簡易檢索 / 詳目顯示

回結果列表
研究生： 盧欣誼
Lu, Hsin-Yi
論文名稱： 根據自閉症兒童之眼動資料建構機器學習模型輔助診斷
The identification of children with autism spectrum disorder by machine learning approach on eye-tracking data
指導教授： 林彥呈
Lin, Yang-Cheng
學位類別： 碩士
Master
系所名稱： 規劃與設計學院 - 工業設計學系碩士在職專班
Department of Industrial Design (on-the-job training program)
論文出版年： 2023
畢業學年度： 111
語文別： 中文
論文頁數： 95
中文關鍵詞： 自閉症類群障礙症 、眼動追蹤 、機器學習 、人工智慧 、診斷
外文關鍵詞： Autism, Eye-tracking, Machine learning, Artificial intelligence, Diagnosis
相關次數： 點閱：102  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 自閉症類群障礙症(Autism Spectrum Disorder, ASD)患者在兒童早期即出現症狀，分別是：有社交溝通與社交互動的障礙，以及有重複而侷限的行為、興趣或活動，症狀嚴重程度廣泛，臨床專業人員會透過提供有關 ASD患者目前功能的資訊，協助照顧者理解預後狀況，或是在需要時轉介到其他服務人員或專業提供者。然而，目前篩檢方法通常不夠靈敏，多以問卷方式進行，較難仔細評估嬰幼兒的發展面貌。ASD 亦可由各種專業人士診斷，在多學科的參與下進行，基於臨床觀察和其他專業人員的評估報告，比僅基於觀察或報告的診斷更可靠且準確。但這種多專業評估以及整合訪談資料的過程往往曠日費時，造成延遲獲得醫療服務。
    研究證明可利用眼動追蹤作為ASD兒童早期生物標誌的有力證據。患有ASD的兒童表現出較少注視人臉、眼部而優先注視嘴部的表現，及對於幾何場景的偏好勝過社交場景的注視次數及時間有差異。眼動追蹤的優點是器材是非侵入性的，且可以在不同功能水平和年齡的個體中進行。但使用傳統的統計方法分析如此複雜且大量的訊息是困難且低效率的，若能透過人工智慧的輔助，將眼動資料快速分析，或許更能客觀地評量使用者的心理認知狀態，並能協助自閉症患者的診斷，以利早期介入提升功能，增進ASD兒童及其家庭之生活品質。
    本研究共58名4至6 歲的兒童(22位ASD,36位TD兒童)參與本研究。 讓兒童觀看靜態圖像，圖像分為五種類別，以人數及互動有無來做圖片分類，並標示出感興趣區 (area of interest, AOI) ，在觀看過程以眼動儀紀錄凝視次數(Fixation Count)、凝視時間(Fixation Duration)、凝視時間比例(Fixation Duration Percentage)、第一次凝視目標物時間(Time to First Fixation AOI)。接著將PCA方法做特徵選取，並以SMOTE方法人工合成一些少數樣本，最後再以決策樹(Decision Tree，DT)、隨機森林(Random Forest，RF)、邏輯斯迴歸(Logistic Regression，LR)、 極限梯度提升(Extreme Gradient Boosting， XGBoost)、支持向量機(Support Vector Machine， SVM)共五種演算法，對自閉症兒童和正常發育兒童進行分類。
    研究結果顯示，兩組兒童皆能在引導後完成校正及施測，眼動測試過程無不良反應發生。結果顯示當選擇十二個特徵時，分類模型分別可達SVM準確率83.3%，AUC為0.94, LR準確率83.3%，AUC為0.95, DT準確率77.8%，AUC為0.81, RF準確率88.9%，AUC為0.96, XGB準確率94.4%，AUC為0.99。藉由特徵重要性分析後，可將二十五張圖像減少至八張，並可發現主要影響圖像鑑別力的因素為：互動、食物、明顯情緒、單人圖像、日常興趣或活動。結果表明，以機器學習方法分析眼動追踪數據，是識別自閉症兒童的有用工具，未來預期能夠大幅縮短診斷的時間並提升準確度，以利ASD兒童及早獲得醫療資源之協助。

    Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects social communication, interaction, and behavior. Early detection and intervention can significantly improve the lives of children with ASD and their families. This study aimed to investigate the use of eye tracking and machine learning to diagnose ASD in children aged 4 to 6 years old. The study included 22 children with ASD and 36 typically developing children. Eye-tracking data were collected while the children viewed static images divided into five categories.
    Principal component analysis and the SMOTE method were used for feature selection and data balancing, respectively. Five classification algorithms were used to classify ASD and TD children. The results showed that the machine learning models achieved high accuracy rates, with the XGBoost algorithm achieving the highest accuracy rate of 94.4% and an AUC of 0.99. Feature importance analysis identified interaction, food, obvious emotion, single-person images, and daily interests or activities as the main factors influencing image discriminability.
    The study demonstrates the potential of eye tracking and machine learning to diagnose ASD in young children quickly and accurately. This approach can significantly shorten diagnosis time, improve accuracy, and facilitate early access to medical resources for ASD children, leading to improved outcomes for affected children and their families. However, further studies are necessary to validate the results on a larger sample size and to optimize the algorithms for clinical use.

    摘要 i Thesis Title: The identification of children with autism spectrum disorder by machine learning approach on eye-tracking data iii 誌謝 vii 目錄 viii 表目錄 xi 圖目錄 xii 符號與縮寫列表 xiv 第1章 緒論 1 1.1 研究背景 1 1.1.1 自閉症類群障礙症的症狀 2 1.1.2 自閉症類群障礙症之診斷方式 3 1.1.3 人工智慧應用於自閉症類群障礙症診斷 5 1.2 研究動機 7 1.3 研究目的 9 1.4 研究範圍 9 1.5 研究架構 10 第2章 文獻探討 12 2.1 評估自閉症類群障礙症之工具 12 2.1.1 兒童自閉症評估量表 (Childhood Autism Rating Scale ，CARS) 13 2.1.2 自閉症診斷會談問卷修訂版(Autism Diagnostic Interview-Revised，ADI-R) 14 2.1.3 自閉症診斷觀察量表(Autism Diagnostic Observation Scales，ADOS) 14 2.2 凝視模式異常之理論基礎 16 2.3 自閉症類群障礙之眼睛凝視模式異常 17 2.3.1 自閉症凝視臉部之眼動資料 18 2.3.2 自閉症共享式注意力之眼動資料 22 2.3.3 自閉症瞳孔大小變化之眼動資料 24 2.4 人工智慧分析眼動資料協助自閉症診斷 27 2.4.1 動態影像 27 2.4.2 靜態影像 27 2.4.3 情緒辨識任務 28 2.4.4 其他(動作模仿、網頁搜尋、情緒模仿) 29 第3章 研究方法 31 3.1 研究流程 32 3.2 研究對象 33 3.3 數據紀錄 33 3.3.1 儀器設備 33 3.3.2 感興趣區域定義 35 3.3.3 眼球動作資料 36 3.3.4 量測程序 36 3.4 篩選具鑑別力之圖像 38 3.4.1 計算係數(coefficients)獲得特徵重要性 38 3.4.2 從樹狀結構模型(Tree-based model)獲得特徵重要性 38 3.5 機器學習模型分類ASD嚴重程度 38 3.5.1 決策樹(Decision Tree，DT) 39 3.5.2 隨機森林(Random Forest，RF) 40 3.5.3 極限梯度提升(Extreme Gradient Boosting，XGBoost) 41 3.5.4 邏輯斯迴歸(Logistic Regression，LR) 42 3.5.5 支持向量機(Support Vector Machine，SVM) 43 3.5.6 評估模型表現 44 3.6 預期成果與研究價值 46 第4章 結果與討論 47 4.1 數據介紹 47 4.2 資料預處理 48 4.3 探索性資料分析 49 4.3.1 原始資料變數索引圖 49 4.3.2 復健時間與眼睛凝視時間 50 4.3.3 TD與ASD兒童不同部位凝視時間之差異 50 4.3.4 眼動數據連續變數之間的相關係數 51 4.3.5 觀看不同部位之間眼動數據連續變數的相關係數 51 4.4 建立初始模型 52 4.5 特徵縮放(Feature scaling) 54 4.6 特徵選擇(Feature extraction) 56 4.7 SMOTE(Synthetic Minority Over-sampling Technique)演算法 61 4.8 模型超參數調整 63 4.9 具鑑別力之分類圖像 66 4.10 影響圖像鑑別力之因素初探 68 4.11 基於篩選圖像數據之模型建置 69 4.12 綜合討論 71 第5章 結論 74 5.1 結論與建議 74 5.2 研究限制與建議 76 參考文獻 77 Appendix A 高雄醫學大學附設中和紀念醫院人體試驗倫理委員會 通過證明 91 Appendix B 受試者健康狀況調查表 92 Appendix C 兒童觀看圖像 93 Appendix D 各因素所對應之圖像 94

    1. 陳世鐘. (2017). 自閉症兒童的盛行率，發生率及共病現象相關因素的探討。. 國立臺中教育大學幼兒教育學系早期療育研究所，碩士論文。.
    2. Aghdam, M. A., Sharifi, A., & Pedram, M. M. (2018). Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network. Journal of digital imaging, 31(6), 895-903.
    3. Aguillon‐Hernandez, N., Mofid, Y., Latinus, M., Roché, L., Bufo, M. R., Lemaire, M., . . . Bonnet‐Brilhault, F. (2020). The pupil: a window on social automatic processing in autism spectrum disorder children. Journal of Child Psychology and Psychiatry, 61(7), 768-778.
    4. Almourad, M. B., Bataineh, E., Stocker, J., & Marir, F. (2018). Analyzing the behavior of autistic and normal developing children using eye tracking data. Paper presented at the International conference on kansei engineering & emotion research.
    5. Anderson, C. J., Colombo, J., & Jill Shaddy, D. (2006). Visual scanning and pupillary responses in young children with autism spectrum disorder. Journal of Clinical and Experimental Neuropsychology, 28(7), 1238-1256.
    6. Anzulewicz, A., Sobota, K., & Delafield-Butt, J. T. (2016). Toward the Autism Motor Signature: Gesture patterns during smart tablet gameplay identify children with autism. Scientific reports, 6(1), 1-13.
    7. Auyeung, B., Lombardo, M. V., Heinrichs, M., Chakrabarti, B., Sule, A., Deakin, J. B., . . . Sipple, J. A. (2015). Oxytocin increases eye contact during a real-time, naturalistic social interaction in males with and without autism. Translational psychiatry, 5(2), e507-e507.
    8. Baron-Cohen, S. (2000). Theory of mind and autism: A review. International review of research in mental retardation, 23, 169-184.
    9. Bauman, M. L., & Kemper, T. L. (2005). Neuroanatomic observations of the brain in autism: a review and future directions. International journal of developmental neuroscience, 23(2-3), 183-187.
    10. Billeci, L., Narzisi, A., Campatelli, G., Crifaci, G., Calderoni, S., Gagliano, A., . . . Muratori, F. (2016). Disentangling the initiation from the response in joint attention: an eye-tracking study in toddlers with autism spectrum disorders. Translational psychiatry, 6(5), e808-e808.
    11. Blaser, E., Eglington, L., Carter, A. S., & Kaldy, Z. (2014). Pupillometry reveals a mechanism for the Autism Spectrum Disorder (ASD) advantage in visual tasks. Scientific reports, 4(1), 1-5.
    12. Bosl, W. J., Tager-Flusberg, H., & Nelson, C. A. (2018). EEG analytics for early detection of autism spectrum disorder: a data-driven approach. Scientific reports, 8(1), 1-20.
    13. Brondino, N., Bertoglio, F., Forneris, F., Faravelli, S., Borghesi, A., Damiani, S., . . . Caviglia, M. (2021). A Pilot Study on Covid and Autism: Prevalence, Clinical Presentation and Vaccine Side Effects. Brain Sciences, 11(7), 860.
    14. Buettner, R. (2013). Cognitive workload of humans using artificial intelligence systems: towards objective measurement applying eye-tracking technology. Paper presented at the Annual conference on artificial intelligence.
    15. Bussu, G., Jones, E. J., Charman, T., Johnson, M. H., & Buitelaar, J. (2018). Prediction of autism at 3 years from behavioural and developmental measures in high-risk infants: a longitudinal cross-domain classifier analysis. Journal of Autism and Developmental Disorders, 48(7), 2418-2433.
    16. Caligor, E., Kernberg, O., Clarkin, J., & Yeomans, F. (2018). American Psychiatric Association Publishing. Psychodynamic Therapy for Personality Pathology: Treating Self and Interpersonal Functioning. Washington, DC: American Psychiatric Association Publishing.
    17. Camaioni, L., Perucchini, P., Bellagamba, F., & Colonnesi, C. (2004). The role of declarative pointing in developing a theory of mind. Infancy, 5(3), 291-308.
    18. Capriola-Hall, N. N., Wieckowski, A. T., Swain, D., Tech, V., Aly, S., Youssef, A., . . . White, S. W. (2019). Group differences in facial emotion expression in autism: evidence for the utility of machine classification. Behavior therapy, 50(4), 828-838.

    19. Carette, R., Elbattah, M., Cilia, F., Dequen, G., Guérin, J.-L., & Bosche, J. (2019). Learning to Predict Autism Spectrum Disorder based on the Visual Patterns of Eye-tracking Scanpaths. Paper presented at the HEALTHINF.
    20. Chaidez, V., Hansen, R. L., & Hertz-Picciotto, I. (2014). Gastrointestinal problems in children with autism, developmental delays or typical development. Journal of autism and developmental disorders, 44(5), 1117-1127.
    21. Chang, Z., Di Martino, J. M., Aiello, R., Baker, J., Carpenter, K., Compton, S., . . . Flowers, J. (2021). Computational Methods to Measure Patterns of Gaze in Toddlers With Autism Spectrum Disorder. JAMA pediatrics.
    22. Charman, T., Pickles, A., Simonoff, E., Chandler, S., Loucas, T., & Baird, G. (2011). IQ in children with autism spectrum disorders: data from the Special Needs and Autism Project (SNAP). Psychological medicine, 41(3), 619-627.
    23. Charman, T., & Shmueli-Goetz, Y. (1998). The relationship between theory of mind, language and narrative discourse: an experimental study. Cahiers de Psychologie Cognitive/Current Psychology of Cognition.
    24. Chen, T., Chen, Y., Yuan, M., Gerstein, M., Li, T., Liang, H., . . . Lu, L. (2020). The development of a practical artificial intelligence tool for diagnosing and evaluating autism spectrum disorder: multicenter study. JMIR medical informatics, 8(5), e15767.
    25. Chen, Y.-W., & Lin, C.-J. (2006). Combining SVMs with various feature selection strategies. In Feature extraction (pp. 315-324): Springer.
    26. Chevallier, C., Kohls, G., Troiani, V., Brodkin, E. S., & Schultz, R. T. (2012). The social motivation theory of autism. Trends in cognitive sciences, 16(4), 231-239.
    27. Chlebowski, C., Green, J. A., Barton, M. L., & Fein, D. (2010). Using the childhood autism rating scale to diagnose autism spectrum disorders. Journal of autism and developmental disorders, 40(7), 787-799.
    28. Conley, M. I., Dellarco, D. V., Rubien-Thomas, E., Cohen, A. O., Cervera, A., Tottenham, N., & Casey, B. (2018). The racially diverse affective expression (RADIATE) face stimulus set. Psychiatry research, 270, 1059-1067.
    29. Corona, L. L., Weitlauf, A. S., Hine, J., Berman, A., Miceli, A., Nicholson, A., . . . Juárez, A. P. (2020). Parent perceptions of caregiver-mediated telemedicine tools for assessing autism risk in toddlers. Journal of Autism and Developmental Disorders, 1-11.
    30. Crosser, A., Snideman, M., & Vazquez Klisans, D. (2019). Determining Static Mount Accuracy with a Mid-Range Eye Tracker. Paper presented at the Proceedings of the Human Factors and Ergonomics Society Annual Meeting.
    31. Curtin, C., Hubbard, K., Anderson, S. E., Mick, E., Must, A., & Bandini, L. G. (2015). Food selectivity, mealtime behavior problems, spousal stress, and family food choices in children with and without autism spectrum disorder. Journal of autism and developmental disorders, 45(10), 3308-3315.
    32. Dalton, K. M., Nacewicz, B. M., Johnstone, T., Schaefer, H. S., Gernsbacher, M. A., Goldsmith, H. H., . . . Davidson, R. J. (2005). Gaze fixation and the neural circuitry of face processing in autism. Nature neuroscience, 8(4), 519-526.
    33. Dawson, G., Webb, S. J., & McPartland, J. (2005). Understanding the nature of face processing impairment in autism: insights from behavioral and electrophysiological studies. Developmental neuropsychology, 27(3), 403-424.
    34. Duda, M., Ma, R., Haber, N., & Wall, D. (2016). Use of machine learning for behavioral distinction of autism and ADHD. Translational psychiatry, 6(2), e732-e732.
    35. Falck-Ytter, T., Thorup, E., & Bölte, S. (2015). Brief report: lack of processing bias for the objects other people attend to in 3-year-olds with autism. Journal of autism and developmental disorders, 45(6), 1897-1904.
    36. Falck‐Ytter, T. (2008). Face inversion effects in autism: a combined looking time and pupillometric study. Autism Research, 1(5), 297-306.
    37. Falkmer, T., Anderson, K., Falkmer, M., & Horlin, C. (2013). Diagnostic procedures in autism spectrum disorders: a systematic literature review. European child & adolescent psychiatry, 22(6), 329-340.
    38. Franchini, M., Armstrong, V. L., Schaer, M., & Smith, I. M. (2019). Initiation of joint attention and related visual attention processes in infants with autism spectrum disorder: Literature review. Child Neuropsychology, 25(3), 287-317.
    39. Frazier, T. W., Strauss, M., Klingemier, E. W., Zetzer, E. E., Hardan, A. Y., Eng, C., & Youngstrom, E. A. (2017). A meta-analysis of gaze differences to social and nonsocial information between individuals with and without autism. Journal of the American Academy of Child & Adolescent Psychiatry, 56(7), 546-555.
    40. Freeth, M., Chapman, P., Ropar, D., & Mitchell, P. (2010). Do gaze cues in complex scenes capture and direct the attention of high functioning adolescents with ASD? Evidence from eye-tracking. Journal of autism and developmental disorders, 40(5), 534-547.
    41. Galazka, M. A., Åsberg Johnels, J., Zürcher, N. R., Hippolyte, L., Lemonnier, E., Billstedt, E., . . . Hadjikhani, N. (2019). Pupillary contagion in autism. Psychological science, 30(2), 309-315.
    42. Hadjikhani, N., Johnels, J. Å., Zürcher, N. R., Lassalle, A., Guillon, Q., Hippolyte, L., . . . Gillberg, C. (2017). Look me in the eyes: constraining gaze in the eye-region provokes abnormally high subcortical activation in autism. Scientific Reports, 7(1), 1-7.
    43. Hanley, M., McPhillips, M., Mulhern, G., & Riby, D. M. (2013). Spontaneous attention to faces in Asperger syndrome using ecologically valid static stimuli. Autism, 17(6), 754-761.
    44. Hepach, R., Hedley, D., & Nuske, H. J. (2020). Prosocial attention in children with and without autism spectrum disorder: Dissociation between anticipatory gaze and internal arousal. Journal of abnormal child psychology, 48(4), 589-605.
    45. Hyman, S. L., & Johnson, J. K. (2012). Autism and pediatric practice: Toward a medical home. Journal of Autism and Developmental Disorders, 42(6), 1156-1164.
    46. Jiang, M., Francis, S. M., Tseng, A., Srishyla, D., DuBois, M., Beard, K., . . . Jacob, S. (2020). Predicting core characteristics of ASD through facial emotion recognition and eye tracking in youth. Paper presented at the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
    47. Jiao, Y., Chen, R., Ke, X., Cheng, L., Chu, K., Lu, Z., & Herskovits, E. H. (2012). Single nucleotide polymorphisms predict symptom severity of autism spectrum disorder. Journal of autism and developmental disorders, 42(6), 971-983.
    48. Jones, E. A., & Carr, E. G. (2004). Joint attention in children with autism: Theory and intervention. Focus on autism and other developmental disabilities, 19(1), 13-26.
    49. Kang, J., Han, X., Hu, J.-F., Feng, H., & Li, X. (2020). 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. Journal of Clinical Neuroscience, 81, 54-60.
    50. Kang, J., Han, X., Song, J., Niu, Z., & Li, X. (2020). The identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data. Computers in biology and medicine, 120, 103722.
    51. Khan, N. Z., Gallo, L. A., Arghir, A., Budisteanu, B., Budisteanu, M., Dobrescu, I., . . . Kalambayi, F. (2012). Autism and the grand challenges in global mental health.
    52. Kim, S. H., Bal, V. H., & Lord, C. (2018). Longitudinal follow‐up of academic achievement in children with autism from age 2 to 18. Journal of Child Psychology and Psychiatry, 59(3), 258-267.
    53. Kliemann, D., Dziobek, I., Hatri, A., Steimke, R., & Heekeren, H. R. (2010). Atypical reflexive gaze patterns on emotional faces in autism spectrum disorders. Journal of Neuroscience, 30(37), 12281-12287.
    54. Kou, Y., Betancur, C., Xu, H., Buxbaum, J. D., & Ma'Ayan, A. (2012). Network‐and attribute‐based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability. Paper presented at the American Journal of Medical Genetics Part C: Seminars in Medical Genetics.
    55. Kret, M. E. (2015). Emotional expressions beyond facial muscle actions. A call for studying autonomic signals and their impact on social perception. Frontiers in psychology, 6, 711.
    56. Król, M. E., & Król, M. (2019). A novel machine learning analysis of eye-tracking data reveals suboptimal visual information extraction from facial stimuli in individuals with autism. Neuropsychologia, 129, 397-406.
    57. Lai, D.-C., Tseng, Y.-C., Hou, Y.-M., & Guo, H.-R. (2012). Gender and geographic differences in the prevalence of autism spectrum disorders in children: Analysis of data from the national disability registry of Taiwan. Research in developmental disabilities, 33(3), 909-915.
    58. Lavelle, T. A., Weinstein, M. C., Newhouse, J. P., Munir, K., Kuhlthau, K. A., & Prosser, L. A. (2014). Economic burden of childhood autism spectrum disorders. Pediatrics, 133(3), e520-e529.
    59. Li, J., Zhong, Y., Han, J., Ouyang, G., Li, X., & Liu, H. (2020). Classifying ASD children with LSTM based on raw videos. Neurocomputing, 390, 226-238.
    60. Liszkowski, U., Carpenter, M., & Tomasello, M. (2008). Twelve-month-olds communicate helpfully and appropriately for knowledgeable and ignorant partners. Cognition, 108(3), 732-739.
    61. Liu, W., Li, M., & Yi, L. (2016). Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework. Autism Research, 9(8), 888-898.
    62. Lord, C. (1995). Follow‐up of two‐year‐olds referred for possible autism. Journal of child psychology and psychiatry, 36(8), 1365-1382.
    63. Lord, C., Elsabbagh, M., Baird, G., & Veenstra-Vanderweele, J. (2018). Autism spectrum disorder. The Lancet, 392(10146), 508-520.
    64. Lord, C., Petkova, E., Hus, V., Gan, W., Lu, F., Martin, D. M., . . . Gerdts, J. (2012). A multisite study of the clinical diagnosis of different autism spectrum disorders. Archives of general psychiatry, 69(3), 306-313.
    65. Lord, C., Pickles, A., McLennan, J., Rutter, M., Bregman, J., Folstein, S., . . . Minshew, N. (1997). Diagnosing autism: analyses of data from the Autism Diagnostic Interview. Journal of autism and developmental disorders, 27(5), 501-517.
    66. Lord, C., Risi, S., Lambrecht, L., Cook, E. H., Leventhal, B. L., DiLavore, P. C., . . . Rutter, M. (2000). The Autism Diagnostic Observation Schedule—Generic: A standard measure of social and communication deficits associated with the spectrum of autism. Journal of autism and developmental disorders, 30(3), 205-223.
    67. Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of autism and developmental disorders, 24(5), 659-685.
    68. Lord, C., Storoschuk, S., Rutter, M., & Pickles, A. (1993). Using the ADI‐R to diagnose autism in preschool children. Infant Mental Health Journal, 14(3), 234-252.
    69. Lotter, V. (1966). Epidemiology of autistic conditions in young children. Social psychiatry, 1(3), 124-135.
    70. Maenner, M. J., Shaw, K. A., & Baio, J. (2020). Prevalence of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2016. MMWR Surveillance Summaries, 69(4), 1.
    71. Mandell, D., & Mandy, W. (2015). Should all young children be screened for autism spectrum disorder? In: SAGE Publications Sage UK: London, England.
    72. Martineau, J., Hernandez, N., Hiebel, L., Roché, L., Metzger, A., & Bonnet-Brilhault, F. (2011). Can pupil size and pupil responses during visual scanning contribute to the diagnosis of autism spectrum disorder in children? Journal of psychiatric research, 45(8), 1077-1082.
    73. Mastergeorge, A. M., Kahathuduwa, C., & Blume, J. (2021). Eye-tracking in infants and young children at risk for autism spectrum disorder: A systematic review of visual stimuli in experimental paradigms. Journal of Autism and Developmental Disorders, 51(8), 2578-2599.
    74. Mazefsky, C. A., & Oswald, D. P. (2006). The discriminative ability and diagnostic utility of the ADOS-G, ADI-R, and GARS for children in a clinical setting. Autism, 10(6), 533-549.
    75. Mazurek, M. O., Handen, B. L., Wodka, E. L., Nowinski, L., Butter, E., & Engelhardt, C. R. (2014). Age at first autism spectrum disorder diagnosis: the role of birth cohort, demographic factors, and clinical features. Journal of Developmental & Behavioral Pediatrics, 35(9), 561-569.
    76. McCarty, P., & Frye, R. E. (2020). Early Detection and Diagnosis of Autism Spectrum Disorder: Why Is It So Difficult? Paper presented at the Seminars in Pediatric Neurology.
    77. McPartland, J. C., Webb, S. J., Keehn, B., & Dawson, G. (2011). Patterns of visual attention to faces and objects in autism spectrum disorder. Journal of autism and developmental disorders, 41(2), 148-157.
    78. Mellema, C., Treacher, A., Nguyen, K., & Montillo, A. (2019). Multiple deep learning architectures achieve superior performance diagnosing autism spectrum disorder using features previously extracted from structural and functional mri. Paper presented at the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).
    79. Müller, N., Baumeister, S., Dziobek, I., Banaschewski, T., & Poustka, L. (2016). Validation of the movie for the assessment of social cognition in adolescents with ASD: Fixation duration and pupil dilation as predictors of performance. Journal of autism and developmental disorders, 46(9), 2831-2844.
    80. Murias, M., Major, S., Davlantis, K., Franz, L., Harris, A., Rardin, B., . . . Dawson, G. (2018). Validation of eye‐tracking measures of social attention as a potential biomarker for autism clinical trials. Autism Research, 11(1), 166-174.
    81. Nacewicz, B. M., Dalton, K. M., Johnstone, T., Long, M. T., McAuliff, E. M., Oakes, T. R., . . . Davidson, R. J. (2006). Amygdala volume and nonverbal social impairment in adolescent and adult males with autism. Archives of general psychiatry, 63(12), 1417-1428.
    82. Nag, A., Haber, N., Voss, C., Tamura, S., Daniels, J., Ma, J., . . . Winograd, T. (2020). Toward continuous social phenotyping: analyzing gaze patterns in an emotion recognition task for children with autism through wearable smart glasses. Journal of medical Internet research, 22(4), e13810.
    83. Nakano, T., Tanaka, K., Endo, Y., Yamane, Y., Yamamoto, T., Nakano, Y., . . . Kitazawa, S. (2010). Atypical gaze patterns in children and adults with autism spectrum disorders dissociated from developmental changes in gaze behaviour. Proceedings of the Royal Society B: Biological Sciences, 277(1696), 2935-2943.
    84. Navab, A., Gillespie‐Lynch, K., Johnson, S. P., Sigman, M., & Hutman, T. (2012). Eye‐tracking as a measure of responsiveness to joint attention in infants at risk for autism. Infancy, 17(4), 416-431.
    85. Nuske, H. J., Vivanti, G., & Dissanayake, C. (2014). Reactivity to fearful expressions of familiar and unfamiliar people in children with autism: an eye-tracking pupillometry study. Journal of Neurodevelopmental Disorders, 6(1), 1-16.
    86. Nuske, H. J., Vivanti, G., & Dissanayake, C. (2015). No evidence of emotional dysregulation or aversion to mutual gaze in preschoolers with autism spectrum disorder: an eye-tracking pupillometry study. Journal of autism and developmental disorders, 45(11), 3433-3445.
    87. Nuske, H. J., Vivanti, G., Hudry, K., & Dissanayake, C. (2014). Pupillometry reveals reduced unconscious emotional reactivity in autism. Biological psychology, 101, 24-35.
    88. O’Reilly, C., Lewis, J. D., & Elsabbagh, M. (2017). Is functional brain connectivity atypical in autism? A systematic review of EEG and MEG studies. PloS one, 12(5), e0175870.
    89. Oliveira, J. S., Franco, F. O., Revers, M. C., Silva, A. F., Portolese, J., Brentani, H., . . . Nunes, F. L. (2021). Computer-aided autism diagnosis based on visual attention models using eye tracking. Scientific reports, 11(1), 1-11.
    90. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., . . . Dubourg, V. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.
    91. Perry, A., Condillac, R. A., Freeman, N. L., Dunn-Geier, J., & Belair, J. (2005). Multi-site study of the Childhood Autism Rating Scale (CARS) in five clinical groups of young children. Journal of autism and developmental disorders, 35(5), 625-634.
    92. PhillipsSarah, M., KralTanja, V., SherwoodNancy, E., StanishHeidi, I., & BandiniLinda, G. (2017). The effect of age on the prevalence of obesity among US youth with autism spectrum disorder. Childhood Obesity.
    93. Pop-Jordanova, N., Zorcec, T., Demerdzieva, A., & Gucev, Z. (2010). QEEG characteristics and spectrum weighted frequency for children diagnosed as autistic spectrum disorder. Nonlinear Biomedical Physics, 4(1), 1-7.
    94. Premack, D., & Woodruff, G. (1978). Does the chimpanzee have a theory of mind? Behavioral and brain sciences, 1(4), 515-526.
    95. Randall, M., Egberts, K. J., Samtani, A., Scholten, R. J., Hooft, L., Livingstone, N., . . . Williams, K. (2018). Diagnostic tests for autism spectrum disorder (ASD) in preschool children. Cochrane Database of Systematic Reviews(7).
    96. Reisinger, D. L., Shaffer, R. C., Horn, P. S., Hong, M. P., Pedapati, E. V., Dominick, K. C., & Erickson, C. A. (2020). Atypical social attention and emotional face processing in autism spectrum disorder: Insights from face scanning and pupillometry. Frontiers in integrative neuroscience, 13, 76.
    97. Rellini, E., Tortolani, D., Trillo, S., Carbone, S., & Montecchi, F. (2004). Childhood Autism Rating Scale (CARS) and Autism Behavior Checklist (ABC) correspondence and conflicts with DSM-IV criteria in diagnosis of autism. Journal of autism and developmental disorders, 34(6), 703-708.
    98. Riby, D. M., Hancock, P. J., Jones, N., & Hanley, M. (2013). Spontaneous and cued gaze-following in autism and Williams syndrome. Journal of neurodevelopmental disorders, 5(1), 1-12.
    99. Rigby, S. N., Stoesz, B. M., & Jakobson, L. S. (2016). Gaze patterns during scene processing in typical adults and adults with autism spectrum disorders. Research in Autism Spectrum Disorders, 25, 24-36.
    100. Rutter, M., Le Couteur, A., & Lord, C. (2003). ADI-R. Autism diagnostic interview revised. Manual. Los Angeles: Western Psychological Services.
    101. Sappok, T., Diefenbacher, A., Budczies, J., Schade, C., Grubich, C., Bergmann, T., . . . Dziobek, I. (2013). Diagnosing autism in a clinical sample of adults with intellectual disabilities: How useful are the ADOS and the ADI-R? Research in developmental disabilities, 34(5), 1642-1655.
    102. Schopler, E., Reichler, R., & Rochen Renner, B. (1988). The Childhood Autism Rating Scale: Western Psychological Services. In.
    103. Schopler, E., Reichler, R. J., DeVellis, R. F., & Daly, K. (1980). Toward objective classification of childhood autism: Childhood Autism Rating Scale (CARS). Journal of autism and developmental disorders, 10(1), 91-103.
    104. Sepeta, L., Tsuchiya, N., Davies, M. S., Sigman, M., Bookheimer, S. Y., & Dapretto, M. (2012). Abnormal social reward processing in autism as indexed by pupillary responses to happy faces. Journal of neurodevelopmental disorders, 4(1), 1-9.
    105. Simonoff, E., Pickles, A., Charman, T., Chandler, S., Loucas, T., & Baird, G. (2008). Psychiatric disorders in children with autism spectrum disorders: prevalence, comorbidity, and associated factors in a population-derived sample. Journal of the American Academy of Child & Adolescent Psychiatry, 47(8), 921-929.
    106. Sivertsen, B., Posserud, M.-B., Gillberg, C., Lundervold, A. J., & Hysing, M. (2012). Sleep problems in children with autism spectrum problems: a longitudinal population-based study. Autism, 16(2), 139-150.
    107. Swanson, M. R., & Siller, M. (2013). Patterns of gaze behavior during an eye-tracking measure of joint attention in typically developing children and children with autism spectrum disorder. Research in Autism Spectrum Disorders, 7(9), 1087-1096.
    108. Terry, M. (2009). Telemedicine and autism: Researchers and clinicians are just starting to consider telemedicine applications for the diagnosis and treatment of autism. Telemedicine and e-Health, 15(5), 416-419.
    109. Thomas, S., Hovinga, M. E., Rai, D., & Lee, B. K. (2017). Brief report: prevalence of co-occurring epilepsy and autism spectrum disorder: the US National Survey of Children’s Health 2011–2012. Journal of autism and developmental disorders, 47(1), 224-229.
    110. Thorup, E., Nyström, P., Gredebäck, G., Bölte, S., & Falck-Ytter, T. (2018). Reduced alternating gaze during social interaction in infancy is associated with elevated symptoms of autism in toddlerhood. Journal of Abnormal Child Psychology, 46(7), 1547-1561.
    111. Vabalas, A., Gowen, E., Poliakoff, E., & Casson, A. J. (2020). Applying machine learning to kinematic and eye movement features of a movement imitation task to predict autism diagnosis. Scientific reports, 10(1), 1-13.
    112. Vargas-Cuentas, N. I., Roman-Gonzalez, A., Gilman, R. H., Barrientos, F., Ting, J., Hidalgo, D., . . . Zimic, M. (2017). Developing an eye-tracking algorithm as a potential tool for early diagnosis of autism spectrum disorder in children. PloS one, 12(11), e0188826.
    113. Vaughan, A., Mundy, P., Block, J., Burnette, C., Delgado, C., Gomez, Y., . . . Pomares, Y. (2003). Child, caregiver, and temperament contributions to infant joint attention. Infancy, 4(4), 603-616.
    114. Ventola, P. E., Kleinman, J., Pandey, J., Barton, M., Allen, S., Green, J., . . . Fein, D. (2006). Agreement among four diagnostic instruments for autism spectrum disorders in toddlers. Journal of autism and developmental disorders, 36(7), 839-847.
    115. Vivanti, G., Fanning, P. A., Hocking, D. R., Sievers, S., & Dissanayake, C. (2017). Social attention, joint attention and sustained attention in autism Spectrum disorder and Williams syndrome: convergences and divergences. Journal of autism and developmental disorders, 47(6), 1866-1877.
    116. Volkmar, F., Siegel, M., Woodbury-Smith, M., King, B., McCracken, J., & State, M. (2014). Practice parameter for the assessment and treatment of children and adolescents with autism spectrum disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 53(2), 237-257.
    117. Wagner, J. B., Hirsch, S. B., Vogel-Farley, V. K., Redcay, E., & Nelson, C. A. (2013). Eye-tracking, autonomic, and electrophysiological correlates of emotional face processing in adolescents with autism spectrum disorder. Journal of autism and developmental disorders, 43(1), 188-199.
    118. Wagner, J. B., Luyster, R. J., Tager‐Flusberg, H., & Nelson, C. A. (2016). Greater pupil size in response to emotional faces as an early marker of social‐communicative difficulties in infants at high risk for autism. Infancy, 21(5), 560-581.
    119. Wall, D. P., Dally, R., Luyster, R., Jung, J.-Y., & DeLuca, T. F. (2012). Use of artificial intelligence to shorten the behavioral diagnosis of autism.
    120. Wan, G., Kong, X., Sun, B., Yu, S., Tu, Y., Park, J., . . . Feng, Z. (2019). Applying eye tracking to identify autism spectrum disorder in children. Journal of autism and developmental disorders, 49(1), 209-215.
    121. Wilson, C. E., Brock, J., & Palermo, R. (2010). Attention to social stimuli and facial identity recognition skills in autism spectrum disorder. Journal of Intellectual Disability Research, 54(12), 1104-1115.
    122. Wimpory, D. C., Hobson, R. P., Williams, J. M. G., & Nash, S. (2000). Are infants with autism socially engaged? A study of recent retrospective parental reports. Journal of autism and developmental disorders, 30(6), 525-536.
    123. Woynaroski, T., Yoder, P., & Watson, L. R. (2016). Atypical cross‐modal profiles and longitudinal associations between vocabulary scores in initially minimally verbal children with ASD. Autism Research, 9(2), 301-310.
    124. Yaneva, V., Eraslan, S., Yesilada, Y., & Mitkov, R. (2020). Detecting high-functioning autism in adults using eye tracking and machine learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(6), 1254-1261.
    125. Young, G. S., Merin, N., Rogers, S. J., & Ozonoff, S. (2009). Gaze behavior and affect at 6 months: predicting clinical outcomes and language development in typically developing infants and infants at risk for autism. Developmental science, 12(5), 798-814.
    126. Zhang, R., Saran, A., Liu, B., Zhu, Y., Guo, S., Niekum, S., . . . Hayhoe, M. (2020). Human gaze assisted artificial intelligence: a review. Paper presented at the IJCAI: Proceedings of the Conference.
    127. Zwaigenbaum, L., Bauman, M. L., Fein, D., Pierce, K., Buie, T., Davis, P. A., . . . Choueiri, R. (2015). Early screening of autism spectrum disorder: recommendations for practice and research. Pediatrics, 136(Supplement 1), S41-S59.

    無法下載圖示 校內：2028-07-27公開
    校外：2028-07-27公開
    電子論文尚未授權公開，紙本請查館藏目錄
    QR CODE