publication . Preprint . 2020

A Convolutional Neural Network for gaze preference detection: A potential tool for diagnostics of autism spectrum disorder in children

Fernández, Dennis Núñez; Porras, Franklin Barrientos; Gilman, Robert H.; Mondonedo, Macarena Vittet; Sheen, Patricia; Zimic, Mirko;
Open Access English
  • Published: 28 Jul 2020
Abstract
Early diagnosis of autism spectrum disorder (ASD) is known to improve the quality of life of affected individuals. However, diagnosis is often delayed even in wealthier countries including the US, largely due to the fact that gold standard diagnostic tools such as the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview-Revised (ADI-R) are time consuming and require expertise to administer. This trend is even more pronounced lower resources settings due to a lack of trained experts. As a result, alternative, less technical methods that leverage the unique ways in which children with ASD react to visual stimulation in a controlled env...
Subjects
Medical Subject Headings: genetic structuresmental disorders
free text keywords: Computer Science - Computer Vision and Pattern Recognition
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31 references, page 1 of 3

1. Y. Jia, et al. Caffe: Convolutional Architecture for Fast Feature Embedding. In Proceedings of the 22nd ACM international conference on Multimedia (MM 14). ACM, New York, NY, USA, 675-678

2. Puttemans, Steven et al. “Improving Open Source Face Detection by Combining an Adapted Cascade Classification Pipeline and Active Learning.” VISIGRAPP (2017). [OpenAIRE]

3. P. Viola and M. Jones,” Rapid object detection using a boosted cascade of simple features,” Proceedings of the 2001 IEEE Computer Society Conf. on Comp. Vision and Pattern Recogn. CVPR 2001, pp. I-511-I-518 vol.1.

4. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proc. of the IEEE. (1998) 2278-2324

5. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS. (2012) 1097-1105

6. Hashemi, J., Spina, T.V., Tepper, M., Esler, A., Morellas, V., Papanikolopoulos, N., Sapiro, G.: A computer vision approach for the assessment of autism-related behavioral markers. In: Development and Learning and Epigenetic Robotics (ICDL), 2012 IEEE International Conference on. pp. 1-7. IEEE (2012)

7. Liu, W., Li, M., Yi, L.: Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework. Autism research: official journal of the International Society for Autism Research 9(8), 888-898 (2016)

8. Drimalla, H., Landwehr, N., Baskow, I., Behnia, B., Roepke, S., Dziobek, I., Scheffer, T.: Detecting autism by analyzing a simulated social interaction. Proceedings of the European Conference on Machine Learning (2018).

9. Chong, E., Chanda, K., Ye, Z., Southerland, A., Ruiz, N., Jones, R.M., Rozga, A., Rehg, J.M.: Detecting gaze towards eyes in natural social interactions and its use in child assessment. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1(3), 43 (2017)

10. American Psychiatric Association. Autism spectrum disorder. In: Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, American Psychiatric Association, Arlington, VA 2013. P.50.

11. Autism and Developmental Disabilities Monitoring Network Surveillance Year 2002 Principal investigators, “Centers for Disease Control and Prevention. Prevalence of autism spectrum disorders: autism and developmental disabilities monitoring network, 14 sites, United States, 2002,” MMWR Surveillance Summaries, vol. 56, no. 1, pp. 12-28, 2007.

12. Cidav Z, Marcus SC, Mandell DS. Implications of childhood autism for parental employment and earnings. Pediatrics. 2012; 129(4):617-23. https://doi.org/10.1542/peds.2011-2700 PMID: 22430453 [OpenAIRE]

13. Kogan MD, Strickland BB, Blumberg SJ, Singh GK, Perrin JM, van Dyck PC. A National Profile of the Health Care Experiences and Family Impact of Autism Spectrum Disorder Among Children in the United States, 2005-2006. Pediatrics. 2008; 122(6): e1149-e1158. https://doi.org/10.1542/peds.2008-1057 PMID: 19047216

14. Peacock G, Amendah D, Ouyang L, Grosse SD. Autism spectrum disorders and health care expenditures: the effects of co-occurring conditions. J Dev Behav Pediatr. 2012; 33(1):2-8. https://doi.org/10.1097/DBP.0b013e31823969de PMID: 22157409

15. Corsello CM. Early Intervention in Autism. Infants and Young Children. 2005; 18(2):74085.

31 references, page 1 of 3
Abstract
Early diagnosis of autism spectrum disorder (ASD) is known to improve the quality of life of affected individuals. However, diagnosis is often delayed even in wealthier countries including the US, largely due to the fact that gold standard diagnostic tools such as the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview-Revised (ADI-R) are time consuming and require expertise to administer. This trend is even more pronounced lower resources settings due to a lack of trained experts. As a result, alternative, less technical methods that leverage the unique ways in which children with ASD react to visual stimulation in a controlled env...
Subjects
Medical Subject Headings: genetic structuresmental disorders
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Download from
31 references, page 1 of 3

1. Y. Jia, et al. Caffe: Convolutional Architecture for Fast Feature Embedding. In Proceedings of the 22nd ACM international conference on Multimedia (MM 14). ACM, New York, NY, USA, 675-678

2. Puttemans, Steven et al. “Improving Open Source Face Detection by Combining an Adapted Cascade Classification Pipeline and Active Learning.” VISIGRAPP (2017). [OpenAIRE]

3. P. Viola and M. Jones,” Rapid object detection using a boosted cascade of simple features,” Proceedings of the 2001 IEEE Computer Society Conf. on Comp. Vision and Pattern Recogn. CVPR 2001, pp. I-511-I-518 vol.1.

4. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proc. of the IEEE. (1998) 2278-2324

5. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS. (2012) 1097-1105

6. Hashemi, J., Spina, T.V., Tepper, M., Esler, A., Morellas, V., Papanikolopoulos, N., Sapiro, G.: A computer vision approach for the assessment of autism-related behavioral markers. In: Development and Learning and Epigenetic Robotics (ICDL), 2012 IEEE International Conference on. pp. 1-7. IEEE (2012)

7. Liu, W., Li, M., Yi, L.: Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework. Autism research: official journal of the International Society for Autism Research 9(8), 888-898 (2016)

8. Drimalla, H., Landwehr, N., Baskow, I., Behnia, B., Roepke, S., Dziobek, I., Scheffer, T.: Detecting autism by analyzing a simulated social interaction. Proceedings of the European Conference on Machine Learning (2018).

9. Chong, E., Chanda, K., Ye, Z., Southerland, A., Ruiz, N., Jones, R.M., Rozga, A., Rehg, J.M.: Detecting gaze towards eyes in natural social interactions and its use in child assessment. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1(3), 43 (2017)

10. American Psychiatric Association. Autism spectrum disorder. In: Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, American Psychiatric Association, Arlington, VA 2013. P.50.

11. Autism and Developmental Disabilities Monitoring Network Surveillance Year 2002 Principal investigators, “Centers for Disease Control and Prevention. Prevalence of autism spectrum disorders: autism and developmental disabilities monitoring network, 14 sites, United States, 2002,” MMWR Surveillance Summaries, vol. 56, no. 1, pp. 12-28, 2007.

12. Cidav Z, Marcus SC, Mandell DS. Implications of childhood autism for parental employment and earnings. Pediatrics. 2012; 129(4):617-23. https://doi.org/10.1542/peds.2011-2700 PMID: 22430453 [OpenAIRE]

13. Kogan MD, Strickland BB, Blumberg SJ, Singh GK, Perrin JM, van Dyck PC. A National Profile of the Health Care Experiences and Family Impact of Autism Spectrum Disorder Among Children in the United States, 2005-2006. Pediatrics. 2008; 122(6): e1149-e1158. https://doi.org/10.1542/peds.2008-1057 PMID: 19047216

14. Peacock G, Amendah D, Ouyang L, Grosse SD. Autism spectrum disorders and health care expenditures: the effects of co-occurring conditions. J Dev Behav Pediatr. 2012; 33(1):2-8. https://doi.org/10.1097/DBP.0b013e31823969de PMID: 22157409

15. Corsello CM. Early Intervention in Autism. Infants and Young Children. 2005; 18(2):74085.

31 references, page 1 of 3
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