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Qi SY, Zhang SJ, Lin LL, Li YR, Chen JG, Ni YC, Du X, Zhang J, Ge P, Liu GH, Wu JY, Lin S, Gong M, Lin JW, Chen LF, He LL, Lin D. Quantifying attention in children with intellectual and developmental disabilities through multicenter electrooculogram signal analysis. Sci Rep 2024; 14:22186. [PMID: 39333619 DOI: 10.1038/s41598-024-70304-x] [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: 04/25/2024] [Accepted: 08/14/2024] [Indexed: 09/29/2024] Open
Abstract
In a multicenter case-control investigation, we assessed the efficacy of the Electrooculogram Signal Analysis (EOG-SA) method, which integrates attention-related visual evocation, electrooculography, and nonlinear analysis, for distinguishing between intellectual and developmental disabilities (IDD) and typical development (TD) in children. Analyzing 127 participants (63 IDD, 64 TD), we applied nonlinear dynamics for feature extraction. Results indicated EOG-SA's capability to distinguish IDD, with higher template thresholds and Correlation Dimension values correlating with clinical severity. The template threshold proved a robust indicator, with higher values denoting severe IDD. Discriminative metrics showed areas under the curve of 0.91 (template threshold) and 0.85/0.91 (D2), with sensitivities and specificities of 77.6%/95.9% and 93.5%/71.0%, respectively. EOG-SA emerges as a promising tool, offering interpretable neural biomarkers for early and nuanced diagnosis of IDD.
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Affiliation(s)
- Shi-Yi Qi
- Department of Acupuncture and Tuina, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China
| | - Si-Jia Zhang
- Department of Acupuncture and Tuina, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China
- Tongxiang Hospital of Traditional Chinese Medicine, Tongxiang, Zhejiang Province, China
| | - Li-Li Lin
- Department of Acupuncture and Tuina, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China
- Institute of Acupuncture and Meridian, Fujian Academy of Chinese Medical Sciences, Fuzhou, Fujian Province, China
| | - Yu-Rong Li
- Department of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian Province, China
| | - Jian-Guo Chen
- Department of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian Province, China
| | - You-Cong Ni
- School of Computer and Cyberspace Security, Fujian Normal University, Fuzhou, Fujian Province, China
| | - Xin Du
- School of Computer and Cyberspace Security, Fujian Normal University, Fuzhou, Fujian Province, China
| | - Jie Zhang
- Department of Rehabilitation, The Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China
| | - Pin Ge
- Fujian Maternity and Child Health Hospital, Fuzhou, Fujian Province, China
| | - Gui-Hua Liu
- Fujian Maternity and Child Health Hospital, Fuzhou, Fujian Province, China
| | - Jiang-Yun Wu
- Department of Rehabilitation, The Third People's Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China
| | - Shen Lin
- Fujian Maternity and Child Health Hospital, Fuzhou, Fujian Province, China
| | - Meng Gong
- Department of Acupuncture and Tuina, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China
| | - Jin-Wen Lin
- Department of Acupuncture and Tuina, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China
| | - Lan-Fang Chen
- Department of Rehabilitation, The Third People's Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China
| | - Ling-Ling He
- Department of Acupuncture and Tuina, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China
| | - Dong Lin
- Department of Acupuncture and Tuina, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China.
- Department of Rehabilitation, The Third People's Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China.
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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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Sha M, Alqahtani A, Alsubai S, Dutta AK. Modified Meta Heuristic BAT with ML Classifiers for Detection of Autism Spectrum Disorder. Biomolecules 2023; 14:48. [PMID: 38254648 PMCID: PMC10813510 DOI: 10.3390/biom14010048] [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: 11/20/2023] [Revised: 12/22/2023] [Accepted: 12/27/2023] [Indexed: 01/24/2024] Open
Abstract
ASD (autism spectrum disorder) is a complex developmental and neurological disorder that impacts the social life of the affected person by disturbing their capability for interaction and communication. As it is a behavioural disorder, early treatment will improve the quality of life of ASD patients. Traditional screening is carried out with behavioural assessment through trained physicians, which is expensive and time-consuming. To resolve the issue, several conventional methods strive to achieve an effective ASD identification system, but are limited by handling large data sets, accuracy, and speed. Therefore, the proposed identification system employed the MBA (modified bat) algorithm based on ANN (artificial neural networks), modified ANN (modified artificial neural networks), DT (decision tree), and KNN (k-nearest neighbours) for the classification of ASD in children and adolescents. A BA (bat algorithm) is utilised for the automatic zooming capability, which improves the system's efficacy by excellently finding the solutions in the identification system. Conversely, BA is effective in the identification, it still has certain drawbacks like speed, accuracy, and falls into local extremum. Therefore, the proposed identification system modifies the BA optimisation with random perturbation of trends and optimal orientation. The dataset utilised in the respective model is the Q-chat-10 dataset. This dataset contains data of four stages of age groups such as toddlers, children, adolescents, and adults. To analyse the quality of the dataset, dataset evaluation mechanism, such as the Chi-Squared Statistic and p-value, are used in the respective research. The evaluation signifies the relation of the dataset with respect to the proposed model. Further, the performance of the proposed detection system is examined with certain performance metrics to calculate its efficiency. The outcome revealed that the modified ANN classifier model attained an accuracy of 1.00, ensuring improved performance when compared with other state-of-the-art methods. Thus, the proposed model was intended to assist physicians and researchers in enhancing the diagnosis of ASD to improve the standard of life of ASD patients.
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Affiliation(s)
- Mohemmed Sha
- Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia;
| | - Abdullah Alqahtani
- Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia;
| | - Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia;
| | - Ashit Kumar Dutta
- Department of Computer Science and Information Systems, College of Applied Sciences, Almaarefa University, Riyadh 11597, Saudi Arabia;
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Franco FO, Oliveira JS, Portolese J, Sumiya FM, Silva AF, Machado-Lima A, Nunes FLS, Brentani H. Computer-aided autism diagnosis using visual attention models and eye-tracking: replication and improvement proposal. BMC Med Inform Decis Mak 2023; 23:285. [PMID: 38098001 PMCID: PMC10722824 DOI: 10.1186/s12911-023-02389-9] [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: 05/11/2023] [Accepted: 12/04/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Autism Spectrum Disorder (ASD) diagnosis can be aided by approaches based on eye-tracking signals. Recently, the feasibility of building Visual Attention Models (VAMs) from features extracted from visual stimuli and their use for classifying cases and controls has been demonstrated using Neural Networks and Support Vector Machines. The present work has three aims: 1) to evaluate whether the trained classifier from the previous study was generalist enough to classify new samples with a new stimulus; 2) to replicate the previously approach to train a new classifier with a new dataset; 3) to evaluate the performance of classifiers obtained by a new classification algorithm (Random Forest) using the previous and the current datasets. METHODS The previously approach was replicated with a new stimulus and new sample, 44 from the Typical Development group and 33 from the ASD group. After the replication, Random Forest classifier was tested to substitute Neural Networks algorithm. RESULTS The test with the trained classifier reached an AUC of 0.56, suggesting that the trained classifier requires retraining of the VAMs when changing the stimulus. The replication results reached an AUC of 0.71, indicating the potential of generalization of the approach for aiding ASD diagnosis, as long as the stimulus is similar to the originally proposed. The results achieved with Random Forest were superior to those achieved with the original approach, with an average AUC of 0.95 for the previous dataset and 0.74 for the new dataset. CONCLUSION In summary, the results of the replication experiment were satisfactory, which suggests the robustness of the approach and the VAM-based approaches feasibility to aid in ASD diagnosis. The proposed method change improved the classification performance. Some limitations are discussed and additional studies are encouraged to test other conditions and scenarios.
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Affiliation(s)
- Felipe O Franco
- Interunit PostGraduate Program on Bioinformatics, Institute of Mathematics and Statistics (IME), University of São Paulo (USP), 05508-090, São Paulo, SP, Brazil.
- Department of Psychiatry, University of São Paulo's School of Medicine (FMUSP), 05403-903, São Paulo-SP, Brazil.
| | - Jessica S Oliveira
- School of Arts, Sciences and Humanities (EACH), University of São Paulo (USP), 03828-000, São Paulo-SP, Brazil
| | - Joana Portolese
- Department of Psychiatry, University of São Paulo's School of Medicine (FMUSP), 05403-903, São Paulo-SP, Brazil
| | - Fernando M Sumiya
- Department of Psychiatry, University of São Paulo's School of Medicine (FMUSP), 05403-903, São Paulo-SP, Brazil
| | - Andréia F Silva
- Department of Psychiatry, University of São Paulo's School of Medicine (FMUSP), 05403-903, São Paulo-SP, Brazil
| | - Ariane Machado-Lima
- School of Arts, Sciences and Humanities (EACH), University of São Paulo (USP), 03828-000, São Paulo-SP, Brazil
| | - Fatima L S Nunes
- School of Arts, Sciences and Humanities (EACH), University of São Paulo (USP), 03828-000, São Paulo-SP, Brazil
| | - Helena Brentani
- Department of Psychiatry, University of São Paulo's School of Medicine (FMUSP), 05403-903, São Paulo-SP, Brazil
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5
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Silva ES, Barros MCDM, Borten JBL, Carlini LP, Balda RDCX, Orsi RN, Heiderich TM, Thomaz CE, Guinsburg R. Pediatricians' focus of sight at pain assessment during a neonatal heel puncture. REVISTA PAULISTA DE PEDIATRIA : ORGAO OFICIAL DA SOCIEDADE DE PEDIATRIA DE SAO PAULO 2023; 42:e2023089. [PMID: 38088681 PMCID: PMC10712942 DOI: 10.1590/1984-0462/2024/42/2023089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 09/18/2023] [Indexed: 12/17/2023]
Abstract
OBJECTIVE To evaluate the focus of pediatricians' gaze during the heel prick of neonates. METHODS Prospective study in which pediatricians wearing eye tracker glasses evaluated neonatal pain before/after a heel prtick. Pediatricians scored the pain they perceived in the neonate in a verbal analogue numerical scale (0=no pain; 10=maximum pain). The outcomes measured were number and time of visual fixations in upper face, lower face, and hands, in two 10-second periods, before (pre) and after the puncture (post). These outcomes were compared between the periods, and according to pediatricians' pain perception: absent/mild (score: 0-5) and moderate/intense (score: 6-10). RESULTS 24 pediatricians (31 years old, 92% female) evaluated 24 neonates. The median score attributed to neonatal pain during the heel prick was 7.0 (Interquartile range: 5-8). Compared to pre-, in the post-periods, more pediatricians fixed their gaze on the lower face (63 vs. 92%; p=0.036) and the number of visual fixations was greater on the lower face (2.0 vs. 5.0; p=0.018). There was no difference in the number and time of visual fixations according to the intensity of pain. CONCLUSIONS At bedside, pediatricians change their focus of attention on the neonatal face after a painful procedure, focusing mainly on the lower part of the face.
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Affiliation(s)
- Erica Souza Silva
- Universidade Federal de São Paulo, Escola Paulista de Medicina, Departamento de Pediatria, Disciplina de Pediatria Neonatal – São Paulo, SP, Brasil
| | - Marina Carvalho de Moraes Barros
- Universidade Federal de São Paulo, Escola Paulista de Medicina, Departamento de Pediatria, Disciplina de Pediatria Neonatal – São Paulo, SP, Brasil
| | - Julia Baptista Lopes Borten
- Universidade Federal de São Paulo, Escola Paulista de Medicina, Departamento de Pediatria, Disciplina de Pediatria Neonatal – São Paulo, SP, Brasil
| | - Lucas Pereira Carlini
- Centro Universitario FEI, Departamento de Engenharia Elétrica, Laboratório de Processamento de Imagens – São Bernardo do Campo, SP, Brasil
| | - Rita de Cássia Xavier Balda
- Universidade Federal de São Paulo, Escola Paulista de Medicina, Departamento de Pediatria, Disciplina de Pediatria Neonatal – São Paulo, SP, Brasil
| | - Rafael Nobre Orsi
- Centro Universitario FEI, Departamento de Engenharia Elétrica, Laboratório de Processamento de Imagens – São Bernardo do Campo, SP, Brasil
| | - Tatiany Marcondes Heiderich
- Centro Universitario FEI, Departamento de Engenharia Elétrica, Laboratório de Processamento de Imagens – São Bernardo do Campo, SP, Brasil
| | - Carlos Eduardo Thomaz
- Centro Universitario FEI, Departamento de Engenharia Elétrica, Laboratório de Processamento de Imagens – São Bernardo do Campo, SP, Brasil
| | - Ruth Guinsburg
- Universidade Federal de São Paulo, Escola Paulista de Medicina, Departamento de Pediatria, Disciplina de Pediatria Neonatal – São Paulo, SP, Brasil
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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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Jiang H, Hou Y, Miao H, Ye H, Gao M, Li X, Jin R, Liu J. Eye tracking based deep learning analysis for the early detection of diabetic retinopathy: A pilot study. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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Wu X, Deng H, Jian S, Chen H, Li Q, Gong R, Wu J. Global trends and hotspots in the digital therapeutics of autism spectrum disorders: a bibliometric analysis from 2002 to 2022. Front Psychiatry 2023; 14:1126404. [PMID: 37255688 PMCID: PMC10225518 DOI: 10.3389/fpsyt.2023.1126404] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 04/26/2023] [Indexed: 06/01/2023] Open
Abstract
Introduction Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder that has become a major cause of disability in children. Digital therapeutics (DTx) delivers evidence-based therapeutic interventions to patients that are driven by software to prevent, manage, or treat a medical disorder or disease. This study objectively analyzed the current research status of global DTx in ASD from 2002 to 2022, aiming to explore the current global research status and trends in the field. Methods The Web of Science database was searched for articles about DTx in ASD from January 2002 to October 2022. CiteSpace was used to analyze the co-occurrence of keywords in literature, partnerships between authors, institutions, and countries, the sudden occurrence of keywords, clustering of keywords over time, and analysis of references, cited authors, and cited journals. Results A total of 509 articles were included. The most productive country and institution were the United States and Vanderbilt University. The largest contributing authors were Warren, Zachary, and Sarkar, Nilanjan. The most-cited journal was the Journal of Autism and Developmental Disorders. The most-cited and co-cited articles were Brian Scarselati (Robots for Use in Autism Research, 2012) and Ralph Adolphs (Abnormal processing of social information from faces in autism, 2001). "Artificial Intelligence," "machine learning," "Virtual Reality," and "eye tracking" were common new and cutting-edge trends in research on DTx in ASD. Discussion The use of DTx in ASD is developing rapidly and gaining the attention of researchers worldwide. The publications in this field have increased year by year, mainly concentrated in the developed countries, especially in the United States. Both Vanderbilt University and Yale University are very important institutions in the field. The researcher from Vanderbilt University, Warren and Zachary, his dynamics or achievements in the field is also more worth our attention. The application of new technologies such as virtual reality, machine learning, and eye-tracking in this field has driven the development of DTx on ASD and is currently a popular research topic. More cross-regional and cross-disciplinary collaborations are recommended to advance the development and availability of DTx.
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Affiliation(s)
- Xuesen Wu
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Haiyin Deng
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Shiyun Jian
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Huian Chen
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Qing Li
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Ruiyu Gong
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Jingsong Wu
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
- Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
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Martínez-Lorca M, Gómez Fernández D. Rendimiento de los estímulos visuales en el diagnóstico del TEA por Eye Tracking: Revisión Sistemática. REVISTA DE INVESTIGACIÓN EN LOGOPEDIA 2023. [DOI: 10.5209/rlog.83937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023] Open
Abstract
El eye-tracking es una herramienta diagnóstica que tiene como fin el estudio del comportamiento de la mirada a través del escaneo de ojos para observar el seguimiento ocular, cómo se distribuye la mirada y la precisión de los movimientos oculares. Este sistema se ha utilizado con niños/as del Trastorno del Espectro Autista. El objetivo de esta revisión sistemática ha sido analizar el rendimiento de los estímulos visuales en el diagnóstico del TEA por método eye tracking. Para ello, se siguió la metodología PRISMA, realizando una búsqueda en las bases de datos PubMed, Science Direct y Scopus, así como, Reseach Gate. Se seleccionaron 22 artículos que cumplían los criterios de inclusión con experimentos unifactoriales, experimentales factoriales y cuasiexperimentales. Todos los experimentos han tenido un grupo control compuesto de muestra con participantes con desarrollo normotípico y de un grupo de caso compuesto de muestra con participantes TEA. Esta revisión sintetiza en tres categorías en base a las características del estímulo usado en el diagnóstico (estímulos sociales, no sociales y por confrontación), el análisis del rendimiento de los estímulos visuales, de manera que los estímulos sociales y los estímulos por confrontación van a ser eficaces para establecer un diagnóstico preciso de TEA puesto que permiten realizar un cribado de ambos grupos y establecer un riesgo temprano del trastorno.
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Previously Marzena Szkodo MOR, Micai M, Caruso A, Fulceri F, Fazio M, Scattoni ML. Technologies to support the diagnosis and/or treatment of neurodevelopmental disorders: A systematic review. Neurosci Biobehav Rev 2023; 145:105021. [PMID: 36581169 DOI: 10.1016/j.neubiorev.2022.105021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/13/2022] [Accepted: 12/23/2022] [Indexed: 12/27/2022]
Abstract
In recent years, there has been a great interest in utilizing technology in mental health research. The rapid technological development has encouraged researchers to apply technology as a part of a diagnostic process or treatment of Neurodevelopmental Disorders (NDDs). With the large number of studies being published comes an urgent need to inform clinicians and researchers about the latest advances in this field. Here, we methodically explore and summarize findings from studies published between August 2019 and February 2022. A search strategy led to the identification of 4108 records from PubMed and APA PsycInfo databases. 221 quantitative studies were included, covering a wide range of technologies used for diagnosis and/or treatment of NDDs, with the biggest focus on Autism Spectrum Disorder (ASD). The most popular technologies included machine learning, functional magnetic resonance imaging, electroencephalogram, magnetic resonance imaging, and neurofeedback. The results of the review indicate that technology-based diagnosis and intervention for NDD population is promising. However, given a high risk of bias of many studies, more high-quality research is needed.
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Affiliation(s)
| | - Martina Micai
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
| | - Angela Caruso
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
| | - Francesca Fulceri
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
| | - Maria Fazio
- Department of Mathematics, Computer Science, Physics and Earth Sciences (MIFT), University of Messina, Viale F. Stagno d'Alcontres, 31, 98166 Messina, Italy.
| | - Maria Luisa Scattoni
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
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Mengi M, Malhotra D. A systematic literature review on traditional to artificial intelligence based socio-behavioral disorders diagnosis in India: Challenges and future perspectives. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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12
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Kanhirakadavath MR, Chandran MSM. Investigation of Eye-Tracking Scan Path as a Biomarker for Autism Screening Using Machine Learning Algorithms. Diagnostics (Basel) 2022; 12:518. [PMID: 35204608 PMCID: PMC8871384 DOI: 10.3390/diagnostics12020518] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/11/2022] [Accepted: 02/12/2022] [Indexed: 02/06/2023] Open
Abstract
Autism spectrum disorder is a group of disorders marked by difficulties with social skills, repetitive activities, speech, and nonverbal communication. Deficits in paying attention to, and processing, social stimuli are common for children with autism spectrum disorders. It is uncertain whether eye-tracking technologies can assist in establishing an early biomarker of autism based on the children's atypical visual preference patterns. In this study, we used machine learning methods to test the applicability of eye-tracking data in children to aid in the early screening of autism. We looked into the effectiveness of various machine learning techniques to discover the best model for predicting autism using visualized eye-tracking scan path images. We adopted three traditional machine learning models and a deep neural network classifier to run experimental trials. This study employed a publicly available dataset of 547 graphical eye-tracking scan paths from 328 typically developing and 219 autistic children. We used image augmentation to populate the dataset to prevent the model from overfitting. The deep neural network model outperformed typical machine learning approaches on the populated dataset, with 97% AUC, 93.28% sensitivity, 91.38% specificity, 94.46% NPV, and 90.06% PPV (fivefold cross-validated). The findings strongly suggest that eye-tracking data help clinicians for a quick and reliable autism screening.
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Affiliation(s)
- Mujeeb Rahman Kanhirakadavath
- School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India;
- Department of Biomedical Engineering, Ajman University, Ajman P.O. Box 346, United Arab Emirates
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13
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Eye Tracking-Based Diagnosis and Early Detection of Autism Spectrum Disorder Using Machine Learning and Deep Learning Techniques. ELECTRONICS 2022. [DOI: 10.3390/electronics11040530] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Eye tracking is a useful technique for detecting autism spectrum disorder (ASD). One of the most important aspects of good learning is the ability to have atypical visual attention. The eye-tracking technique provides useful information about children’s visual behaviour for early and accurate diagnosis. It works by scanning the paths of the eyes to extract a sequence of eye projection points on the image to analyse the behaviour of children with autism. In this study, three artificial-intelligence techniques were developed, namely, machine learning, deep learning, and a hybrid technique between them, for early diagnosis of autism. The first technique, neural networks [feedforward neural networks (FFNNs) and artificial neural networks (ANNs)], is based on feature classification extracted by a hybrid method between local binary pattern (LBP) and grey level co-occurrence matrix (GLCM) algorithms. This technique achieved a high accuracy of 99.8% for FFNNs and ANNs. The second technique used a pre-trained convolutional neural network (CNN) model, such as GoogleNet and ResNet-18, on the basis of deep feature map extraction. The GoogleNet and ResNet-18 models achieved high performances of 93.6% and 97.6%, respectively. The third technique used the hybrid method between deep learning (GoogleNet and ResNet-18) and machine learning (SVM), called GoogleNet + SVM and ResNet-18 + SVM. This technique depends on two blocks. The first block used CNN to extract deep feature maps, whilst the second block used SVM to classify the features extracted from the first block. This technique proved its high diagnostic ability, achieving accuracies of 95.5% and 94.5% for GoogleNet + SVM and ResNet-18 + SVM, respectively.
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Tayanloo-Beik A, Hamidpour SK, Abedi M, Shojaei H, Tavirani MR, Namazi N, Larijani B, Arjmand B. Zebrafish Modeling of Autism Spectrum Disorders, Current Status and Future Prospective. Front Psychiatry 2022; 13:911770. [PMID: 35911241 PMCID: PMC9329562 DOI: 10.3389/fpsyt.2022.911770] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/22/2022] [Indexed: 11/13/2022] Open
Abstract
Autism spectrum disorder (ASD) refers to a complicated range of childhood neurodevelopmental disorders which can occur via genetic or non-genetic factors. Clinically, ASD is associated with problems in relationships, social interactions, and behaviors that pose many challenges for children with ASD and their families. Due to the complexity, heterogeneity, and association of symptoms with some neuropsychiatric disorders such as ADHD, anxiety, and sleep disorders, clinical trials have not yielded reliable results and there still remain challenges in drug discovery and development pipeline for ASD patients. One of the main steps in promoting lead compounds to the suitable drug for commercialization is preclinical animal testing, in which the efficacy and toxicity of candidate drugs are examined in vivo. In recent years, zebrafish have been able to attract the attention of many researchers in the field of neurological disorders such as ASD due to their outstanding features. The presence of orthologous genes for ASD modeling, the anatomical similarities of parts of the brain, and similar neurotransmitter systems between zebrafish and humans are some of the main reasons why scientists draw attention to zebrafish as a prominent animal model in preclinical studies to discover highly effective treatment approaches for the ASD through genetic and non-genetic modeling methods.
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Affiliation(s)
- Akram Tayanloo-Beik
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Shayesteh Kokabi Hamidpour
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mina Abedi
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamide Shojaei
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Nazli Namazi
- Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Bagher Larijani
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Babak Arjmand
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
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Zhang S, Wang S, Liu R, Dong H, Zhang X, Tai X. A bibliometric analysis of research trends of artificial intelligence in the treatment of autistic spectrum disorders. Front Psychiatry 2022; 13:967074. [PMID: 36104988 PMCID: PMC9464861 DOI: 10.3389/fpsyt.2022.967074] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 07/28/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Autism Spectrum Disorder (ASD) is a serious neurodevelopmental disorder that has become the leading cause of disability in children. Artificial intelligence (AI) is a potential solution to this issue. This study objectively analyzes the global research situation of AI in the treatment of ASD from 1995 to 2022, aiming to explore the global research status and frontier trends in this field. METHODS Web of Science (WoS) and PubMed databese were searched for Literature related to AI on ASD from 1995 to April 2022. CiteSpace, VOSviewer, Pajek and Scimago Graphica were used to analyze the collaboration between countries/institutions/authors, clusters and bursts of keywords, as well as analyses on references. RESULTS A total of 448 literature were included, the total number of literature has shown an increasing trend. The most productive country and institution were the USA, and Vanderbilt University. The authors with the greatest contributions were Warren, Zachary, Sakar, Nilanjan and Swanson, Amy. the most prolific and cited journal is Journal of Autism and Developmental Disorders, the highest cited and co-cited articles were Dautenhahn (Socially intelligent robots: dimensions of human-robot interaction 2007) and Scassellati B (Robots for Use in Autism Research 2012). "Artificial Intelligence", "Brain Computer Interface" and "Humanoid Robot" were the hotspots and frontier trends of AI on ASD. CONCLUSION The application of AI in the treatment of ASD has attracted the attention of researchers all over the world. The education, social function and joint attention of children with ASD are the most concerned issues for global researchers. Robots shows gratifying advantages in these issues and have become the most commonly used technology. Wearable devices and brain-computer interface (BCI) were emerging AI technologies in recent years, which is the direction of further exploration. Restoring social function in individuals with ASD is the ultimate aim and driving force of research in the future.
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Affiliation(s)
- Shouyao Zhang
- School of Second Clinical Medicine/The Second Affiliated Hospital, Yunnan University of Chinese Medicine, Kunming, China
| | - Shuang Wang
- School of Second Clinical Medicine/The Second Affiliated Hospital, Yunnan University of Chinese Medicine, Kunming, China
| | - Ruilu Liu
- School of Second Clinical Medicine/The Second Affiliated Hospital, Yunnan University of Chinese Medicine, Kunming, China
| | - Hang Dong
- School of Second Clinical Medicine/The Second Affiliated Hospital, Yunnan University of Chinese Medicine, Kunming, China
| | - Xinghe Zhang
- School of Second Clinical Medicine/The Second Affiliated Hospital, Yunnan University of Chinese Medicine, Kunming, China
| | - Xiantao Tai
- School of Second Clinical Medicine/The Second Affiliated Hospital, Yunnan University of Chinese Medicine, Kunming, China
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Vortmann LM, Putze F. Combining Implicit and Explicit Feature Extraction for Eye Tracking: Attention Classification Using a Heterogeneous Input. SENSORS 2021; 21:s21248205. [PMID: 34960295 PMCID: PMC8707750 DOI: 10.3390/s21248205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/29/2021] [Accepted: 12/02/2021] [Indexed: 01/24/2023]
Abstract
Statistical measurements of eye movement-specific properties, such as fixations, saccades, blinks, or pupil dilation, are frequently utilized as input features for machine learning algorithms applied to eye tracking recordings. These characteristics are intended to be interpretable aspects of eye gazing behavior. However, prior research has demonstrated that when trained on implicit representations of raw eye tracking data, neural networks outperform these traditional techniques. To leverage the strengths and information of both feature sets, we integrated implicit and explicit eye tracking features in one classification approach in this work. A neural network was adapted to process the heterogeneous input and predict the internally and externally directed attention of 154 participants. We compared the accuracies reached by the implicit and combined features for different window lengths and evaluated the approaches in terms of person- and task-independence. The results indicate that combining implicit and explicit feature extraction techniques for eye tracking data improves classification results for attentional state detection significantly. The attentional state was correctly classified during new tasks with an accuracy better than chance, and person-independent classification even outperformed person-dependently trained classifiers for some settings. For future experiments and applications that require eye tracking data classification, we suggest to consider implicit data representation in addition to interpretable explicit features.
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Zammarchi G, Conversano C. Application of Eye Tracking Technology in Medicine: A Bibliometric Analysis. Vision (Basel) 2021; 5:56. [PMID: 34842855 PMCID: PMC8628933 DOI: 10.3390/vision5040056] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/04/2021] [Accepted: 11/08/2021] [Indexed: 11/16/2022] Open
Abstract
Eye tracking provides a quantitative measure of eye movements during different activities. We report the results from a bibliometric analysis to investigate trends in eye tracking research applied to the study of different medical conditions. We conducted a search on the Web of Science Core Collection (WoS) database and analyzed the dataset of 2456 retrieved articles using VOSviewer and the Bibliometrix R package. The most represented area was psychiatry (503, 20.5%) followed by neuroscience (465, 18.9%) and psychology developmental (337, 13.7%). The annual scientific production growth was 11.14% and showed exponential growth with three main peaks in 2011, 2015 and 2017. Extensive collaboration networks were identified between the three countries with the highest scientific production, the USA (35.3%), the UK (9.5%) and Germany (7.3%). Based on term co-occurrence maps and analyses of sources of articles, we identified autism spectrum disorders as the most investigated condition and conducted specific analyses on 638 articles related to this topic which showed an annual scientific production growth of 16.52%. The majority of studies focused on autism used eye tracking to investigate gaze patterns with regards to stimuli related to social interaction. Our analysis highlights the widespread and increasing use of eye tracking in the study of different neurological and psychiatric conditions.
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Affiliation(s)
- Gianpaolo Zammarchi
- Department of Economics and Business Sciences, University of Cagliari, 09123 Cagliari, Italy;
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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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