618 related articles for article (PubMed ID: 34435957)
1. Classification of Children With Autism and Typical Development Using Eye-Tracking Data From Face-to-Face Conversations: Machine Learning Model Development and Performance Evaluation.
Zhao Z; Tang H; Zhang X; Qu X; Hu X; Lu J
J Med Internet Res; 2021 Aug; 23(8):e29328. PubMed ID: 34435957
[TBL] [Abstract][Full Text] [Related]
2. The study of the differences between low-functioning autistic children and typically developing children in the processing of the own-race and other-race faces by the machine learning approach.
Kang J; Han X; Hu JF; Feng H; Li X
J Clin Neurosci; 2020 Nov; 81():54-60. PubMed ID: 33222968
[TBL] [Abstract][Full Text] [Related]
3. The identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data.
Kang J; Han X; Song J; Niu Z; Li X
Comput Biol Med; 2020 May; 120():103722. PubMed ID: 32250854
[TBL] [Abstract][Full Text] [Related]
4. Characteristics of Visual Fixation in Chinese Children with Autism During Face-to-Face Conversations.
Zhao Z; Tang H; Zhang X; Zhu Z; Xing J; Li W; Tao D; Qu X; Lu J
J Autism Dev Disord; 2023 Feb; 53(2):746-758. PubMed ID: 34105046
[TBL] [Abstract][Full Text] [Related]
5. Early identification of autism spectrum disorder based on machine learning with eye-tracking data.
Wei Q; Dong W; Yu D; Wang K; Yang T; Xiao Y; Long D; Xiong H; Chen J; Xu X; Li T
J Affect Disord; 2024 Aug; 358():326-334. PubMed ID: 38615846
[TBL] [Abstract][Full Text] [Related]
6. Machine learning based on eye-tracking data to identify Autism Spectrum Disorder: A systematic review and meta-analysis.
Wei Q; Cao H; Shi Y; Xu X; Li T
J Biomed Inform; 2023 Jan; 137():104254. PubMed ID: 36509416
[TBL] [Abstract][Full Text] [Related]
7. Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach.
Zhao Z; Wei J; Xing J; Zhang X; Qu X; Hu X; Lu J
J Autism Dev Disord; 2023 Mar; 53(3):934-946. PubMed ID: 35913654
[TBL] [Abstract][Full Text] [Related]
8. Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis.
Alcañiz M; Chicchi-Giglioli IA; Carrasco-Ribelles LA; Marín-Morales J; Minissi ME; Teruel-García G; Sirera M; Abad L
Autism Res; 2022 Jan; 15(1):131-145. PubMed ID: 34811930
[TBL] [Abstract][Full Text] [Related]
9. Combined frequency-tagging EEG and eye-tracking measures provide no support for the "excess mouth/diminished eye attention" hypothesis in autism.
Vettori S; Van der Donck S; Nys J; Moors P; Van Wesemael T; Steyaert J; Rossion B; Dzhelyova M; Boets B
Mol Autism; 2020 Nov; 11(1):94. PubMed ID: 33228763
[TBL] [Abstract][Full Text] [Related]
10. A constellation of eye-tracking measures reveals social attention differences in ASD and the broad autism phenotype.
Nayar K; Shic F; Winston M; Losh M
Mol Autism; 2022 May; 13(1):18. PubMed ID: 35509089
[TBL] [Abstract][Full Text] [Related]
11. Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework.
Liu W; Li M; Yi L
Autism Res; 2016 Aug; 9(8):888-98. PubMed ID: 27037971
[TBL] [Abstract][Full Text] [Related]
12. [Slowing down the flow of facial information enhances facial scanning in children with autism spectrum disorders: A pilot eye tracking study].
Charrier A; Tardif C; Gepner B
Encephale; 2017 Feb; 43(1):32-40. PubMed ID: 26995150
[TBL] [Abstract][Full Text] [Related]
13. Active Viewing Facilitates Gaze to the Eye Region in Young Children with Autism Spectrum Disorder.
Wang Y; Peng S; Shao Z; Feng T
J Autism Dev Disord; 2023 Mar; 53(3):1082-1090. PubMed ID: 35129796
[TBL] [Abstract][Full Text] [Related]
14. Applying Eye Tracking to Identify Autism Spectrum Disorder in Children.
Wan G; Kong X; Sun B; Yu S; Tu Y; Park J; Lang C; Koh M; Wei Z; Feng Z; Lin Y; Kong J
J Autism Dev Disord; 2019 Jan; 49(1):209-215. PubMed ID: 30097760
[TBL] [Abstract][Full Text] [Related]
15. Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study.
Tariq Q; Fleming SL; Schwartz JN; Dunlap K; Corbin C; Washington P; Kalantarian H; Khan NZ; Darmstadt GL; Wall DP
J Med Internet Res; 2019 Apr; 21(4):e13822. PubMed ID: 31017583
[TBL] [Abstract][Full Text] [Related]
16. Gazefinder as a clinical supplementary tool for discriminating between autism spectrum disorder and typical development in male adolescents and adults.
Fujioka T; Inohara K; Okamoto Y; Masuya Y; Ishitobi M; Saito DN; Jung M; Arai S; Matsumura Y; Fujisawa TX; Narita K; Suzuki K; Tsuchiya KJ; Mori N; Katayama T; Sato M; Munesue T; Okazawa H; Tomoda A; Wada Y; Kosaka H
Mol Autism; 2016; 7():19. PubMed ID: 27011784
[TBL] [Abstract][Full Text] [Related]
17. Mobile detection of autism through machine learning on home video: A development and prospective validation study.
Tariq Q; Daniels J; Schwartz JN; Washington P; Kalantarian H; Wall DP
PLoS Med; 2018 Nov; 15(11):e1002705. PubMed ID: 30481180
[TBL] [Abstract][Full Text] [Related]
18. Promoting social attention in 3-year-olds with ASD through gaze-contingent eye tracking.
Wang Q; Wall CA; Barney EC; Bradshaw JL; Macari SL; Chawarska K; Shic F
Autism Res; 2020 Jan; 13(1):61-73. PubMed ID: 31468735
[TBL] [Abstract][Full Text] [Related]
19. Eye-tracking correlates of response to joint attention in preschool children with autism spectrum disorder.
de Belen RA; Pincham H; Hodge A; Silove N; Sowmya A; Bednarz T; Eapen V
BMC Psychiatry; 2023 Mar; 23(1):211. PubMed ID: 36991383
[TBL] [Abstract][Full Text] [Related]
20. Machine learning for distinguishing saudi children with and without autism via eye-tracking data.
Alarifi H; Aldhalaan H; Hadjikhani N; Johnels JÅ; Alarifi J; Ascenso G; Alabdulaziz R
Child Adolesc Psychiatry Ment Health; 2023 Sep; 17(1):112. PubMed ID: 37777792
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
