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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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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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Koehler JC, Dong MS, Song DY, Bong G, Koutsouleris N, Yoo H, Falter-Wagner CM. Classifying autism in a clinical population based on motion synchrony: a proof-of-concept study using real-life diagnostic interviews. Sci Rep 2024; 14:5663. [PMID: 38453972 PMCID: PMC10920641 DOI: 10.1038/s41598-024-56098-y] [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: 07/20/2023] [Accepted: 03/01/2024] [Indexed: 03/09/2024] Open
Abstract
Predictive modeling strategies are increasingly studied as a means to overcome clinical bottlenecks in the diagnostic classification of autism spectrum disorder. However, while some findings are promising in the light of diagnostic marker research, many of these approaches lack the scalability for adequate and effective translation to everyday clinical practice. In this study, our aim was to explore the use of objective computer vision video analysis of real-world autism diagnostic interviews in a clinical sample of children and young individuals in the transition to adulthood to predict diagnosis. Specifically, we trained a support vector machine learning model on interpersonal synchrony data recorded in Autism Diagnostic Observation Schedule (ADOS-2) interviews of patient-clinician dyads. Our model was able to classify dyads involving an autistic patient (n = 56) with a balanced accuracy of 63.4% against dyads including a patient with other psychiatric diagnoses (n = 38). Further analyses revealed no significant associations between our classification metrics with clinical ratings. We argue that, given the above-chance performance of our classifier in a highly heterogeneous sample both in age and diagnosis, with few adjustments this highly scalable approach presents a viable route for future diagnostic marker research in autism.
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Affiliation(s)
- Jana Christina Koehler
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany.
| | - Mark Sen Dong
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Da-Yea Song
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Guiyoung Bong
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Heejeong Yoo
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
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程 蓉, 赵 众, 侯 文, 周 刚, 廖 昊, 张 雪, 李 晶. [Machine learning algorithms for identifying autism spectrum disorder through eye-tracking in different intention videos]. ZHONGGUO DANG DAI ER KE ZA ZHI = CHINESE JOURNAL OF CONTEMPORARY PEDIATRICS 2024; 26:151-157. [PMID: 38436312 PMCID: PMC10921872 DOI: 10.7499/j.issn.1008-8830.2309073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/25/2023] [Indexed: 03/05/2024]
Abstract
OBJECTIVES To investigate the differences in visual perception between children with autism spectrum disorder (ASD) and typically developing (TD) children when watching different intention videos, and to explore the feasibility of machine learning algorithms in objectively distinguishing between ASD children and TD children. METHODS A total of 58 children with ASD and 50 TD children were enrolled and were asked to watch the videos containing joint intention and non-joint intention, and the gaze duration and frequency in different areas of interest were used as original indicators to construct classifier-based models. The models were evaluated in terms of the indicators such as accuracy, sensitivity, and specificity. RESULTS When using eight common classifiers, including support vector machine, linear discriminant analysis, decision tree, random forest, and K-nearest neighbors (with K values of 1, 3, 5, and 7), based on the original feature indicators, the highest classification accuracy achieved was 81.90%. A feature reconstruction approach with a decision tree classifier was used to further improve the accuracy of classification, and then the model showed the accuracy of 91.43%, the specificity of 89.80%, and the sensitivity of 92.86%, with an area under the receiver operating characteristic curve of 0.909 (P<0.001). CONCLUSIONS The machine learning model based on eye-tracking data can accurately distinguish ASD children from TD children, which provides a scientific basis for developing rapid and objective ASD screening tools.
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Affiliation(s)
- 蓉 程
- 中国科学院大学心理学系北京100049
- 深圳大学机电与控制工程学院,广东深圳518010
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Singh P, Bornstein MM, Hsung RTC, Ajmera DH, Leung YY, Gu M. Frontiers in Three-Dimensional Surface Imaging Systems for 3D Face Acquisition in Craniofacial Research and Practice: An Updated Literature Review. Diagnostics (Basel) 2024; 14:423. [PMID: 38396462 PMCID: PMC10888365 DOI: 10.3390/diagnostics14040423] [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: 01/08/2024] [Revised: 02/02/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Digitalizing all aspects of dental care is a contemporary approach to ensuring the best possible clinical outcomes. Ongoing advancements in 3D face acquisition have been driven by continuous research on craniofacial structures and treatment effects. An array of 3D surface-imaging systems are currently available for generating photorealistic 3D facial images. However, choosing a purpose-specific system is challenging for clinicians due to variations in accuracy, reliability, resolution, and portability. Therefore, this review aims to provide clinicians and researchers with an overview of currently used or potential 3D surface imaging technologies and systems for 3D face acquisition in craniofacial research and daily practice. Through a comprehensive literature search, 71 articles meeting the inclusion criteria were included in the qualitative analysis, investigating the hardware, software, and operational aspects of these systems. The review offers updated information on 3D surface imaging technologies and systems to guide clinicians in selecting an optimal 3D face acquisition system. While some of these systems have already been implemented in clinical settings, others hold promise. Furthermore, driven by technological advances, novel devices will become cost-effective and portable, and will also enable accurate quantitative assessments, rapid treatment simulations, and improved outcomes.
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Affiliation(s)
- Pradeep Singh
- Discipline of Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (P.S.); (D.H.A.)
| | - Michael M. Bornstein
- Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Mattenstrasse 40, 4058 Basel, Switzerland;
| | - Richard Tai-Chiu Hsung
- Department of Computer Science, Hong Kong Chu Hai College, Hong Kong SAR, China;
- Discipline of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Deepal Haresh Ajmera
- Discipline of Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (P.S.); (D.H.A.)
| | - Yiu Yan Leung
- Discipline of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Min Gu
- Discipline of Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (P.S.); (D.H.A.)
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de Belen RAJ, Eapen V, Bednarz T, Sowmya A. Using visual attention estimation on videos for automated prediction of autism spectrum disorder and symptom severity in preschool children. PLoS One 2024; 19:e0282818. [PMID: 38346053 PMCID: PMC10861059 DOI: 10.1371/journal.pone.0282818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 12/17/2023] [Indexed: 02/15/2024] Open
Abstract
Atypical visual attention in individuals with autism spectrum disorders (ASD) has been utilised as a unique diagnosis criterion in previous research. This paper presents a novel approach to the automatic and quantitative screening of ASD as well as symptom severity prediction in preschool children. We develop a novel computational pipeline that extracts learned features from a dynamic visual stimulus to classify ASD children and predict the level of ASD-related symptoms. Experimental results demonstrate promising performance that is superior to using handcrafted features and machine learning algorithms, in terms of evaluation metrics used in diagnostic tests. Using a leave-one-out cross-validation approach, we obtained an accuracy of 94.59%, a sensitivity of 100%, a specificity of 76.47% and an area under the receiver operating characteristic curve (AUC) of 96% for ASD classification. In addition, we obtained an accuracy of 94.74%, a sensitivity of 87.50%, a specificity of 100% and an AUC of 99% for ASD symptom severity prediction.
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Affiliation(s)
- Ryan Anthony J. de Belen
- School of Computer Science and Engineering, University of New South Wales, New South Wales, Australia
| | - Valsamma Eapen
- School of Psychiatry, University of New South Wales, New South Wales, Australia
| | - Tomasz Bednarz
- School of Art & Design, University of New South Wales, New South Wales, Australia
| | - Arcot Sowmya
- School of Computer Science and Engineering, University of New South Wales, New South Wales, Australia
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Pandya S, Jain S, Verma J. A comprehensive analysis towards exploring the promises of AI-related approaches in autism research. Comput Biol Med 2024; 168:107801. [PMID: 38064848 DOI: 10.1016/j.compbiomed.2023.107801] [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: 08/26/2023] [Revised: 11/09/2023] [Accepted: 11/29/2023] [Indexed: 01/10/2024]
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that presents challenges in communication, social interaction, repetitive behaviour, and limited interests. Detecting ASD at an early stage is crucial for timely interventions and an improved quality of life. In recent times, Artificial Intelligence (AI) has been increasingly used in ASD research. The rise in ASD diagnoses is due to the growing number of ASD cases and the recognition of the importance of early detection, which leads to better symptom management. This study explores the potential of AI in identifying early indicators of autism, aligning with the United Nations Sustainable Development Goals (SDGs) of Good Health and Well-being (Goal 3) and Peace, Justice, and Strong Institutions (Goal 16). The paper aims to provide a comprehensive overview of the current state-of-the-art AI-based autism classification by reviewing recent publications from the last decade. It covers various modalities such as Eye gaze, Facial Expression, Motor skill, MRI/fMRI, and EEG, and multi-modal approaches primarily grouped into behavioural and biological markers. The paper presents a timeline spanning from the history of ASD to recent developments in the field of AI. Additionally, the paper provides a category-wise detailed analysis of the AI-based application in ASD with a diagrammatic summarization to convey a holistic summary of different modalities. It also reports on the successes and challenges of applying AI for ASD detection while providing publicly available datasets. The paper paves the way for future scope and directions, providing a complete and systematic overview for researchers in the field of ASD.
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Affiliation(s)
- Shivani Pandya
- Department of Computer Science and Engineering, Nirma University, Ahmedabad, Gujarat 382481, India.
| | - Swati Jain
- Department of Computer Science and Engineering, Nirma University, Ahmedabad, Gujarat 382481, India.
| | - Jaiprakash Verma
- Department of Computer Science and Engineering, Nirma University, Ahmedabad, Gujarat 382481, India.
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Mukherjee D, Bhavnani S, Lockwood Estrin G, Rao V, Dasgupta J, Irfan H, Chakrabarti B, Patel V, Belmonte MK. Digital tools for direct assessment of autism risk during early childhood: A systematic review. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2024; 28:6-31. [PMID: 36336996 PMCID: PMC10771029 DOI: 10.1177/13623613221133176] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
LAY ABSTRACT The challenge of finding autistic children, and finding them early enough to make a difference for them and their families, becomes all the greater in parts of the world where human and material resources are in short supply. Poverty of resources delays interventions, translating into a poverty of outcomes. Digital tools carry potential to lessen this delay because they can be administered by non-specialists in children's homes, schools or other everyday environments, they can measure a wide range of autistic behaviours objectively and they can automate analysis without requiring an expert in computers or statistics. This literature review aimed to identify and describe digital tools for screening children who may be at risk for autism. These tools are predominantly at the 'proof-of-concept' stage. Both portable (laptops, mobile phones, smart toys) and fixed (desktop computers, virtual-reality platforms) technologies are used to present computerised games, or to record children's behaviours or speech. Computerised analysis of children's interactions with these technologies differentiates children with and without autism, with promising results. Tasks assessing social responses and hand and body movements are the most reliable in distinguishing autistic from typically developing children. Such digital tools hold immense potential for early identification of autism spectrum disorder risk at a large scale. Next steps should be to further validate these tools and to evaluate their applicability in a variety of settings. Crucially, stakeholders from underserved communities globally must be involved in this research, lest it fail to capture the issues that these stakeholders are facing.
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Affiliation(s)
- Debarati Mukherjee
- Indian Institute of Public Health - Bengaluru, Public Health Foundation of India, India
| | | | | | - Vaisnavi Rao
- Institute for Democracy and Economic Affairs (IDEAS), Malaysia
| | | | | | | | - Vikram Patel
- Child Development Group, Sangath, India
- Harvard Medical School, USA
- Harvard T.H. Chan School of Public Health, USA
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Singh P, Hsung RTC, Ajmera DH, Leung YY, McGrath C, Gu M. Can smartphones be used for routine dental clinical application? A validation study for using smartphone-generated 3D facial images. J Dent 2023; 139:104775. [PMID: 37944629 DOI: 10.1016/j.jdent.2023.104775] [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: 09/11/2023] [Revised: 11/01/2023] [Accepted: 11/06/2023] [Indexed: 11/12/2023] Open
Abstract
OBJECTIVES To compare the accuracy of smartphone-generated three-dimensional (3D) facial images to that of direct anthropometry (DA) and 3dMD with the aim of assessing the validity and reliability of smartphone-generated 3D facial images for routine clinical applications. MATERIALS AND METHODS Twenty-five anthropometric soft-tissue facial landmarks were labelled manually on 22 orthognathic surgery patients (11 males and 11 females; mean age 26.2 ± 5.3 years). For each labelled face, two imaging operations were performed using two different surface imaging systems: 3dMDface and Bellus3D FaceApp. Next, 42 inter-landmark facial measurements amongst the identified facial landmarks were measured directly on each labelled face and also digitally on 3D facial images. The measurements obtained from smartphone-generated 3D facial images (SGI) were statistically compared with those from DA and 3dMD. RESULTS SGI had slightly higher measurement values than DA and 3dMD, but there was no statistically significant difference between the mean values of inter-landmark measures across the three methods. Clinically acceptable differences (≤3 mm or ≤5°) were observed for 67 % and 74 % of measurements with good agreement between DA and SGI, and 3dMD and SGI, respectively. An overall small systematic bias of ± 0.2 mm was observed between the three methods. Furthermore, the mean absolute difference between DA and SGI methods was highest for linear (1.41 ± 0.33 mm) as well as angular measurements (3.07 ± 0.73°). CONCLUSIONS SGI demonstrated fair trueness compared to DA and 3dMD. The central region and flat areas of the face in SGI are more accurate. Despite this, SGI have limited clinical application, and the panfacial accuracy of the SGI would be more desirable from a clinical application standpoint. CLINICAL SIGNIFICANCE The usage of SGI in clinical practice for region-specific macro-proportional facial assessment involving central and flat regions of the face or for patient education purposes, which does not require accuracy within 3 mm and 5° can be considered.
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Affiliation(s)
- Pradeep Singh
- Discipline of Orthodontics, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Richard Tai-Chiu Hsung
- Department of Computer Science, Hong Kong Chu Hai College, Hong Kong SAR, China; Discipline of Oral and Maxillofacial Surgery, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Deepal Haresh Ajmera
- Discipline of Orthodontics, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Yiu Yan Leung
- Discipline of Oral and Maxillofacial Surgery, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Colman McGrath
- Discipline of Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Min Gu
- Discipline of Orthodontics, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China.
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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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Thapa R, Garikipati A, Ciobanu M, Singh NP, Browning E, DeCurzio J, Barnes G, Dinenno FA, Mao Q, Das R. Machine Learning Differentiation of Autism Spectrum Sub-Classifications. J Autism Dev Disord 2023:10.1007/s10803-023-06121-4. [PMID: 37751097 DOI: 10.1007/s10803-023-06121-4] [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: 08/19/2023] [Indexed: 09/27/2023]
Abstract
PURPOSE Disorders on the autism spectrum have characteristics that can manifest as difficulties with communication, executive functioning, daily living, and more. These challenges can be mitigated with early identification. However, diagnostic criteria has changed from DSM-IV to DSM-5, which can make diagnosing a disorder on the autism spectrum complex. We evaluated machine learning to classify individuals as having one of three disorders of the autism spectrum under DSM-IV, or as non-spectrum. METHODS We employed machine learning to analyze retrospective data from 38,560 individuals. Inputs encompassed clinical, demographic, and assessment data. RESULTS The algorithm achieved AUROCs ranging from 0.863 to 0.980. The model correctly classified 80.5% individuals; 12.6% of individuals from this dataset were misclassified with another disorder on the autism spectrum. CONCLUSION Machine learning can classify individuals as having a disorder on the autism spectrum or as non-spectrum using minimal data inputs.
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Affiliation(s)
- R Thapa
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - A Garikipati
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - M Ciobanu
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - N P Singh
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - E Browning
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - J DeCurzio
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - G Barnes
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - F A Dinenno
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - Q Mao
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA.
| | - R Das
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
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12
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Meng F, Li F, Wu S, Yang T, Xiao Z, Zhang Y, Liu Z, Lu J, Luo X. Machine learning-based early diagnosis of autism according to eye movements of real and artificial faces scanning. Front Neurosci 2023; 17:1170951. [PMID: 37795184 PMCID: PMC10545898 DOI: 10.3389/fnins.2023.1170951] [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: 02/21/2023] [Accepted: 08/17/2023] [Indexed: 10/06/2023] Open
Abstract
Background Studies on eye movements found that children with autism spectrum disorder (ASD) had abnormal gaze behavior to social stimuli. The current study aimed to investigate whether their eye movement patterns in relation to cartoon characters or real people could be useful in identifying ASD children. Methods Eye-tracking tests based on videos of cartoon characters and real people were performed for ASD and typically developing (TD) children aged between 12 and 60 months. A three-level hierarchical structure including participants, events, and areas of interest was used to arrange the data obtained from eye-tracking tests. Random forest was adopted as the feature selection tool and classifier, and the flattened vectors and diagnostic information were used as features and labels. A logistic regression was used to evaluate the impact of the most important features. Results A total of 161 children (117 ASD and 44 TD) with a mean age of 39.70 ± 12.27 months were recruited. The overall accuracy, precision, and recall of the model were 0.73, 0.73, and 0.75, respectively. Attention to human-related elements was positively related to the diagnosis of ASD, while fixation time for cartoons was negatively related to the diagnosis. Conclusion Using eye-tracking techniques with machine learning algorithms might be promising for identifying ASD. The value of artificial faces, such as cartoon characters, in the field of ASD diagnosis and intervention is worth further exploring.
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Affiliation(s)
- Fanchao Meng
- The National Clinical Research Center for Mental Disorder & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Fenghua Li
- Key Lab of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Shuxian Wu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Tingyu Yang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhou Xiao
- Department of Child Psychiatry, Kangning Hospital of Shenzhen, Shenzhen Mental Health Center, Shenzhen, Guangdong, China
| | - Yujian Zhang
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, Sichuan, China
| | - Zhengkui Liu
- Key Lab of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Jianping Lu
- Department of Child Psychiatry, Kangning Hospital of Shenzhen, Shenzhen Mental Health Center, Shenzhen, Guangdong, China
| | - Xuerong Luo
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
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13
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Washington P, Wall DP. A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism. Annu Rev Biomed Data Sci 2023; 6:211-228. [PMID: 37137169 PMCID: PMC11093217 DOI: 10.1146/annurev-biodatasci-020722-125454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic features are observable, can be tracked over time, and can be managed or even eliminated through proper therapy and treatments. However, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related neurodevelopmental delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide increased access to services for affected families. Several efforts previously conducted by a multitude of research labs have spawned great progress toward improved digital diagnostics and digital therapies for children with autism. We review the literature on digital health methods for autism behavior quantification and beneficial therapies using data science. We describe both case-control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics that integrate machine learning models of autism-related behaviors, including the factors that must be addressed for translational use. Finally, we describe ongoing challenges and potential opportunities for the field of autism data science. Given the heterogeneous nature of autism and the complexities of the relevant behaviors, this review contains insights that are relevant to neurological behavior analysis and digital psychiatry more broadly.
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Affiliation(s)
- Peter Washington
- Department of Information and Computer Sciences, University of Hawai'i at Mānoa, Honolulu, Hawai'i, USA
| | - Dennis P Wall
- Departments of Pediatrics (Systems Medicine), Biomedical Data Science, and Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA;
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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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Abstract
ABSTRACT This article briefly examines the life and work of the late clinical psychologist and philosopher of science Paul E. Meehl. His thesis in Clinical versus Statistical Prediction (1954) that the data combination performed by mechanical operations, as compared to clinicians, achieves higher accuracy in predicting human behavior is one of the earliest theoretical works that laid the groundwork for utilizing statistics and computational modeling in research in psychiatry and clinical psychology. For today's psychiatric researchers and clinicians grappling with the challenges of translating the ever-increasing data of the human mind into practice tools, Meehl's advocacy for both accurate modeling of the data and their clinically relevant use is timely.
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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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17
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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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Xue H, Zhang L, Wang J, Liu W, Liu S, Ming D. Dynamic eye avoidance patterns in the high autistic traits group: An eye-tracking study. Front Psychiatry 2023; 14:1086282. [PMID: 37032943 PMCID: PMC10079916 DOI: 10.3389/fpsyt.2023.1086282] [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: 11/01/2022] [Accepted: 03/01/2023] [Indexed: 04/11/2023] Open
Abstract
Introduction Reduced fixation to the eye area is the main characteristic of social deficits associated with Autism Spectrum Disorder; a similar pattern may exist in individuals with high autistic traits. However, their scanning patterns to the eye area of emotional faces are still unclear on the time scale. Methods In the present study, we recruited 46 participants and divided them into the high autistic traits (HAT) group (23 participants) and the low autistic traits (LAT) group (20 participants) based on their Autism Spectrum Quotient (AQ) scores. Moreover, we captured their eye movement patterns when observing different angular emotional faces. We extracted the proportional fixation time to the eye area under different time windows. Results The results showed that the fixation time of the HAT group was always significantly smaller than that of the LAT group (p < 0.05), and the difference between the two groups increased in the middle and late stages of face presentation. The results of the linear regression analysis showed that the proportional fixation time was negatively correlated with AQ scores (p < 0.05), indicating that the proportional fixation time to the eye area could be a potential indicator to measure the level of autistic traits. We then calculated the latency to orient the eye area and the latency to disengage the eye area to explore the priority of observation of the eyes. The results showed that compared with the LAT group, the HAT group has a longer latency to orient the eye area (p < 0.05) and has longer latency to disengage the eye area (p < 0.05), illustrating that the HAT group saw the eyes more slowly and left them faster.
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Affiliation(s)
- Huiqin Xue
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Ludan Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Junling Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Wei Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Children’s Hospital, Tianjin University, Tianjin, China
- *Correspondence: Wei Liu,
| | - Shuang Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Shuang Liu,
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
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Iwauchi K, Tanaka H, Okazaki K, Matsuda Y, Uratani M, Morimoto T, Nakamura S. Eye-movement analysis on facial expression for identifying children and adults with neurodevelopmental disorders. Front Digit Health 2023; 5:952433. [PMID: 36874367 PMCID: PMC9978093 DOI: 10.3389/fdgth.2023.952433] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 01/30/2023] [Indexed: 02/18/2023] Open
Abstract
Experienced psychiatrists identify people with autism spectrum disorder (ASD) and schizophrenia (Sz) through interviews based on diagnostic criteria, their responses, and various neuropsychological tests. To improve the clinical diagnosis of neurodevelopmental disorders such as ASD and Sz, the discovery of disorder-specific biomarkers and behavioral indicators with sufficient sensitivity is important. In recent years, studies have been conducted using machine learning to make more accurate predictions. Among various indicators, eye movement, which can be easily obtained, has attracted much attention and various studies have been conducted for ASD and Sz. Eye movement specificity during facial expression recognition has been studied extensively in the past, but modeling taking into account differences in specificity among facial expressions has not been conducted. In this paper, we propose a method to detect ASD or Sz from eye movement during the Facial Emotion Identification Test (FEIT) while considering differences in eye movement due to the facial expressions presented. We also confirm that weighting using the differences improves classification accuracy. Our data set sample consisted of 15 adults with ASD and Sz, 16 controls, and 15 children with ASD and 17 controls. Random forest was used to weight each test and classify the participants as control, ASD, or Sz. The most successful approach used heat maps and convolutional neural networks (CNN) for eye retention. This method classified Sz in adults with 64.5% accuracy, ASD in adults with up to 71.0% accuracy, and ASD in children with 66.7% accuracy. Classifying of ASD result was significantly different (p<.05) by the binomial test with chance rate. The results show a 10% and 16.7% improvement in accuracy, respectively, compared to a model that does not take facial expressions into account. In ASD, this indicates that modeling is effective, which weights the output of each image.
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Affiliation(s)
- Kota Iwauchi
- Augmented Human Communication Laboratory, Nara Institute of Science and Technology, Ikoma, Nara, Japan
| | - Hiroki Tanaka
- Augmented Human Communication Laboratory, Nara Institute of Science and Technology, Ikoma, Nara, Japan
| | - Kosuke Okazaki
- Department of Psychiatry, Nara Medical University School of Medicine, Kashihara, Nara, Japan
| | - Yasuhiro Matsuda
- Department of Psychiatry, Nara Medical University School of Medicine, Kashihara, Nara, Japan.,Osaka Psychiatric Medical Center, Osaka, Japan
| | - Mitsuhiro Uratani
- Department of Psychiatry, Nara Medical University School of Medicine, Kashihara, Nara, Japan
| | - Tsubasa Morimoto
- Department of Psychiatry, Nara Medical University School of Medicine, Kashihara, Nara, Japan
| | - Satoshi Nakamura
- Augmented Human Communication Laboratory, Nara Institute of Science and Technology, Ikoma, Nara, Japan
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20
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Wei Q, Cao H, Shi Y, Xu X, Li T. Machine learning based on eye-tracking data to identify Autism Spectrum Disorder: A systematic review and meta-analysis. J Biomed Inform 2023; 137:104254. [PMID: 36509416 DOI: 10.1016/j.jbi.2022.104254] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND Machine learning has been widely used to identify Autism Spectrum Disorder (ASD) based on eye-tracking, but its accuracy is uncertain. We aimed to summarize the available evidence on the performances of machine learning algorithms in classifying ASD and typically developing (TD) individuals based on eye-tracking data. METHODS We searched Medline, Embase, Web of Science, Scopus, Cochrane Library, IEEE Xplore Digital Library, Wan Fang Database, China National Knowledge Infrastructure, Chinese BioMedical Literature Database, VIP Database for Chinese Technical Periodicals, from database inception to December 24, 2021. Studies using machine learning methods to classify ASD and TD individuals based on eye-tracking technologies were included. We extracted the data on study population, model performances, algorithms of machine learning, and paradigms of eye-tracking. This study is registered with PROSPERO, CRD42022296037. RESULTS 261 articles were identified, of which 24 studies with sample sizes ranging from 28 to 141 were included (n = 1396 individuals). Machine learning based on eye-tracking yielded the pooled classified accuracy of 81 % (I2 = 73 %), specificity of 79 % (I2 = 61 %), and sensitivity of 84 % (I2 = 61 %) in classifying ASD and TD individuals. In subgroup analysis, the accuracy was 88 % (95 % CI: 85-91 %), 79 % (95 % CI: 72-84 %), 71 % (95 % CI: 59-91 %) for preschool-aged, school-aged, and adolescent-adult group. Eye-tracking stimuli and machine learning algorithms varied widely across studies, with social, static, and active stimuli and Support Vector Machine and Random Forest most commonly reported. Regarding the model performance evaluation, 15 studies reported their final results on validation datasets, four based on testing datasets, and five did not report whether they used validation datasets. Most studies failed to report the information on eye-tracking hardware and the implementation process. CONCLUSION Using eye-tracking data, machine learning has shown potential in identifying ASD individuals with high accuracy, especially in preschool-aged children. However, the heterogeneity between studies, the absence of test set-based performance evaluations, the small sample size, and the non-standardized implementation of eye-tracking might deteriorate the reliability of results. Further well-designed and well-executed studies with comprehensive and transparent reporting are needed to determine the optimal eye-tracking paradigms and machine learning algorithms.
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Affiliation(s)
- Qiuhong Wei
- Children Nutrition Research Center, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Childhood Nutrition and Health, Chongqing, China
| | - Huiling Cao
- Department of Neonatology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Yuan Shi
- Department of Neonatology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Ximing Xu
- Big Data Center for Children's Medical Care, Children's Hospital of Chongqing Medical University, Chongqing, China.
| | - Tingyu Li
- Children Nutrition Research Center, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Childhood Nutrition and Health, Chongqing, China.
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21
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Trends and features of autism spectrum disorder research using artificial intelligence techniques: a bibliometric approach. CURRENT PSYCHOLOGY 2022. [DOI: 10.1007/s12144-022-03977-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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22
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Facial Scanners in Dentistry: An Overview. PROSTHESIS 2022. [DOI: 10.3390/prosthesis4040053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Purpose: This narrative review aims to explore the current status of facial scanning technology in the dental field; outlining the history, mechanisms, and current evidence regarding its use and limitations within digital dentistry. Methods: Subtopics within facial scanner technology in dentistry were identified and divided among four reviewers. Electronic searches of the Medline (PubMed) database were performed with the following search terms: facial scanner, dentistry, prosthodontics, virtual patient, sleep apnea, maxillofacial prosthetics, accuracy. For this review only studies or review papers evaluating facial scanning technology for dental or medical applications were included. A total of 44 articles were included. Due to the narrative nature of this review, no formal evidence-based quality assessment was performed and the search was limited to the English language. No further restrictions were applied. Results: The significance, applications, limitations, and future directions of facial scanning technology were reviewed. Specific subtopics include significant history of facial scanner use and development for dentistry, different types and mechanisms used in facial scanning technology, accuracy of scanning technology, use as a diagnostic tool, use in creating a virtual patient, virtual articulation, smile design, diagnosing and treating obstructive sleep apnea, limitations of scanning technology, and future directions with artificial intelligence. Conclusions: Despite limitations in scan quality and software operation, 3D facial scanners are rapid and non-invasive tools that can be utilized in multiple facets of dental care. Facial scanners can serve an invaluable role in the digital workflow by capturing facial records to facilitate interdisciplinary communication, virtual articulation, smile design, and obstructive sleep apnea diagnosis and treatment. Looking into the future, facial scanning technology has promising applications in the fields of craniofacial research, and prosthodontic diagnosis and treatment planning.
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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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24
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Han J, Jiang G, Ouyang G, Li X. A Multimodal Approach for Identifying Autism Spectrum Disorders in Children. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2003-2011. [PMID: 35853070 DOI: 10.1109/tnsre.2022.3192431] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Identification of autism spectrum disorder (ASD) in children is challenging due to the complexity and heterogeneity of ASD. Currently, most existing methods mainly rely on a single modality with limited information and often cannot achieve satisfactory performance. To address this issue, this paper investigates from internal neurophysiological and external behavior perspectives simultaneously and proposes a new multimodal diagnosis framework for identifying ASD in children with fusion of electroencephalogram (EEG) and eye-tracking (ET) data. Specifically, we designed a two-step multimodal feature learning and fusion model based on a typical deep learning algorithm, stacked denoising autoencoder (SDAE). In the first step, two SDAE models are designed for feature learning for EEG and ET modality, respectively. Then, a third SDAE model in the second step is designed to perform multimodal fusion with learned EEG and ET features in a concatenated way. Our designed multimodal identification model can automatically capture correlations and complementarity from behavior modality and neurophysiological modality in a latent feature space, and generate informative feature representations with better discriminability and generalization for enhanced identification performance. We collected a multimodal dataset containing 40 ASD children and 50 typically developing (TD) children to evaluate our proposed method. Experimental results showed that our proposed method achieved superior performance compared with two unimodal methods and a simple feature-level fusion method, which has promising potential to provide an objective and accurate diagnosis to assist clinicians.
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25
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Design, Development, and a Pilot Study of a Low-Cost Robot for Child–Robot Interaction in Autism Interventions. MULTIMODAL TECHNOLOGIES AND INTERACTION 2022. [DOI: 10.3390/mti6060043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Socially assistive robots are widely deployed in interventions with children on the autism spectrum, exploiting the benefits of this technology in social behavior intervention plans, while reducing their autistic behavior. Furthermore, innovations in modern technologies such as machine learning enhance these robots with great capabilities. Since the results of this implementation are promising, their total cost makes them unaffordable for some organizations while the needs are growing progressively. In this paper, a low-cost robot for autism interventions is proposed, benefiting from the advantages of machine learning and low-cost hardware. The mechanical design of the robot and the development of machine learning models are presented. The robot was evaluated by a small group of educators for children with ASD. The results of various model implementations, together with the design evaluation of the robot, are encouraging and indicate that this technology would be advantageous for deployment in child–robot interaction scenarios.
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26
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Early Detection of Autism Spectrum Disorders (ASD) with the Help of Data Mining Tools. BIOMED RESEARCH INTERNATIONAL 2022; 2022:1201129. [PMID: 35655478 PMCID: PMC9152406 DOI: 10.1155/2022/1201129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 05/04/2022] [Accepted: 05/11/2022] [Indexed: 11/17/2022]
Abstract
Autism is a disorder of neurobiological origin that originates a different course in the development of verbal and nonverbal communication, social interactions, the flexibility of behavior, and interests. The results obtained offer relevant information to reflect on the practices currently used in assessing the development of children and the detection of ASD and suggest the need to strengthen the training of health professionals in aspects such as psychology and developmental disorders. This study, based on genuine and current facts, used data from 292 children with an autism spectrum disorder. The input dataset has 20 characteristics, and the output dataset has one attribute. The output property indicates whether or not a certain person has autism. The research study first and foremost performed data pretreatment activities such as filling in missing data gaps in the data collection, digitizing categorical data, and normalizing. The features were then clustered using
-means and
-means clustering methods, then artificial neural networks and a linguistic strong neurofuzzy classifier were used to classify them. The outcomes of each strategy were examined, and their respective performances were compared.
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Robles M, Namdarian N, Otto J, Wassiljew E, Navab N, Falter-Wagner C, Roth D. A Virtual Reality Based System for the Screening and Classification of Autism. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022; 28:2168-2178. [PMID: 35171773 DOI: 10.1109/tvcg.2022.3150489] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Autism - also known as Autism Spectrum Disorders or Autism Spectrum Conditions - is a neurodevelopmental condition characterized by repetitive behaviours and differences in communication and social interaction. As a consequence, many autistic individuals may struggle in everyday life, which sometimes manifests in depression, unemployment, or addiction. One crucial problem in patient support and treatment is the long waiting time to diagnosis, which was approximated to thirteen months on average. Yet, the earlier an intervention can take place the better the patient can be supported, which was identified as a crucial factor. We propose a system to support the screening of Autism Spectrum Disorders based on a virtual reality social interaction, namely a shopping experience, with an embodied agent. During this everyday interaction, behavioral responses are tracked and recorded. We analyze this behavior with machine learning approaches to classify participants from an autistic participant sample in comparison to a typically developed individuals control sample with high accuracy, demonstrating the feasibility of the approach. We believe that such tools can strongly impact the way mental disorders are assessed and may help to further find objective criteria and categorization.
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28
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Dwyer D, Koutsouleris N. Annual Research Review: Translational machine learning for child and adolescent psychiatry. J Child Psychol Psychiatry 2022; 63:421-443. [PMID: 35040130 DOI: 10.1111/jcpp.13545] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/06/2021] [Indexed: 12/14/2022]
Abstract
Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.
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Affiliation(s)
- Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia.,Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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Application of Machine Learning Techniques to Detect the Children with Autism Spectrum Disorder. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:9340027. [PMID: 35368925 PMCID: PMC8975630 DOI: 10.1155/2022/9340027] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/05/2022] [Accepted: 03/08/2022] [Indexed: 11/17/2022]
Abstract
Early detection of autism spectrum disorder (ASD) is highly beneficial to the health sustainability of children. Existing detection methods depend on the assessment of experts, which are subjective and costly. In this study, we proposed a machine learning approach that fuses physiological data (electroencephalography, EEG) and behavioral data (eye fixation and facial expression) to detect children with ASD. Its implementation can improve detection efficiency and reduce costs. First, we used an innovative approach to extract features of eye fixation, facial expression, and EEG data. Then, a hybrid fusion approach based on a weighted naive Bayes algorithm was presented for multimodal data fusion with a classification accuracy of 87.50%. Results suggest that the machine learning classification approach in this study is effective for the early detection of ASD. Confusion matrices and graphs demonstrate that eye fixation, facial expression, and EEG have different discriminative powers for the detection of ASD and typically developing children, and EEG may be the most discriminative information. The physiological and behavioral data have important complementary characteristics. Thus, the machine learning approach proposed in this study, which combines the complementary information, can significantly improve classification accuracy.
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30
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Alsaade FW, Alzahrani MS. Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8709145. [PMID: 35265118 PMCID: PMC8901307 DOI: 10.1155/2022/8709145] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 01/14/2022] [Accepted: 02/07/2022] [Indexed: 11/20/2022]
Abstract
Autism spectrum disorder (ASD) is a type of mental illness that can be detected by using social media data and biomedical images. Autism spectrum disorder (ASD) is a neurological disease correlated with brain growth that later impacts the physical impression of the face. Children with ASD have dissimilar facial landmarks, which set them noticeably apart from typically developed (TD) children. Novelty of the proposed research is to design a system that is based on autism spectrum disorder detection on social media and face recognition. To identify such landmarks, deep learning techniques may be used, but they require a precise technology for extracting and producing the proper patterns of the face features. This study assists communities and psychiatrists in experimentally detecting autism based on facial features, by using an uncomplicated web application based on a deep learning system, that is, a convolutional neural network with transfer learning and the flask framework. Xception, Visual Geometry Group Network (VGG19), and NASNETMobile are the pretrained models that were used for the classification task. The dataset that was used to test these models was collected from the Kaggle platform and consisted of 2,940 face images. Standard evaluation metrics such as accuracy, specificity, and sensitivity were used to evaluate the results of the three deep learning models. The Xception model achieved the highest accuracy result of 91%, followed by VGG19 (80%) and NASNETMobile (78%).
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Affiliation(s)
- Fawaz Waselallah Alsaade
- College of Computer Science and Information Technology, King Faisal University, P.O. Box 4000, Al-Ahsa, Saudi Arabia
| | - Mohammed Saeed Alzahrani
- College of Computer Science and Information Technology, King Faisal University, P.O. Box 4000, Al-Ahsa, Saudi Arabia
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31
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Deep learning-based school attendance prediction for autistic students. Sci Rep 2022; 12:1431. [PMID: 35082310 PMCID: PMC8791997 DOI: 10.1038/s41598-022-05258-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 01/07/2022] [Indexed: 12/31/2022] Open
Abstract
Autism Spectrum Disorder is a neurodevelopmental disorder characterized by deficits in social communication and interaction as well as the presence of repetitive, restricted patterns of behavior, interests, or activities. Many autistic students experience difficulty with daily functioning at school and home. Given these difficulties,
regular school attendance is a primary source for autistic students to receive an appropriate range of needed educational and therapeutic interventions. Moreover, school absenteeism (SA) is associated with negative consequences such as school drop-out. Therefore, early SA prediction would help school districts to intervene properly to ameliorate this issue. Due to its heterogeneity, autistic students show within-group differences concerning their SA. A comprehensive statistical analysis performed by the authors shows that the individual and demographic characteristics of the targeted population are not predictive factors of SA. So, we used the students’ recent previous attendance to predict their future attendance. We introduce a deep learning-based framework for predicting short-and long-term SA of autistic students using the Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) algorithms. The adopted algorithms outperform other machine learning algorithms. In detail, LSTM increased the accuracy and recall of short-term SA prediction by 20% and 13%, while the same scores of long-term SA prediction increased by 5% using MLP.
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32
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Development of a visual attention based decision support system for autism spectrum disorder screening. Int J Psychophysiol 2022; 173:69-81. [PMID: 35007668 DOI: 10.1016/j.ijpsycho.2022.01.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 12/14/2021] [Accepted: 01/04/2022] [Indexed: 11/24/2022]
Abstract
Visual attention of young children with autism spectrum disorder (ASD) has been well documented in the literature for the past 20 years. In this study, we developed a Decision Support System (DSS) that uses machine learning (ML) techniques to identify young children with ASD from typically developing (TD) children. Study participants included 26 to 36 months old young children with ASD (n = 61) and TD children (n = 72). The results showed that the proposed DSS achieved up to 87.5% success rate in the early assessment of ASD in young children. Findings suggested that visual attention is a unique, promising biomarker for early assessment of ASD. Study results were discussed, and suggestions for future research were provided.
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33
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Detection of Autism Spectrum Disorder using fMRI Functional Connectivity with Feature Selection and Deep Learning. Cognit Comput 2022. [DOI: 10.1007/s12559-021-09981-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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34
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Kong XJ, Wei Z, Sun B, Tu Y, Huang Y, Cheng M, Yu S, Wilson G, Park J, Feng Z, Vangel M, Kong J, Wan G. Different Eye Tracking Patterns in Autism Spectrum Disorder in Toddler and Preschool Children. Front Psychiatry 2022; 13:899521. [PMID: 35757211 PMCID: PMC9218189 DOI: 10.3389/fpsyt.2022.899521] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 05/10/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Children with autism spectrum disorder (ASD) have been observed to be associated with fixation abnormality as measured eye tracking, but the dynamics behind fixation patterns across age remain unclear. MATERIALS AND METHODS In this study, we investigated gaze patterns between toddlers and preschoolers with and without ASD while they viewed video clips and still images (i.e., mouth-moving face, biological motion, mouthing face vs. moving object, still face picture vs. objects, and moving toys). RESULTS We found that the fixation time percentage of children with ASD showed significant decrease compared with that of TD children in almost all areas of interest (AOI) except for moving toy (helicopter). We also observed a diagnostic group (ASD vs. TD) and chronological age (Toddlers vs. preschooler) interaction for the eye AOI during the mouth-moving video clip. Support vector machine analysis showed that the classifier could discriminate ASD from TD in toddlers with an accuracy of 80% and could discriminate ASD from TD in preschoolers with an accuracy of 71%. CONCLUSION Our results suggest that toddlers and preschoolers may be associated with both common and distinct fixation patterns. A combination of eye tracking and machine learning methods has the potential to shed light on the development of new early screening/diagnosis methods for ASD.
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Affiliation(s)
- Xue-Jun Kong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Zhen Wei
- Department of Child Psychiatry and Rehabilitation, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Binbin Sun
- Department of Child Psychiatry and Rehabilitation, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Yiheng Tu
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Yiting Huang
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Ming Cheng
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Siyi Yu
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Georgia Wilson
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Joel Park
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Zhe Feng
- Department of Child Psychiatry and Rehabilitation, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Mark Vangel
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Jian Kong
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Guobin Wan
- Department of Child Psychiatry and Rehabilitation, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
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35
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Attallah O. A deep learning-based diagnostic tool for identifying various diseases via facial images. Digit Health 2022; 8:20552076221124432. [PMID: 36105626 PMCID: PMC9465585 DOI: 10.1177/20552076221124432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/18/2022] [Indexed: 11/16/2022] Open
Abstract
With the current health crisis caused by the COVID-19 pandemic, patients have
become more anxious about infection, so they prefer not to have direct contact
with doctors or clinicians. Lately, medical scientists have confirmed that
several diseases exhibit corresponding specific features on the face the face.
Recent studies have indicated that computer-aided facial diagnosis can be a
promising tool for the automatic diagnosis and screening of diseases from facial
images. However, few of these studies used deep learning (DL) techniques. Most
of them focused on detecting a single disease, using handcrafted feature
extraction methods and conventional machine learning techniques based on
individual classifiers trained on small and private datasets using images taken
from a controlled environment. This study proposes a novel computer-aided facial
diagnosis system called FaceDisNet that uses a new public dataset based on
images taken from an unconstrained environment and could be employed for
forthcoming comparisons. It detects single and multiple diseases. FaceDisNet is
constructed by integrating several spatial deep features from convolutional
neural networks of various architectures. It does not depend only on spatial
features but also extracts spatial-spectral features. FaceDisNet searches for
the fused spatial-spectral feature set that has the greatest impact on the
classification. It employs two feature selection techniques to reduce the large
dimension of features resulting from feature fusion. Finally, it builds an
ensemble classifier based on stacking to perform classification. The performance
of FaceDisNet verifies its ability to diagnose single and multiple diseases.
FaceDisNet achieved a maximum accuracy of 98.57% and 98% after the ensemble
classification and feature selection steps for binary and multiclass
classification categories. These results prove that FaceDisNet is a reliable
tool and could be employed to avoid the difficulties and complications of manual
diagnosis. Also, it can help physicians achieve accurate diagnoses without the
need for physical contact with the patients.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
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36
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Abstract
Several products consist of probiotics that are available in markets, and their potential uses are growing day by day, mainly because some strains of probiotics promote the health of gut microbiota, especially Furmicutes and Bacteroidetes, and may prevent certain gastrointestinal tract (GIT) problems. Some common diseases are inversely linked with the consumption of probiotics, i.e., obesity, type 2 diabetes, autism, osteoporosis, and some immunological disorders, for which the disease progression gets delayed. In addition to disease mitigating properties, these microbes also improve oral, nutritional, and intestinal health, followed by a robust defensive mechanism against particular gut pathogens, specifically by antimicrobial substances and peptides producing probiotics (AMPs). All these positive attributes of probiotics depend upon the type of microbial strains dispensed. Lactic acid bacteria (LAB) and Bifidobacteria are the most common microbes used, but many other microbes are available, and their use depends upon origin and health-promoting properties. This review article focuses on the most common probiotics, their health benefits, and the alleviating mechanisms against chronic kidney diseases (CKD), type 1 diabetes (T1D), type 2 diabetes (T2D), gestational diabetes mellitus (GDM), and obesity.
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Alcañiz M, Chicchi-Giglioli IA, Carrasco-Ribelles LA, Marín-Morales J, Minissi ME, Teruel-García G, Sirera M, Abad L. Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis. Autism Res 2021; 15:131-145. [PMID: 34811930 DOI: 10.1002/aur.2636] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 10/13/2021] [Accepted: 10/28/2021] [Indexed: 12/11/2022]
Abstract
The core symptoms of autism spectrum disorder (ASD) mainly relate to social communication and interactions. ASD assessment involves expert observations in neutral settings, which introduces limitations and biases related to lack of objectivity and does not capture performance in real-world settings. To overcome these limitations, advances in technologies (e.g., virtual reality) and sensors (e.g., eye-tracking tools) have been used to create realistic simulated environments and track eye movements, enriching assessments with more objective data than can be obtained via traditional measures. This study aimed to distinguish between autistic and typically developing children using visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to and extraction of socially relevant information. The 55 children participated. Autistic children presented a higher number of frames, both overall and per scenario, and showed higher visual preferences for adults over children, as well as specific preferences for adults' rather than children's faces on which looked more at bodies. A set of multivariate supervised machine learning models were developed using recursive feature selection to recognize ASD based on extracted eye gaze features. The models achieved up to 86% accuracy (sensitivity = 91%) in recognizing autistic children. Our results should be taken as preliminary due to the relatively small sample size and the lack of an external replication dataset. However, to our knowledge, this constitutes a first proof of concept in the combined use of virtual reality, eye-tracking tools, and machine learning for ASD recognition. LAY SUMMARY: Core symptoms in children with ASD involve social communication and interaction. ASD assessment includes expert observations in neutral settings, which show limitations and biases related to lack of objectivity and do not capture performance in real settings. To overcome these limitations, this work aimed to distinguish between autistic and typically developing children in visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to, and extraction of, socially relevant information.
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Affiliation(s)
- Mariano Alcañiz
- Instituto de Investigación e Innovación en Bioingeniería (i3B), Universitat Politécnica de Valencia, Valencia, Spain
| | - Irene Alice Chicchi-Giglioli
- Instituto de Investigación e Innovación en Bioingeniería (i3B), Universitat Politécnica de Valencia, Valencia, Spain
| | - Lucía A Carrasco-Ribelles
- Instituto de Investigación e Innovación en Bioingeniería (i3B), Universitat Politécnica de Valencia, Valencia, Spain.,Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Javier Marín-Morales
- Instituto de Investigación e Innovación en Bioingeniería (i3B), Universitat Politécnica de Valencia, Valencia, Spain
| | - Maria Eleonora Minissi
- Instituto de Investigación e Innovación en Bioingeniería (i3B), Universitat Politécnica de Valencia, Valencia, Spain
| | - Gonzalo Teruel-García
- Instituto de Investigación e Innovación en Bioingeniería (i3B), Universitat Politécnica de Valencia, Valencia, Spain
| | - Marian Sirera
- Red Cenit, Centros de Desarrollo Cognitivo, Valencia, Spain
| | - Luis Abad
- Red Cenit, Centros de Desarrollo Cognitivo, Valencia, Spain
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Identifying autism spectrum disorder symptoms using response and gaze behavior during the Go/NoGo game CatChicken. Sci Rep 2021; 11:22012. [PMID: 34759296 PMCID: PMC8581032 DOI: 10.1038/s41598-021-01050-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 10/18/2021] [Indexed: 11/08/2022] Open
Abstract
Previous studies have found that Autism Spectrum Disorder (ASD) children scored lower during a Go/No-Go task and faced difficulty focusing their gaze on the speaker's face during a conversation. To date, however, there has not been an adequate study examining children's response and gaze during the Go/No-Go task to distinguish ASD from typical children. We investigated typical and ASD children's gaze modulation when they played a version of the Go/No-Go game. The proposed system represents the Go and the No-Go stimuli as chicken and cat characters, respectively. It tracks children's gaze using an eye tracker mounted on the monitor. Statistically significant between-group differences in spatial and auto-regressive temporal gaze-related features for 21 ASD and 31 typical children suggest that ASD children had more unstable gaze modulation during the test. Using the features that differ significantly as inputs, the AdaBoost meta-learning algorithm attained an accuracy rate of 88.6% in differentiating the ASD subjects from the typical ones.
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39
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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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40
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Rafiee F, Rezvani Habibabadi R, Motaghi M, Yousem DM, Yousem IJ. Brain MRI in Autism Spectrum Disorder: Narrative Review and Recent Advances. J Magn Reson Imaging 2021; 55:1613-1624. [PMID: 34626442 DOI: 10.1002/jmri.27949] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/27/2021] [Accepted: 09/27/2021] [Indexed: 01/31/2023] Open
Abstract
Autism spectrum disorder (ASD) is neuropsychiatric continuum of disorders characterized by persistent deficits in social communication and restricted repetitive patterns of behavior which impede optimal functioning. Early detection and intervention in ASD children can mitigate the deficits in social interaction and result in a better outcome. Various non-invasive imaging methods and molecular techniques have been developed for the early identification of ASD characteristics. There is no general consensus on specific neuroimaging features of autism; however, quantitative magnetic resonance techniques have provided valuable structural and functional information in understanding the neuropathophysiology of ASD and how the autistic brain changes during childhood, adolescence, and adulthood. In this review of decades of ASD neuroimaging research, we identify the structural, functional, and molecular imaging clues that most accurately point to the diagnosis of ASD vs. typically developing children. These studies highlight the 1) exaggerated synaptic pruning, 2) anomalous gyrification, 3) interhemispheric under- and overconnectivity, and 4) excitatory glutamate and inhibitory GABA imbalance theories of ASD. The application of these various theories to the analysis of a patient with ASD is mitigated often by superimposed comorbid neuropsychological disorders, evolving brain maturation processes, and pharmacologic and behavioral interventions that may affect the structure and function of the brain. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Faranak Rafiee
- Department of Radiology, Fara Parto Medical Imaging and Interventional Radiology Center, Shiraz, Iran
| | - Roya Rezvani Habibabadi
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institution, Baltimore, Maryland, USA
| | - Mina Motaghi
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, USA
| | - David M Yousem
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institution, Baltimore, Maryland, USA
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41
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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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42
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Lin Y, Gu Y, Xu Y, Hou S, Ding R, Ni S. Autistic spectrum traits detection and early screening: A machine learning based eye movement study. JOURNAL OF CHILD AND ADOLESCENT PSYCHIATRIC NURSING 2021; 35:83-92. [PMID: 34432921 DOI: 10.1111/jcap.12346] [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: 04/29/2021] [Revised: 06/29/2021] [Accepted: 08/06/2021] [Indexed: 11/26/2022]
Abstract
PROBLEM Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders and generally accompanied by social disorders, verbal or nonverbal communication defects, inability to concentrate and other negative symptoms that affect the autistic person's normal life. However, traditional screening methods are time-consuming and public health resources are limited. METHODS This study proposed a novel technique that combined eye-movement data and machine learning algorithms for predicting autistic traits. We converted raw eye movement data into features, trained and tested a model for early screening. FINDINGS In the preliminary experiment, 107 participants (average age = 24.84 ± 5.24 years) wore HTC Vive to watch a VR scene for 15-20 s. We explored eight classification models, among which the ensemble model performed best, with 0.73 accuracy, 0.68 precision, 0.81 recall, 0.74 F1-score, and an area under the curve of 0.90. And in the test experiment, 22 participants (average age = 12.68 ± 7.61 years) diagnosed as ASD took the experiment and the ensemble model showed a recall of 0.77. Eye movement data is an effectively distinguishable tool and we find that the proportion of time to observe figure and animal region continuously can distinguish participants with obvious and unobvious autistic traits effectively in the model. CONCLUSION This study focuses on the detection of autistic traits, and proposes a more objective and faster method for undertaking early screening, which provides possibilities to save precious time to intervene and alleviate its symptoms before making a definite diagnosis.
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Affiliation(s)
- Yiqi Lin
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong, China
| | - Yating Gu
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China
| | - Yekai Xu
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong, China
| | - Shumeng Hou
- Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong, China
| | - Ruyi Ding
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China
| | - Shiguang Ni
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China
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Rahman S, Ahmed SF, Shahid O, Arrafi MA, Ahad MAR. Automated Detection Approaches to Autism Spectrum Disorder Based on Human Activity Analysis: A Review. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09895-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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44
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Tseng AS, Shelly-Cohen M, Attia IZ, Noseworthy PA, Friedman PA, Oh JK, Lopez-Jimenez F. Spectrum bias in algorithms derived by artificial intelligence: a case study in detecting aortic stenosis using electrocardiograms. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:561-567. [PMID: 36713099 PMCID: PMC9707965 DOI: 10.1093/ehjdh/ztab061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/03/2021] [Indexed: 02/01/2023]
Abstract
Aims Spectrum bias can arise when a diagnostic test is derived from study populations with different disease spectra than the target population, resulting in poor generalizability. We used a real-world artificial intelligence (AI)-derived algorithm to detect severe aortic stenosis (AS) to experimentally assess the effect of spectrum bias on test performance. Methods and results All adult patients at the Mayo Clinic between 1 January 1989 and 30 September 2019 with transthoracic echocardiograms within 180 days after electrocardiogram (ECG) were identified. Two models were developed from two distinct patient cohorts: a whole-spectrum cohort comparing severe AS to any non-severe AS and an extreme-spectrum cohort comparing severe AS to no AS at all. Model performance was assessed. Overall, 258 607 patients had valid ECG and echocardiograms pairs. The area under the receiver operator curve was 0.87 and 0.91 for the whole-spectrum and extreme-spectrum models, respectively. Sensitivity and specificity for the whole-spectrum model was 80% and 81%, respectively, while for the extreme-spectrum model it was 84% and 84%, respectively. When applying the AI-ECG derived from the extreme-spectrum cohort to patients in the whole-spectrum cohort, the sensitivity, specificity, and area under the curve dropped to 83%, 73%, and 0.86, respectively. Conclusion While the algorithm performed robustly in identifying severe AS, this study shows that limiting datasets to clearly positive or negative labels leads to overestimation of test performance when testing an AI algorithm in the setting of classifying severe AS using ECG data. While the effect of the bias may be modest in this example, clinicians should be aware of the existence of such a bias in AI-derived algorithms.
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Affiliation(s)
- Andrew S Tseng
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Michal Shelly-Cohen
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Itzhak Z Attia
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Jae K Oh
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Francisco Lopez-Jimenez
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA,Corresponding author. Tel: +1 507 284 8087, Fax: +1 507 266 7929,
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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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Szymona B, Maciejewski M, Karpiński R, Jonak K, Radzikowska-Büchner E, Niderla K, Prokopiak A. Robot-Assisted Autism Therapy (RAAT). Criteria and Types of Experiments Using Anthropomorphic and Zoomorphic Robots. Review of the Research. SENSORS (BASEL, SWITZERLAND) 2021; 21:3720. [PMID: 34071829 PMCID: PMC8198717 DOI: 10.3390/s21113720] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/24/2021] [Accepted: 05/26/2021] [Indexed: 01/16/2023]
Abstract
Supporting the development of a child with autism is a multi-profile therapeutic work on disturbed areas, especially understanding and linguistic expression used in social communication and development of social contacts. Previous studies show that it is possible to perform some therapy using a robot. This article is a synthesis review of the literature on research with the use of robots in the therapy of children with the diagnosis of early childhood autism. The review includes scientific journals from 2005-2021. Using descriptors: ASD (Autism Spectrum Disorders), Social robots, and Robot-based interventions, an analysis of available research in PubMed, Scopus and Web of Science was done. The results showed that a robot seems to be a great tool that encourages contact and involvement in joint activities. The review of the literature indicates the potential value of the use of robots in the therapy of people with autism as a facilitator in social contacts. Robot-Assisted Autism Therapy (RAAT) can encourage child to talk or do exercises. In the second aspect (prompting during a conversation), a robot encourages eye contact and suggests possible answers, e.g., during free conversation with a peer. In the third aspect (teaching, entertainment), the robot could play with autistic children in games supporting the development of joint attention. These types of games stimulate the development of motor skills and orientation in the body schema. In future work, a validation test would be desirable to check whether children with ASD are able to do the same with a real person by learning distrust and cheating the robot.
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Affiliation(s)
- Barbara Szymona
- Sanus Medical Center, Day Treatment Center for Children with Autism, Magnoliowa 2, 20-143 Lublin, Poland;
| | - Marcin Maciejewski
- Department of Electronics and Information Technology, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
| | - Robert Karpiński
- Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
- Chair and I Department of Psychiatry, Psychotherapy, and Early Intervention, Medical University of Lublin, 20-439 Lublin, Poland
| | - Kamil Jonak
- Department of Clinical Neuropsychiatry, Medical University of Lublin, 20-439 Lublin, Poland;
- Department of Computer Science, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, 20-618 Lublin, Poland
| | - Elżbieta Radzikowska-Büchner
- Department of Plastic, Reconstructive and Maxillary Surgery, Central Clinical Hospital MSWiA, Wołoska 137, 02-507 Warsaw, Poland;
| | - Konrad Niderla
- Dream-Art sp. z o.o., Capital Park, Rejtana 67/5.16, 35-326 Rzeszów, Poland;
- University of Economics and Innovation, Projektowa 4, 20-209 Lublin, Poland
| | - Anna Prokopiak
- Alpha Medical Center, Warszawska 15, 20-803 Lublin, Poland;
- Department of Special Psychopedagogy and Special Sociopedagogy, Maria Curie-Sklodowska University in Lublin, Curie-Skłodowskiej 5, 20-031 Lublin, Poland
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Cavus N, Lawan AA, Ibrahim Z, Dahiru A, Tahir S, Abdulrazak UI, Hussaini A. A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder. J Pers Med 2021; 11:299. [PMID: 33919878 PMCID: PMC8070763 DOI: 10.3390/jpm11040299] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/06/2021] [Accepted: 04/12/2021] [Indexed: 01/22/2023] Open
Abstract
Autism spectrum disorder (ASD) is associated with significant social, communication, and behavioral challenges. The insufficient number of trained clinicians coupled with limited accessibility to quick and accurate diagnostic tools resulted in overlooking early symptoms of ASD in children around the world. Several studies have utilized behavioral data in developing and evaluating the performance of machine learning (ML) models toward quick and intelligent ASD assessment systems. However, despite the good evaluation metrics achieved by the ML models, there is not enough evidence on the readiness of the models for clinical use. Specifically, none of the existing studies reported the real-life application of the ML-based models. This might be related to numerous challenges associated with the data-centric techniques utilized and their misalignment with the conceptual basis upon which professionals diagnose ASD. The present work systematically reviewed recent articles on the application of ML in the behavioral assessment of ASD, and highlighted common challenges in the studies, and proposed vital considerations for real-life implementation of ML-based ASD screening and diagnostic systems. This review will serve as a guide for researchers, neuropsychiatrists, psychologists, and relevant stakeholders on the advances in ASD screening and diagnosis using ML.
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Affiliation(s)
- Nadire Cavus
- Department of Computer Information Systems, Near East University, Nicosia 99138, Cyprus;
- Computer Information Systems Research and Technology Centre, Near East University, Nicosia 99138, Cyprus
| | - Abdulmalik A. Lawan
- Department of Computer Information Systems, Near East University, Nicosia 99138, Cyprus;
- Department of Computer Science, Kano University of Science and Technology, Wudil 713281, Nigeria;
| | - Zurki Ibrahim
- Department of Medical Genetics, Near East University, Nicosia 99138, Cyprus;
| | - Abdullahi Dahiru
- College of Nursing and Midwifery, School of Nursing, Kano 700233, Nigeria;
| | - Sadiya Tahir
- Department of Pediatrics, Murtala Muhammad Specialist Hospital, Kano 700251, Nigeria;
| | - Usama Ishaq Abdulrazak
- Department of Emergency Medicine, Peterborough City Hospital, North West Anglia NHS Foundation Trust, Peterborough PE3 9GZ, UK;
| | - Adamu Hussaini
- Department of Computer Science, Kano University of Science and Technology, Wudil 713281, Nigeria;
- Crestic Laboratory, Universite de Reims, 51100 Reims, France
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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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50
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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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