Developing an eye-tracking algorithm as a potential tool for early diagnosis of autism spectrum disorder in children

Vargas-Cuentas, Natalia I.; Román-Gonzalez, Avid; Gilman, Robert H.; Barrientos, Franklin; Ting, James; Hidalgo, Daniela; Jensen, Kelly; Zimic, Mirko

doi:10.1371/journal.pone.0188826

Cited by 66 publications

(35 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…After each weak classifier classifies the input samples, the final classification result can be acquired by the combination of results obtained from the weak classifiers. The probability of a test sample to be classified into the j-th category is given by Equation (8)…”

Section: ) Strong Classifier Construction Methodsmentioning

confidence: 99%

“…In terms of existing research results throughout the world, research on congenital nystagmus is the most in-depth research [2][3][4][5]; it focuses on the treatment of nystagmus and its related diseases. In paper [6][7][8][9][10], eye-tracking technologies were applied to research in children with autism spectrum disorder (ASD). Natalia I. Vargas-Cuentas et al [8] developed an eye-tracking algorithm as a potential tool for early diagnosis of ASD in children.…”

Section: Introductionmentioning

confidence: 99%

“…In paper [6][7][8][9][10], eye-tracking technologies were applied to research in children with autism spectrum disorder (ASD). Natalia I. Vargas-Cuentas et al [8] developed an eye-tracking algorithm as a potential tool for early diagnosis of ASD in children. Since changes in people's psychological and physiological statuses are likely to be reflected in pupil size, Antoinette et al [9] applied the pupil adaptation ability as a quantitative test method for children with autism.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Disease Classification Based on Synthesis of Multiple Long Short-Term Memory Classifiers Corresponding to Eye Movement Features

Mao

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Medical research confirms that eye movement abnormalities are related to a variety of psychological activities, mental disorders and physical diseases. However, as the specific manifestations of various diseases in terms of eye movement disorders remain unclear, the accurate diagnosis of diseases according to eye movement is difficult. In this paper, a deep neural network (DNN) method is employed to establish a disease discrimination model according to eye movement. First, multiple eye-tracking experiments are designed to obtain eye images. Second, pupil characteristics, including position and size, are extracted, and the feature vectors of eye movement are obtained from the normalized pupil information. Based on a long short-term memory (LSTM) network, a classifier that corresponds to each feature, which is referred to as a weak classifier, is built. The experimental samples are preclassified, and the classification ability of each weak classifier for different diseases is also calculated. Last, a strong classifier is achieved for disease discrimination by synthesizing all the weak classifiers and their classification abilities. By classification testing for three categories of healthy controls, brain injury patients and vertigo patients, the experimental results demonstrated the efficiency of this method. With the deep learning method, more medical information can be excavated from eye movement to improve the values in disease diagnosis.

show abstract

Section: ) Strong Classifier Construction Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Disease Classification Based on Synthesis of Multiple Long Short-Term Memory Classifiers Corresponding to Eye Movement Features

Mao

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…It has become a tool for visual examination of attention and cognition, and the diagnosis of both mental disorders and neurodegenerative diseases. It is also used in the description of functioning of people with autism, ADHD or diagnosis and description of mild impairments [10,11,12,13,14], and even the risk assessment of suicide tendencies [15]. There are also papers in which eye tracking was used for other purposes, such as a therapy or improvement of medical education [16,17,18], it is also increasingly used to test the usefulness of medical devices [19].…”

Section: Introductionmentioning

confidence: 99%

The effects of interior design on wellness – Eye tracking analysis in determining emotional experience of architectural space. A survey on a group of volunteers from the Lublin Region, Eastern Poland

Tuszyńska-Bogucka¹,

Kwiatkowski²,

Chmielewska³

et al. 2020

Ann Agric Environ Med.

View full text Add to dashboard Cite

Introduction and objective. Using the concepts of Ulrich's theory of supportive design and Malkin's healing environment, an eye tracking experiment was designed in order to measure respondents' reactions while looking at visualisations of various interiors, with the aim of verifying whether certain parameters of an interior are related to emotional reactions in terms of positive stimulation, and the sense of security and comfort. Materials and method. 12 boards were designed, incorporating standard features of an interior, i.e. (1) proportions, (2) lighting, (3) colour scheme of a room, as well as (4) the colours and spatial arrangement of furnishings. Respondents' reactions were recorded with an eye tracker Tobii TX300 and supplemented by self-descriptions of emotional reactions. Results. The results showed that the varying spatial and colour arrangements presented in the interior visualisations provoked different emotional responses, confirmed by pupil reaction parameters, as measured by the eye tracking device. Conclusions. Architectural space can have a diverse emotional significance and impact on an individual's emotional state. This is an important conclusion from the point of view of optimising and creating the so-called supportive and healing environment. The results have implications for the interpretation of the pupil diameter as an index of emotional reactions to different architectural space visualisations. Testing the eye tracker as a method helpful in diagnosing the emotional reactions to features of the interior is justified, and can provide an effective tool for early diagnosis of the impact of architectural space on the well-being of individuals. It can also be a good form of testing the emotional significance of architectural designs before they are implemented.

show abstract

“…They analyzed the relationship between the children's eye gaze performance and social communication outcome measures that are typically used in ASD clinical trials, and it was proposed that eye gaze tracking could be a non-invasive, quantitative, and objective biomarker associated with social communication abilities in children with ASD. Vargas-Cuentas et al (2017) designed an eye tracking algorithm to measure the gaze preferences of children with ASD and of a healthy control group when viewing social scenes and abstract scenes shown simultaneously on the left and the right sides of the screen, respectively, and achieved very high accuracy in the classification of children with ASD and healthy children. Dicriscio and Troiani (2017) implemented a paradigm designed to capture patterns of pupil adaptation in children with ASD during sustained periods in dark and light environments.…”

Section: Introductionmentioning

confidence: 99%

Disease Classification Based on Eye Movement Features With Decision Tree and Random Forest

Mao

Liu

et al. 2020

Front. Neurosci.

View full text Add to dashboard Cite

Medical research shows that eye movement disorders are related to many kinds of neurological diseases. Eye movement characteristics can be used as biomarkers of Parkinson's disease, Alzheimer's disease (AD), schizophrenia, and other diseases. However, due to the unknown medical mechanism of some diseases, it is difficult to establish an intuitive correspondence between eye movement characteristics and diseases. In this paper, we propose a disease classification method based on decision tree and random forest (RF). First, a variety of experimental schemes are designed to obtain eye movement images, and information such as pupil position and area is extracted as original features. Second, with the original features as training samples, the long short-term memory (LSTM) network is used to build classifiers, and the classification results of the samples are regarded as the evolutionary features. After that, multiple decision trees are built according to the C4.5 rules based on the evolutionary features. Finally, a RF is constructed with these decision trees, and the results of disease classification are determined by voting. Experiments show that the RF method has good robustness and its classification accuracy is significantly better than the performance of previous classifiers. This study shows that the application of advanced artificial intelligence (AI) technology in the pathological analysis of eye movement has obvious advantages and good prospects.

show abstract

Developing an eye-tracking algorithm as a potential tool for early diagnosis of autism spectrum disorder in children

Cited by 66 publications

References 22 publications

Disease Classification Based on Synthesis of Multiple Long Short-Term Memory Classifiers Corresponding to Eye Movement Features

Disease Classification Based on Synthesis of Multiple Long Short-Term Memory Classifiers Corresponding to Eye Movement Features

The effects of interior design on wellness – Eye tracking analysis in determining emotional experience of architectural space. A survey on a group of volunteers from the Lublin Region, Eastern Poland

Disease Classification Based on Eye Movement Features With Decision Tree and Random Forest

Contact Info

Product

Resources

About