1932

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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2023-08-10
2024-06-13
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Literature Cited

  1. 1.
    Maenner MJ, Shaw KA, Bakian AV, Bilder DA, Durkin MS et al. 2021. Prevalence and characteristics of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018. MMWR Surveill. Summ. 70:111–16
    [Google Scholar]
  2. 2.
    Ning M, Daniels J, Schwartz J, Dunlap K, Washington P et al. 2019. Identification and quantification of gaps in access to autism resources in the United States: an infodemiological study. J. Med. Internet Res. 21:7e13094
    [Google Scholar]
  3. 3.
    Bargiela S, Steward R, Mandy W. 2016. The experiences of late-diagnosed women with autism spectrum conditions: an investigation of the female autism phenotype. J. Autism Dev. Disord. 46:103281–94
    [Google Scholar]
  4. 4.
    Fenske EC, Zalenski S, Krantz PJ, McClannahan LE. 1985. Age at intervention and treatment outcome for autistic children in a comprehensive intervention program. Anal. Interv. Dev. Disabil. 5:1/249–58
    [Google Scholar]
  5. 5.
    Pew Res. Cent 2021. Mobile fact sheet Fact Sheet, Pew Res. Cent Washington, DC: https://www.pewresearch.org/internet/fact-sheet/mobile
    [Google Scholar]
  6. 6.
    Silver L, Smith A, Johnson C, Taylor K, Jiang J et al. 2019. Mobile connectivity in emerging economies Tech. Rep. Pew Res. Cent. Washington, DC:
    [Google Scholar]
  7. 7.
    Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL et al. 2000. The Autism Diagnostic Observation Schedule—Generic: a standard measure of social and communication deficits associated with the spectrum of autism. J. Autism Dev. Disord. 30:3205–23
    [Google Scholar]
  8. 8.
    Rutter M, Le Couteur A, Lord C 2003. Autism Diagnostic Interview—Revised Los Angeles: West. Psychol. Serv.
    [Google Scholar]
  9. 9.
    Van Bourgondien ME, Marcus LM, Schopler E. 1992. Comparison of DSM-III-R and childhood autism rating scale diagnoses of autism. J. Autism Dev. Disord. 22:4493–506
    [Google Scholar]
  10. 10.
    South M, Williams BJ, McMahon WM, Owley T, Filipek PA et al. 2002. Utility of the Gilliam Autism Rating Scale in research and clinical populations. J. Autism Dev. Disord. 32:593–99
    [Google Scholar]
  11. 11.
    Geschwind DH, Sowinski J, Lord C, Iversen P, Shestack J et al. 2001. The Autism Genetic Resource Exchange: a resource for the study of autism and related neuropsychiatric conditions. Am. J. Hum. Genet. 69:2463–66
    [Google Scholar]
  12. 12.
    Hall D, Huerta MF, McAuliffe MJ, Farber GK. 2012. Sharing heterogeneous data: the National Database for Autism Research. Neuroinformatics 10:4331–39
    [Google Scholar]
  13. 13.
    Fischbach GD, Lord C. 2010. The Simons Simplex Collection: a resource for identification of autism genetic risk factors. Neuron 68:2192–95
    [Google Scholar]
  14. 14.
    Simons VIP (Var. Individ.) Consort 2012. Simons Variation in Individuals Project (Simons VIP): a genetics-first approach to studying autism spectrum and related neurodevelopmental disorders. Neuron 73:61063–67
    [Google Scholar]
  15. 15.
    Hu-Lince D, Craig DW, Huentelman MJ, Stephan DA. 2005. The Autism Genome Project. Am. J. Pharmacogenom. 5:4233–46
    [Google Scholar]
  16. 16.
    de Belen RA, Bednarz T, Sowmya A, Del Favero D. 2020. Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019. Transl. Psychiatry 10:333
    [Google Scholar]
  17. 17.
    Abbe A, Grouin C, Zweigenbaum P, Falissard B. 2016. Text mining applications in psychiatry: a systematic literature review. . Int. J. Methods Psychiatr. Res. 25:286–100
    [Google Scholar]
  18. 18.
    Fusaroli R, Lambrechts A, Bang D, Bowler DM, Gaigg SB. 2017. Is voice a marker for autism spectrum disorder? A systematic review and meta-analysis. Autism Res. 10:3384–407
    [Google Scholar]
  19. 19.
    Kalantarian H, Washington P, Schwartz J, Daniels J, Haber N et al. 2019. Guess what?. J. Healthc. Inform. Res. 3:143–66
    [Google Scholar]
  20. 20.
    Kalantarian H, Jedoui K, Washington P, Wall DP. 2018. A mobile game for automatic emotion-labeling of images. IEEE Trans. Games 12:2213–18
    [Google Scholar]
  21. 21.
    Kalantarian H, Jedoui K, Washington P, Tariq Q, Dunlap K et al. 2019. Labeling images with facial emotion and the potential for pediatric healthcare. Artif. Intell. Med. 98:77–86
    [Google Scholar]
  22. 22.
    Kalantarian H, Washington P, Schwartz J, Daniels J, Haber N et al. 2018. A gamified mobile system for crowdsourcing video for autism research. 2018 IEEE International Conference on Healthcare Informatics (ICHI)350–52. Washington, DC: IEEE Comput. Soc.
    [Google Scholar]
  23. 23.
    Hou C, Kalantarian H, Washington P, Dunlap K, Wall DP. 2021. Leveraging video data from a digital smartphone autism therapy to train an emotion detection classifier. medRxiv 2021.07.28.21260646. https://doi.org/10.1101/2021.07.28.21260646
    [Crossref]
  24. 24.
    Kalantarian H, Jedoui K, Dunlap K, Schwartz J, Washington P et al. 2020. The performance of emotion classifiers for children with parent-reported autism: quantitative feasibility study. JMIR Ment. Health 7:4e13174
    [Google Scholar]
  25. 25.
    Washington P, Kalantarian H, Kent J, Husic A, Kline A et al. 2022. Improved digital therapy for developmental pediatrics using domain-specific artificial intelligence: machine learning study. JMIR Pediatr. Parent. 5:2e26760
    [Google Scholar]
  26. 26.
    Washington P, Kalantarian H, Kent J, Husic A, Kline A et al. 2020. Training an emotion detection classifier using frames from a mobile therapeutic game for children with developmental disorders. arXiv:2012.08678 [cs.CV]
  27. 27.
    Penev Y, Dunlap K, Husic A, Hou C, Washington P et al. 2021. A mobile game platform for improving social communication in children with autism: a feasibility study. Appl. Clin. Inform. 12:51030–40
    [Google Scholar]
  28. 28.
    Scassellati B, Admoni H, Matarić M. 2012. Robots for use in autism research. Annu. Rev. Biomed. Eng. 14:275–94
    [Google Scholar]
  29. 29.
    Scassellati B. 2007. How social robots will help us to diagnose, treat, and understand autism. In Robotics Researched. S Thrun, R Brooks, H Durrant-Whytepp. 552–63 Berlin: Springer
    [Google Scholar]
  30. 30.
    Haber N, Voss C, Wall D. 2020. Making emotions transparent: Google Glass helps autistic kids understand facial expressions through augmented-reality therapy. IEEE Spectr. 57:446–52
    [Google Scholar]
  31. 31.
    Kline A, Voss C, Washington P, Haber N, Schwartz H et al. 2019. Superpower glass. GetMobile Mobile Comput. Commun. 23:235–38
    [Google Scholar]
  32. 32.
    Voss C, Washington P, Haber N, Kline A, Daniels J et al. 2016. Superpower Glass: delivering unobtrusive real-time social cues in wearable systems. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing1218–26. New York: Assoc. Comput. Mach.
    [Google Scholar]
  33. 33.
    Washington P, Voss C, Haber N, Tanaka S, Daniels J et al. 2016. A wearable social interaction aid for children with autism. Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems2348–54. New York: Assoc. Comput. Mach.
    [Google Scholar]
  34. 34.
    Washington P, Voss C, Kline A, Haber N, Daniels J et al. 2017. SuperpowerGlass: a wearable aid for the at-home therapy of children with autism. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1:3112
    [Google Scholar]
  35. 35.
    Daniels J, Haber N, Voss C, Schwartz J, Tamura S et al. 2018. Feasibility testing of a wearable behavioral aid for social learning in children with autism. Appl. Clin. Inform. 9:1129–40
    [Google Scholar]
  36. 36.
    Daniels J, Schwartz J, Haber N, Voss C, Kline A et al. 2017. Design and efficacy of a wearable device for social affective learning in children with autism. J. Am. Acad. Child Adolesc. Psychiatry 56:10S257 Abstr. )
    [Google Scholar]
  37. 37.
    Voss C, Schwartz J, Daniels J, Kline A, Haber N et al. 2019. Effect of wearable digital intervention for improving socialization in children with autism spectrum disorder: a randomized clinical trial. JAMA Pediatr. 173:5446–54
    [Google Scholar]
  38. 38.
    Nag A, Haber N, Voss C, Tamura S, Daniels J et al. 2020. Toward continuous social phenotyping: analyzing gaze patterns in an emotion recognition task for children with autism through wearable smart glasses. J. Med. Internet Res. 22:4e13810
    [Google Scholar]
  39. 39.
    Jones W, Klin A. 2013. Attention to eyes is present but in decline in 2–6-month-old infants later diagnosed with autism. Nature 504:7480427–31
    [Google Scholar]
  40. 40.
    Riby D, Hancock PJ. 2009. Looking at movies and cartoons: eye-tracking evidence from Williams syndrome and autism. J. Intellect. Disabil. Res. 53:2169–81
    [Google Scholar]
  41. 41.
    Chawarska K, Macari S, Shic F. 2013. Decreased spontaneous attention to social scenes in 6-month-old infants later diagnosed with autism spectrum disorders. Biol. Psychiatry 74:3195–203
    [Google Scholar]
  42. 42.
    Campbell K, Carpenter KL, Hashemi J, Espinosa S, Marsan S et al. 2019. Computer vision analysis captures atypical attention in toddlers with autism. Autism 23:3619–28
    [Google Scholar]
  43. 43.
    Sadria M, Karimi S, Layton AT. 2019. Network centrality analysis of eye-gaze data in autism spectrum disorder. Comput. Biol. Med. 111:103332
    [Google Scholar]
  44. 44.
    Varma M, Washington P, Chrisman B, Kline A, Leblanc E et al. 2022. Identification of social engagement indicators associated with autism spectrum disorder using a game-based mobile app: comparative study of gaze fixation and visual scanning methods. J. Med. Internet Res. 24:2e31830
    [Google Scholar]
  45. 45.
    Alvari G, Coviello L, Furlanello C. 2021. EYE-C: eye-contact robust detection and analysis during unconstrained child-therapist interactions in the clinical setting of autism spectrum disorders. Brain Sci. 11:121555
    [Google Scholar]
  46. 46.
    Wen TH, Cheng A, Andreason C, Zahiri J, Xiao Y et al. 2022. Large scale validation of an early-age eye-tracking biomarker of an autism spectrum disorder subtype. Sci. Rep. 12:4253
    [Google Scholar]
  47. 47.
    Putra PU, Shima K, Alvarez SA, Shimatani K. 2021. Identifying autism spectrum disorder symptoms using response and gaze behavior during the Go/NoGo game CatChicken. Sci. Rep. 11:22012
    [Google Scholar]
  48. 48.
    Guha T, Yang Z, Grossman RB, Narayanan SS. 2016. A computational study of expressive facial dynamics in children with autism. IEEE Trans. Affect. Comput. 9:114–20
    [Google Scholar]
  49. 49.
    Egger HL, Dawson G, Hashemi J, Carpenter KL, Espinosa S et al. 2018. Automatic emotion and attention analysis of young children at home: a ResearchKit autism feasibility study. npj Digital Med. 1:20
    [Google Scholar]
  50. 50.
    Martin KB, Hammal Z, Ren G, Cohn JF, Cassell J et al. 2018. Objective measurement of head movement differences in children with and without autism spectrum disorder. Mol. Autism 9:14
    [Google Scholar]
  51. 51.
    Dawson G, Campbell K, Hashemi J, Lippmann SJ, Smith V et al. 2018. Atypical postural control can be detected via computer vision analysis in toddlers with autism spectrum disorder. Sci. Rep. 8:17008
    [Google Scholar]
  52. 52.
    Hudenko WJ, Stone W, Bachorowski JA. 2009. Laughter differs in children with autism: an acoustic analysis of laughs produced by children with and without the disorder. J. Autism Dev. Disord. 39:101392–400
    [Google Scholar]
  53. 53.
    Orlandi S, Manfredi C, Bocchi L, Scattoni ML. 2012. Automatic newborn cry analysis: a non-invasive tool to help autism early diagnosis. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society2953–56. Piscataway, NJ: IEEE
    [Google Scholar]
  54. 54.
    Jordan MI, Mitchell TM. 2015. Machine learning: trends, perspectives, and prospects. Science 349:6245255–60
    [Google Scholar]
  55. 55.
    Wall DP, Kosmicki J, Deluca TF, Harstad E, Fusaro VA. 2012. Use of machine learning to shorten observation-based screening and diagnosis of autism. Transl. Psychiatry 2:4e100
    [Google Scholar]
  56. 56.
    Washington P, Leblanc E, Dunlap K, Penev Y, Kline A et al. 2020. Precision telemedicine through crowdsourced machine learning: testing variability of crowd workers for video-based autism feature recognition. J. Pers. Med. 10:386
    [Google Scholar]
  57. 57.
    Washington P, Leblanc E, Dunlap K, Penev Y, Varma M et al. 2020. Selection of trustworthy crowd workers for telemedical diagnosis of pediatric autism spectrum disorder. Pac. Symp. Biocomput. 26:14–25
    [Google Scholar]
  58. 58.
    Washington P, Kalantarian H, Tariq Q, Schwartz J, Dunlap K et al. 2019. Validity of online screening for autism: crowdsourcing study comparing paid and unpaid diagnostic tasks. J. Med. Internet Res. 21:5e13668
    [Google Scholar]
  59. 59.
    Abbas H, Garberson F, Glover E, Wall DP. 2018. Machine learning approach for early detection of autism by combining questionnaire and home video screening. J. Am. Med. Inform. Assoc. 25:81000–7
    [Google Scholar]
  60. 60.
    Abbas H, Garberson F, Glover E, Wall DP. 2017. Machine learning for early detection of autism (and other conditions) using a parental questionnaire and home video screening. 2017 IEEE International Conference on Big Data3558–61. Piscataway, NJ: IEEE
    [Google Scholar]
  61. 61.
    Abbas H, Garberson F, Liu-Mayo S, Glover E, Wall DP. 2020. Multi-modular AI approach to streamline autism diagnosis in young children. Sci. Rep. 10:5014
    [Google Scholar]
  62. 62.
    Fusaro VA, Daniels J, Duda M, DeLuca TF, D'Angelo O et al. 2014. The potential of accelerating early detection of autism through content analysis of YouTube videos. PLOS ONE 9:4e93533
    [Google Scholar]
  63. 63.
    Kanne SM, Carpenter LA, Warren Z. 2018. Screening in toddlers and preschoolers at risk for autism spectrum disorder: evaluating a novel mobile-health screening tool. Autism Res. 11:71038–49
    [Google Scholar]
  64. 64.
    Tariq Q, Daniels J, Schwartz JN, Washington P, Kalantarian H et al. 2018. Mobile detection of autism through machine learning on home video: a development and prospective validation study. PLOS Med. 15:11e1002705
    [Google Scholar]
  65. 65.
    Tariq Q, Fleming SL, Schwartz JN, Dunlap K, Corbin C et al. 2019. Detecting developmental delay and autism through machine learning models using home videos of Bangladeshi children: development and validation study. J. Med. Internet Res. 21:4e13822
    [Google Scholar]
  66. 66.
    Duda M, Daniels J, Wall DP. 2016. Clinical evaluation of a novel and mobile autism risk assessment. J. Autism Dev. Disord. 46:61953–61
    [Google Scholar]
  67. 67.
    Levy S, Duda M, Haber N, Wall DP. 2017. Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism. Mol. Autism 8:65
    [Google Scholar]
  68. 68.
    Washington P, Paskov KM, Kalantarian H, Stockham N, Voss C et al. 2020. Feature selection and dimension reduction of social autism data. Pac. Symp. Biocomput. 25:707–18
    [Google Scholar]
  69. 69.
    Washington P, Tariq Q, Leblanc E, Chrisman B, Dunlap K et al. 2021. Crowdsourced privacy-preserved feature tagging of short home videos for machine learning ASD detection. Sci. Rep. 11:7620
    [Google Scholar]
  70. 70.
    Washington P, Chrisman B, Leblanc E, Dunlap K, Kline A et al. 2022. Crowd annotations can approximate clinical autism impressions from short home videos with privacy protections. Intell.-Based Med. 6:100056
    [Google Scholar]
  71. 71.
    Liu W, Li M, Yi L. 2016. Identifying children with autism spectrum disorder based on their face processing abnormality: a machine learning framework. Autism Res. 9:8888–98
    [Google Scholar]
  72. 72.
    Duan H, Zhai G, Min X, Che Z, Fang Y et al. 2019. A dataset of eye movements for the children with autism spectrum disorder. Proceedings of the 10th ACM Multimedia Systems Conference255–60. New York: Assoc. Comput. Mach.
    [Google Scholar]
  73. 73.
    Chang Z, Di Martino JM, Aiello R, Baker J, Carpenter K et al. 2021. Computational methods to measure patterns of gaze in toddlers with autism spectrum disorder. JAMA Pediatr. 175:8827–36
    [Google Scholar]
  74. 74.
    Oliveira JS, Franco FO, Revers MC, Silva AF, Portolese J et al. 2021. Computer-aided autism diagnosis based on visual attention models using eye tracking. Sci. Rep. 11:10131
    [Google Scholar]
  75. 75.
    Drimalla H, Landwehr N, Baskow I, Behnia B, Roepke S et al. 2019. Detecting autism by analyzing a simulated social interaction. Joint European Conference on Machine Learning and Knowledge Discovery in Databases193–208. Cham, Switz.: Springer
    [Google Scholar]
  76. 76.
    Drimalla H, Scheffer T, Landwehr N, Baskow I, Roepke S et al. 2020. Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT). npj Digital Med. 3:25
    [Google Scholar]
  77. 77.
    Khosla Y, Ramachandra P, Chaitra N. 2021. Detection of autistic individuals using facial images and deep learning. 2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions1–5. Piscataway, NJ: IEEE
    [Google Scholar]
  78. 78.
    Hashemi J, Spina TV, Tepper M, Esler A, Morellas V et al. 2012. A computer vision approach for the assessment of autism-related behavioral markers. 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics1–7. Piscataway, NJ: IEEE
    [Google Scholar]
  79. 79.
    Lidstone DE, Rochowiak R, Pacheco C, Tunçgenç B, Vidal R et al. 2021. Automated and scalable Computerized Assessment of Motor Imitation (CAMI) in children with Autism Spectrum Disorder using a single 2D camera: a pilot study. Res. Autism Spectr. Disord. 87:101840
    [Google Scholar]
  80. 80.
    Kojovic N, Natraj S, Mohanty SP, Maillart T, Schaer M. 2021. Using 2D video-based pose estimation for automated prediction of autism spectrum disorders in young children. Sci. Rep. 11:15069
    [Google Scholar]
  81. 81.
    Cook A, Mandal B, Berry D, Johnson M. 2019. Towards automatic screening of typical and atypical behaviors in children with autism. 2019 IEEE International Conference on Data Science and Advanced Analytics504–10. Piscataway, NJ: IEEE
    [Google Scholar]
  82. 82.
    Li B, Sharma A, Meng J, Purushwalkam S, Gowen E. 2017. Applying machine learning to identify autistic adults using imitation: an exploratory study. PLOS ONE 12:8e0182652
    [Google Scholar]
  83. 83.
    Anzulewicz A, Sobota K, Delafield-Butt JT. 2016. Toward the autism motor signature: Gesture patterns during smart tablet gameplay identify children with autism. Sci. Rep. 6:31107
    [Google Scholar]
  84. 84.
    Cavallo A, Romeo L, Ansuini C, Battaglia F, Nobili L et al. 2021. Identifying the signature of prospective motor control in children with autism. Sci. Rep. 11:3165
    [Google Scholar]
  85. 85.
    Li M, Tang D, Zeng J, Zhou T, Zhu H et al. 2019. An automated assessment framework for atypical prosody and stereotyped idiosyncratic phrases related to autism spectrum disorder. Comput. Speech Lang. 56:80–94
    [Google Scholar]
  86. 86.
    Chi NA, Washington P, Kline A, Husic A, Hou C et al. 2022. Classifying autism from crowdsourced semistructured speech recordings: machine learning model comparison study. JMIR Pediatr. Parent. 5:2e35406
    [Google Scholar]
  87. 87.
    Lau JC, Patel S, Kang X, Nayar K, Martin GE et al. 2022. Cross-linguistic patterns of speech prosodic differences in autism: a machine learning study. PLOS ONE 17:6e0269637
    [Google Scholar]
  88. 88.
    Maenner MJ, Yeargin-Allsopp M, Van Naarden Braun K, Christensen DL, Schieve LA. 2016. Development of a machine learning algorithm for the surveillance of autism spectrum disorder. PLOS ONE 11:12e0168224
    [Google Scholar]
  89. 89.
    Vabalas A, Gowen E, Poliakoff E, Casson AJ. 2020. Applying machine learning to kinematic and eye movement features of a movement imitation task to predict autism diagnosis. Sci. Rep. 10:8346
    [Google Scholar]
  90. 90.
    Javed H, Park CH. 2020. Behavior-based risk detection of autism spectrum disorder through child-robot interaction. Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction275–77. New York: Assoc. Comput. Mach.
    [Google Scholar]
  91. 91.
    Constantino JN, Kennon-McGill S, Weichselbaum C, Marrus N, Haider A et al. 2017. Infant viewing of social scenes is under genetic control and is atypical in autism. Nature 547:7663340–44
    [Google Scholar]
  92. 92.
    Jones W, Carr K, Klin A. 2008. Absence of preferential looking to the eyes of approaching adults predicts level of social disability in 2-year-old toddlers with autism spectrum disorder. Arch. Gen. Psychiatry 65:8946–54
    [Google Scholar]
  93. 93.
    Klin A, Jones W, Schultz R, Volkmar F, Cohen D. 2002. Visual fixation patterns during viewing of naturalistic social situations as predictors of social competence in individuals with autism. Arch. Gen. Psychiatry 59:9809–16
    [Google Scholar]
  94. 94.
    Klin A, Lin DJ, Gorrindo P, Ramsay G, Jones W. 2009. Two-year-olds with autism orient to non-social contingencies rather than biological motion. Nature 459:7244257–61
    [Google Scholar]
  95. 95.
    Rice K, Moriuchi JM, Jones W, Klin A. 2012. Parsing heterogeneity in autism spectrum disorders: visual scanning of dynamic social scenes in school-aged children. J. Am. Acad. Child Adolesc. Psychiatry 51:3238–48
    [Google Scholar]
  96. 96.
    Shultz S, Klin A, Jones W. 2011. Inhibition of eye blinking reveals subjective perceptions of stimulus salience. PNAS 108:5221270–75
    [Google Scholar]
  97. 97.
    Shultz S, Klin A, Jones W. 2018. Neonatal transitions in social behavior and their implications for autism. Trends Cogn. Sci. 22:5452–69
    [Google Scholar]
  98. 98.
    Grote T. 2021. Trustworthy medical AI systems need to know when they don't know. J. Med. Ethics 47:5337–38
    [Google Scholar]
  99. 99.
    Hendrickx K, Perini L, Van der Plas D, Meert W, Davis J. 2021. Machine learning with a reject option: a survey. arXiv:2107.11277 [cs.LG]
  100. 100.
    Kompa B, Snoek J, Beam AL. 2021. Second opinion needed: communicating uncertainty in medical machine learning. npj Digital Med. 4:4
    [Google Scholar]
  101. 101.
    Char DS, Shah NH, Magnus D. 2018. Implementing machine learning in health care—addressing ethical challenges. N. Engl. J. Med. 378:11981–83
    [Google Scholar]
  102. 102.
    Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. 2021. A survey on bias and fairness in machine learning. ACM Comput. Surv. 54:6115
    [Google Scholar]
  103. 103.
    Feliciano P, Daniels AM, Snyder LG, Beaumont A, Camba A et al. 2018. SPARK: a US cohort of 50,000 families to accelerate autism research. Neuron 97:3488–93
    [Google Scholar]
  104. 104.
    Kollins SH, DeLoss DJ, Cañadas E, Lutz J, Findling RL et al. 2020. A novel digital intervention for actively reducing severity of paediatric ADHD (STARS-ADHD): a randomised controlled trial. Lancet Digit. Health 2:4e168–78
    [Google Scholar]
  105. 105.
    Rudovic O, Lee J, Dai M, Schuller B, Picard RW. 2018. Personalized machine learning for robot perception of affect and engagement in autism therapy. Sci. Robot. 3:19eaao6760
    [Google Scholar]
  106. 106.
    Yu H, Sano A. 2022. Semi-supervised learning and data augmentation in wearable-based momentary stress detection in the wild. arXiv:2202.12935 [eess.SP]
  107. 107.
    Gardner-Hoag J, Novack M, Parlett-Pelleriti C, Stevens E, Dixon D et al. 2021. Unsupervised machine learning for identifying challenging behavior profiles to explore cluster-based treatment efficacy in children with autism spectrum disorder: retrospective data analysis study. JMIR Med. Inform. 9:6e27793
    [Google Scholar]
  108. 108.
    Duda M, Haber N, Daniels J, Wall DP. 2017. Crowdsourced validation of a machine-learning classification system for autism and ADHD. Transl. Psychiatry 7:5e1133
    [Google Scholar]
  109. 109.
    Wawer A, Chojnicka I, Okruszek L, Sarzynska-Wawer J. 2022. Single and cross-disorder detection for autism and schizophrenia. Cogn. Comput. 14:1461–73
    [Google Scholar]
  110. 110.
    Demetriou EA, Park SH, Ho N, Pepper KL, Song YJ et al. 2020. Machine learning for differential diagnosis between clinical conditions with social difficulty: autism spectrum disorder, early psychosis, and social anxiety disorder. Front. Psychiatry 11:545
    [Google Scholar]
  111. 111.
    Iakovidou N, Lanzarini E, Singh J, Fiori F, Santosh P. 2020. Differentiating females with Rett syndrome and those with multi-comorbid autism spectrum disorder using physiological biomarkers: a novel approach. J. Clin. Med. 9:92842
    [Google Scholar]
  112. 112.
    McKernan EP, Kumar M, Di Martino A, Shulman L, Kolevzon A et al. 2022. Intra-topic latency as an automated behavioral marker of treatment response in autism spectrum disorder. Sci. Rep. 12:3255
    [Google Scholar]
  113. 113.
    Kołakowska A, Landowska A, Anzulewicz A, Sobota K. 2017. Automatic recognition of therapy progress among children with autism. Sci. Rep. 7:13863
    [Google Scholar]
  114. 114.
    De Rubeis S, Buxbaum JD. 2015. Genetics and genomics of autism spectrum disorder: embracing complexity. Hum. Mol. Genet. 24:R1R24–31
    [Google Scholar]
  115. 115.
    Mefford HC, Batshaw ML, Hoffman EP. 2012. Genomics, intellectual disability, and autism. N. Engl. J. Med. 366:8733–43
    [Google Scholar]
  116. 116.
    Vorstman JA, Parr JR, Moreno-De-Luca D, Anney RJ, Nurnberger JI Jr. et al. 2017. Autism genetics: opportunities and challenges for clinical translation. Nat. Rev. Genet. 18:6362–76
    [Google Scholar]
  117. 117.
    Willsey HR, Willsey AJ, Wang B, State MW. 2022. Genomics, convergent neuroscience and progress in understanding autism spectrum disorder. Nat. Rev. Neurosci. 23:6323–41
    [Google Scholar]
  118. 118.
    Waye MM, Cheng HY. 2018. Genetics and epigenetics of autism: a review. . Psychiatry Clin. Neurosci. 72:4228–44
    [Google Scholar]
  119. 119.
    Abraham J, Szoko N, Natowicz MR. 2019. Proteomic investigations of autism spectrum disorder: past findings, current challenges, and future prospects. Reviews on Biomarker Studies in Psychiatric and Neurodegenerative Disorders PC Guest 235–52. Cham, Switz.: Springer
    [Google Scholar]
  120. 120.
    Anderson GM. 2015. Autism biomarkers: challenges, pitfalls and possibilities. J. Autism Dev. Disord. 45:41103–13
    [Google Scholar]
  121. 121.
    Al-Ayadhi L, Halepoto DM. 2013. Role of proteomics in the discovery of autism biomarkers. J. Coll. Physicians Surg. Pak. 23:137–43
    [Google Scholar]
  122. 122.
    Ristori MV, Mortera SL, Marzano V, Guerrera S, Vernocchi P et al. 2020. Proteomics and metabolomics approaches towards a functional insight onto autism spectrum disorders: phenotype stratification and biomarker discovery. Int. J. Mol. Sci. 21:176274
    [Google Scholar]
  123. 123.
    Kotsiliti E. 2022. Gut microbiome and autism spectrum disorder. Nat. Rev. Gastroenterol. Hepatol. 19:6
    [Google Scholar]
  124. 124.
    Mulle JG, Sharp WG, Cubells JF. 2013. The gut microbiome: a new frontier in autism research. Curr. Psychiatry Rep. 15:2337
    [Google Scholar]
  125. 125.
    Pulikkan J, Mazumder A, Grace T. 2019. Role of the gut microbiome in autism spectrum disorders. Review on Biomarker Studies in Psychiatric and Neurodegenerative Disorders PC Guest 253–69. Cham, Switz.: Springer
    [Google Scholar]
  126. 126.
    Khodatars M, Shoeibi A, Sadeghi D, Ghaasemi N, Jafari M et al. 2021. Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review. Comput. Biol. Med. 139:104949
    [Google Scholar]
  127. 127.
    Moon SJ, Hwang J, Kana R, Torous J, Kim JW. 2019. Accuracy of machine learning algorithms for the diagnosis of autism spectrum disorder: systematic review and meta-analysis of brain magnetic resonance imaging studies. JMIR Ment. Health 6:12e14108
    [Google Scholar]
  128. 128.
    Nogay HS, Adeli H. 2020. Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging. Rev. Neurosci. 31:8825–41
    [Google Scholar]
  129. 129.
    Pagnozzi AM, Conti E, Calderoni S, Fripp J, Rose SE. 2018. A systematic review of structural MRI biomarkers in autism spectrum disorder: a machine learning perspective. Int. J. Dev. Neurosci. 71:68–82
    [Google Scholar]
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