGROUND Imitation deficits are prevalent in autism spectrum conditions (ASCs) and are associated with core autistic traits. Imitating others' actions is central to the development of social skills in typically developing populations, as it facilitates social learning and bond formation. We present a Computerized Assessment of Motor Imitation (CAMI) using a brief (1-min), highly engaging video game task. METHODS Using Kinect Xbox motion tracking technology, we recorded 48 children (27 with ASCs, 21 typically developing) as they imitated a model's dance movements. We implemented an algorithm based on metric learning and dynamic time warping that automatically detects and evaluates the important joints and returns a score considering spatial position and timing differences between the child and the model. To establish construct validity and reliability, we compared imitation performance measured by the CAMI method to the more traditional human observation coding (HOC) method across repeated trials and two different movement sequences. RESULTS Results revealed poorer imitation in children with ASCs than in typically developing children (ps < .005), with poorer imitation being associated with increased core autism symptoms. While strong correlations between the CAMI and HOC methods (rs = .69-.87) confirmed the CAMI's construct validity, CAMI scores classified the children into diagnostic groups better than the HOC scores (accuracyCAMI = 87.2%, accuracyHOC = 74.4%). Finally, by comparing repeated movement trials, we demonstrated high test-retest reliability of CAMI (rs = .73-.86). CONCLUSIONS Findings support the CAMI as an objective, highly scalable, directly interpretable method for assessing motor imitation differences, providing a promising biomarker for defining biologically meaningful ASC subtypes and guiding intervention.
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Affiliation(s)
- Bahar Tunçgenç
- Center for Neurodevelopment and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland; School of Psychology, University of Nottingham, Nottingham, United Kingdom.
| | - Carolina Pacheco
- Mathematical Institute for Data Science, Johns Hopkins University, Baltimore, Maryland; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Rebecca Rochowiak
- Center for Neurodevelopment and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland
| | - Rosemary Nicholas
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Sundararaman Rengarajan
- Joint Doctoral Program in Language and Communicative Disorders, San Diego State University and University of California San Diego, San Diego, California
| | - Erin Zou
- Center for Neurodevelopment and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland
| | - Brice Messenger
- Center for Neurodevelopment and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland
| | - René Vidal
- Mathematical Institute for Data Science, Johns Hopkins University, Baltimore, Maryland; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Stewart H Mostofsky
- Center for Neurodevelopment and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
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42
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Cavallo A, Romeo L, Ansuini C, Battaglia F, Nobili L, Pontil M, Panzeri S, Becchio C. Identifying the signature of prospective motor control in children with autism. Sci Rep 2021; 11:3165. [PMID: 33542311 PMCID: PMC7862688 DOI: 10.1038/s41598-021-82374-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 01/19/2021] [Indexed: 12/15/2022] Open
Abstract
Failure to develop prospective motor control has been proposed to be a core phenotypic marker of autism spectrum disorders (ASD). However, whether genuine differences in prospective motor control permit discriminating between ASD and non-ASD profiles over and above individual differences in motor output remains unclear. Here, we combined high precision measures of hand movement kinematics and rigorous machine learning analyses to determine the true power of prospective movement data to differentiate children with autism and typically developing children. Our results show that while movement is unique to each individual, variations in the kinematic patterning of sequential grasping movements genuinely differentiate children with autism from typically developing children. These findings provide quantitative evidence for a prospective motor control impairment in autism and indicate the potential to draw inferences about autism on the basis of movement kinematics.
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Affiliation(s)
- Andrea Cavallo
- Cognition, Motion and Neuroscience Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Psychology, Università degli Studi di Torino, Turin, Italy
| | - Luca Romeo
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.,Computational Statistics and Machine Learning Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Caterina Ansuini
- Cognition, Motion and Neuroscience Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Francesca Battaglia
- Cognition, Motion and Neuroscience Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.,Child Neuropsychiatric Unit, IRCCS Istituto G. Gaslini, Genoa, Italy
| | - Lino Nobili
- Child Neuropsychiatric Unit, IRCCS Istituto G. Gaslini, Genoa, Italy.,DINOGMI Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Children's Sciences, Università degli Studi di Genova, Genoa, Italy
| | - Massimiliano Pontil
- Computational Statistics and Machine Learning Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Stefano Panzeri
- Neural Computational Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Cristina Becchio
- Cognition, Motion and Neuroscience Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.
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43
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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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44
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Desideri L, Pérez-Fuster P, Herrera G. Information and Communication Technologies to Support Early Screening of Autism Spectrum Disorder: A Systematic Review. CHILDREN-BASEL 2021; 8:children8020093. [PMID: 33535513 PMCID: PMC7912726 DOI: 10.3390/children8020093] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 01/18/2021] [Accepted: 01/26/2021] [Indexed: 11/16/2022]
Abstract
The aim of this systematic review is to identify recent digital technologies used to detect early signs of autism spectrum disorder (ASD) in preschool children (i.e., up to six years of age). A systematic literature search was performed for English language articles and conference papers indexed in Pubmed, PsycInfo, ERIC, CINAHL, WoS, IEEE, and ACM digital libraries up until January 2020. A follow-up search was conducted to cover the literature published until December 2020 for the usefulness and interest in this area of research during the Covid-19 emergency. In total, 2427 articles were initially retrieved from databases search. Additional 481 articles were retrieved from follow-up search. Finally, 28 articles met the inclusion criteria and were included in the review. The studies included involved four main interface modalities: Natural User Interface (e.g., eye trackers), PC or mobile, Wearable, and Robotics. Most of the papers included (n = 20) involved the use of Level 1 screening tools. Notwithstanding the variability of the solutions identified, psychometric information points to considering available technologies as promising supports in clinical practice to detect early sign of ASD in young children. Further research is needed to understand the acceptability and increase use rates of technology-based screenings in clinical settings. .
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Affiliation(s)
| | - Patricia Pérez-Fuster
- Autism and Technologies Laboratory, University Research Institute on Robotics and Information and Communication Technologies (IRTIC), Universitat de València, 46010 València, Spain; (P.P.-F.); (G.H.)
| | - Gerardo Herrera
- Autism and Technologies Laboratory, University Research Institute on Robotics and Information and Communication Technologies (IRTIC), Universitat de València, 46010 València, Spain; (P.P.-F.); (G.H.)
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45
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Zhang Y, Tian Y, Wu P, Chen D. Application of Skeleton Data and Long Short-Term Memory in Action Recognition of Children with Autism Spectrum Disorder. SENSORS (BASEL, SWITZERLAND) 2021; 21:E411. [PMID: 33430118 PMCID: PMC7827022 DOI: 10.3390/s21020411] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 12/25/2020] [Accepted: 01/06/2021] [Indexed: 11/16/2022]
Abstract
The recognition of stereotyped action is one of the core diagnostic criteria of Autism Spectrum Disorder (ASD). However, it mainly relies on parent interviews and clinical observations, which lead to a long diagnosis cycle and prevents the ASD children from timely treatment. To speed up the recognition process of stereotyped actions, a method based on skeleton data and Long Short-Term Memory (LSTM) is proposed in this paper. In the first stage of our method, the OpenPose algorithm is used to obtain the initial skeleton data from the video of ASD children. Furthermore, four denoising methods are proposed to eliminate the noise of the initial skeleton data. In the second stage, we track multiple ASD children in the same scene by matching distance between current skeletons and previous skeletons. In the last stage, the neural network based on LSTM is proposed to classify the ASD children's actions. The performed experiments show that our proposed method is effective for ASD children's action recognition. Compared to the previous traditional schemes, our scheme has higher accuracy and is almost non-invasive for ASD children.
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Affiliation(s)
- Yunkai Zhang
- School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China; (Y.Z.); (Y.T.)
| | - Yinghong Tian
- School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China; (Y.Z.); (Y.T.)
| | - Pingyi Wu
- Experimental Teaching Center for Teacher Education, East China Normal University, Shanghai 200241, China
| | - Dongfan Chen
- Department of Rehabilitation Sciences, East China Normal University, Shanghai 200062, China;
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46
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Rahman MM, Usman OL, Muniyandi RC, Sahran S, Mohamed S, Razak RA. A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder. Brain Sci 2020; 10:brainsci10120949. [PMID: 33297436 PMCID: PMC7762227 DOI: 10.3390/brainsci10120949] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 12/02/2020] [Accepted: 12/05/2020] [Indexed: 12/27/2022] Open
Abstract
Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning's speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.
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Affiliation(s)
- Md. Mokhlesur Rahman
- Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia; (M.M.R.); (O.L.U.)
| | - Opeyemi Lateef Usman
- Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia; (M.M.R.); (O.L.U.)
| | - Ravie Chandren Muniyandi
- Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia; (M.M.R.); (O.L.U.)
- Correspondence: ; Tel.: +60-123249577
| | - Shahnorbanun Sahran
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia;
| | - Suziyani Mohamed
- Centre of Community Education and Wellbeing, Faculty of Education, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia;
| | - Rogayah A Razak
- Speech Science Programme, Center for Rehabilitation and Special Needs Studies, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, Kuala Lumpur 50300, Malaysia;
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47
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The study of the differences between low-functioning autistic children and typically developing children in the processing of the own-race and other-race faces by the machine learning approach. J Clin Neurosci 2020; 81:54-60. [PMID: 33222968 DOI: 10.1016/j.jocn.2020.09.039] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 08/21/2020] [Accepted: 09/13/2020] [Indexed: 11/24/2022]
Abstract
OBJECTIVE Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder which affects the developmental trajectory in several behavioral domains, including impairments of social communication and stereotyped behavior. Unlike typically developing children who can successfully obtain the detailed facial information to decode the mental status with ease, autistic children cannot infer instant feelings and thoughts of other people due to their abnormal face processing. In the present study, we tested the other-race face, the own-race strange face and the own-race familiar face as stimuli material to explore whether ASD children would display different face fixation patterns for the different types of face compared to TD children. We used a machine learning approach based on eye tracking data to classify autistic children and TD children. METHODS Seventy-seven low-functioning autistic children and eighty typically developing children were recruited. They were required to watch a series face photos in a random order. According to the coordinate frequency distribution, the K-means clustering algorithm divided the image into 64 Area Of Interest (AOI) and selected the features using the minimal redundancy and maximal relevance (mRMR) algorithm. The Support Vector Machine (SVM) was used to classify to determine whether the scan patterns of different faces can be used to identify ASD, and to evaluate classification models from both accuracy and reliability. RESULTS The results showed that the maximum classification accuracy was 72.50% (AUC = 0.77) when 32 of the 64 features of unfamiliar other-race faces were selected; the maximum classification accuracy was 70.63% (AUC = 0.76) when 18 features of own-race strange faces were selected; the maximum classification accuracy was 78.33% (AUC = 0.84) when 48 features of own-race familiar faces were selected; The classification accuracy of combining three types of faces reached a maximum of 84.17% and AUC = 0.89 when 120 features were selected. CONCLUSIONS There are some differences between low-functioning autistic children and typically developing children in the processing of the own-race and other-race faces by the machine learning approach, which might be a useful tool for classifying low-functioning autistic children and TD children.
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48
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Fanelli U, Pappalardo M, Chinè V, Gismondi P, Neglia C, Argentiero A, Calderaro A, Prati A, Esposito S. Role of Artificial Intelligence in Fighting Antimicrobial Resistance in Pediatrics. Antibiotics (Basel) 2020; 9:antibiotics9110767. [PMID: 33139605 PMCID: PMC7692722 DOI: 10.3390/antibiotics9110767] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 10/29/2020] [Accepted: 10/30/2020] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence (AI) is a field of science and engineering concerned with the computational understanding of what is commonly called intelligent behavior. AI is extremely useful in many human activities including medicine. The aim of our narrative review is to show the potential role of AI in fighting antimicrobial resistance in pediatric patients. We searched for PubMed articles published from April 2010 to April 2020 containing the keywords “artificial intelligence”, “machine learning”, “antimicrobial resistance”, “antimicrobial stewardship”, “pediatric”, and “children”, and we described the different strategies for the application of AI in these fields. Literature analysis showed that the applications of AI in health care are potentially endless, contributing to a reduction in the development time of new antimicrobial agents, greater diagnostic and therapeutic appropriateness, and, simultaneously, a reduction in costs. Most of the proposed AI solutions for medicine are not intended to replace the doctor’s opinion or expertise, but to provide a useful tool for easing their work. Considering pediatric infectious diseases, AI could play a primary role in fighting antibiotic resistance. In the pediatric field, a greater willingness to invest in this field could help antimicrobial stewardship reach levels of effectiveness that were unthinkable a few years ago.
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Affiliation(s)
- Umberto Fanelli
- Pediatric Clinic, Pietro Barilla Children’s Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (U.F.); (M.P.); (V.C.); (P.G.); (C.N.); (A.A.)
| | - Marco Pappalardo
- Pediatric Clinic, Pietro Barilla Children’s Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (U.F.); (M.P.); (V.C.); (P.G.); (C.N.); (A.A.)
| | - Vincenzo Chinè
- Pediatric Clinic, Pietro Barilla Children’s Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (U.F.); (M.P.); (V.C.); (P.G.); (C.N.); (A.A.)
| | - Pierpacifico Gismondi
- Pediatric Clinic, Pietro Barilla Children’s Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (U.F.); (M.P.); (V.C.); (P.G.); (C.N.); (A.A.)
| | - Cosimo Neglia
- Pediatric Clinic, Pietro Barilla Children’s Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (U.F.); (M.P.); (V.C.); (P.G.); (C.N.); (A.A.)
| | - Alberto Argentiero
- Pediatric Clinic, Pietro Barilla Children’s Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (U.F.); (M.P.); (V.C.); (P.G.); (C.N.); (A.A.)
| | - Adriana Calderaro
- Microbiology and Virology Unit, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy;
| | - Andrea Prati
- Department of Engineering and Architecture, University of Parma, 43126 Parma, Italy;
| | - Susanna Esposito
- Pediatric Clinic, Pietro Barilla Children’s Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (U.F.); (M.P.); (V.C.); (P.G.); (C.N.); (A.A.)
- Correspondence: ; Tel.: +39-0521-704790
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Chong NK, Chu Shan Elaine C, de Korne DF. Creating a Learning Televillage and Automated Digital Child Health Ecosystem. Pediatr Clin North Am 2020; 67:707-724. [PMID: 32650868 DOI: 10.1016/j.pcl.2020.04.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
This article explores the impact of digital technologies, including telehealth, teleconsultations, wireless devices, and chatbots, in pediatrics. Automated digital health with the Internet of things will allow better collection of real-world data for generation of real-world evidence to improve child health. Artificial intelligence with predictive analytics in turn will drive evidence-based decision-support systems and deliver personalized care to children. This technology creates building blocks for a learning child health and health care ecosystem.
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Affiliation(s)
- Ng Kee Chong
- Medical Innovation & Care Transformation, Division of Medicine, KK Women's & Children's Hospital, 100 Bukit Timah Road, Singapore 229899, Singapore.
| | - Chew Chu Shan Elaine
- Adolescent Medicine Service, Department of Paediatrics, KK Women's & Children's Hospital, Singapore
| | - Dirk F de Korne
- Medical Innovation & Care Transformation, KK Women's & Children's Hospital, Singapore; Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Netherlands; SVRZ Cares in Zeeland, Middelburg, Netherlands
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Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10155135] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.
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