This paper is in the following e-collection/theme issue:

Autism Spectrum Disorder (ASD) (109) New Methods (944) Decision Support for Health Professionals (1185) Machine Learning (1465) Diagnostic Tools in Mental Health (242)

Published on in Vol 6, No 12 (2019): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14108, first published .
Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies

Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies

Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies

Authors of this article:

Sun Jae Moon1 Author Orcid Image ;   Jinseub Hwang2 Author Orcid Image ;   Rajesh Kana3 Author Orcid Image ;   John Torous4 Author Orcid Image ;   Jung Won Kim5 Author Orcid Image
Article Authors Cited by (44) Tweetations (15) Metrics

Journals

  1. Song J, Yoon N, Jang S, Lee G, Kim B. Neuroimaging-Based Deep Learning in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder. Journal of the Korean Academy of Child and Adolescent Psychiatry 2020;31(3):97 View
  2. Popovic D, Schiltz K, Falkai P, Koutsouleris N. Präzisionspsychiatrie und der Beitrag von Brain Imaging und anderen Biomarkern. Fortschritte der Neurologie · Psychiatrie 2020;88(12):778 View
  3. Balcombe L, De Leo D. Digital Mental Health Challenges and the Horizon Ahead for Solutions. JMIR Mental Health 2021;8(3):e26811 View
  4. Graña M, Silva M. Impact of Machine Learning Pipeline Choices in Autism Prediction From Functional Connectivity Data. International Journal of Neural Systems 2021;31(04):2150009 View
  5. Cavus N, Lawan A, Ibrahim Z, Dahiru A, Tahir S, Abdulrazak U, Hussaini A. A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder. Journal of Personalized Medicine 2021;11(4):299 View
  6. Wang H, Avillach P. Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning. JMIR Medical Informatics 2021;9(4):e24754 View
  7. Lin P, Moni M, Gau S, Eapen V. Identifying Subgroups of Patients With Autism by Gene Expression Profiles Using Machine Learning Algorithms. Frontiers in Psychiatry 2021;12 View
  8. Squarcina L, Nosari G, Marin R, Castellani U, Bellani M, Bonivento C, Fabbro F, Molteni M, Brambilla P. Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine. Brain and Behavior 2021;11(8) View
  9. Ghosh T, Banna M, Rahman M, Kaiser M, Mahmud M, Hosen A, Cho G. Artificial intelligence and internet of things in screening and management of autism spectrum disorder. Sustainable Cities and Society 2021;74:103189 View
  10. Jayakumar S, Sounderajah V, Normahani P, Harling L, Markar S, Ashrafian H, Darzi A. Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study. npj Digital Medicine 2022;5(1) View
  11. Liu X, Chen J, Zhang K, Wang X, Wang G, Zhang R. The evaluation of the cognitive and language abilities of autistic children with interactive game technology based on the PEP-3 scale. Education and Information Technologies 2022;27(9):12027 View
  12. Abd-alrazaq A, Alhuwail D, Schneider J, Toro C, Ahmed A, Alzubaidi M, Alajlani M, Househ M. The performance of artificial intelligence-driven technologies in diagnosing mental disorders: an umbrella review. npj Digital Medicine 2022;5(1) View
  13. Balcombe L, De Leo D. Digital Mental Health Amid COVID-19. Encyclopedia 2021;1(4):1047 View
  14. Vieira S, Liang X, Guiomar R, Mechelli A. Can we predict who will benefit from cognitive-behavioural therapy? A systematic review and meta-analysis of machine learning studies. Clinical Psychology Review 2022;97:102193 View
  15. Balcombe L, De Leo D. The Potential Impact of Adjunct Digital Tools and Technology to Help Distressed and Suicidal Men: An Integrative Review. Frontiers in Psychology 2022;12 View
  16. Dwyer D, Koutsouleris N. Annual Research Review: Translational machine learning for child and adolescent psychiatry. Journal of Child Psychology and Psychiatry 2022;63(4):421 View
  17. Kong X, Wei Z, Sun B, Tu Y, Huang Y, Cheng M, Yu S, Wilson G, Park J, Feng Z, Vangel M, Kong J, Wan G. Different Eye Tracking Patterns in Autism Spectrum Disorder in Toddler and Preschool Children. Frontiers in Psychiatry 2022;13 View
  18. Bowe A, Lightbody G, Staines A, Murray D. Big data, machine learning, and population health: predicting cognitive outcomes in childhood. Pediatric Research 2023;93(2):300 View
  19. Nguyen T, Ho C, Bui H, Ho L, Ta V. Multidimensional Machine Learning for Assessing Parameters Associated With COVID-19 in Vietnam: Validation Study. JMIR Formative Research 2023;7:e42895 View
  20. Alsaade F, Alzahrani M, Koundal D. Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms. Computational Intelligence and Neuroscience 2022;2022:1 View
  21. Wei Q, Cao H, Shi Y, Xu X, Li T. Machine learning based on eye-tracking data to identify Autism Spectrum Disorder: A systematic review and meta-analysis. Journal of Biomedical Informatics 2023;137:104254 View
  22. Kim J, Bang S, Yang J, Kwon H, Jang S, Roh S, Kim S, Kim M, Lee H, Lee J, Kim B. Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data. Journal of Autism and Developmental Disorders 2023;53(1):25 View
  23. Balcombe L, De Leo D. Human-Computer Interaction in Digital Mental Health. Informatics 2022;9(1):14 View
  24. Dubey I, Bishain R, Dasgupta J, Bhavnani S, Belmonte M, Gliga T, Mukherjee D, Lockwood Estrin G, Johnson M, Chandran S, Patel V, Gulati S, Divan G, Chakrabarti B. Using mobile health technology to assess childhood autism in low-resource community settings in India: An innovation to address the detection gap. Autism 2024;28(3):755 View
  25. Li L, Haley L, Boyd A, Bernstam E. Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review. Journal of Biomedical Informatics 2023;147:104531 View
  26. Valizadeh A, Moassefi M, Nakhostin-Ansari A, Heidari Some’eh S, Hosseini-Asl H, Saghab Torbati M, Aghajani R, Maleki Ghorbani Z, Menbari-Oskouie I, Aghajani F, Mirzamohamadi A, Ghafouri M, Faghani S, Memari A. Automated diagnosis of autism with artificial intelligence: State of the art. Reviews in the Neurosciences 2024;35(2):141 View
  27. Rashid A, Shaker S. Autism spectrum Disorder detection Using Face Features based on Deep Neural network. Wasit Journal of Computer and Mathematics Science 2023;2(1):74 View
  28. Washington P, Wall D. A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism. Annual Review of Biomedical Data Science 2023;6(1):211 View
  29. Li Y, Huang W, Song P. A face image classification method of autistic children based on the two-phase transfer learning. Frontiers in Psychology 2023;14 View
  30. Giansanti D. An Umbrella Review of the Fusion of fMRI and AI in Autism. Diagnostics 2023;13(23):3552 View
  31. Koehler J, Dong M, Song D, Bong G, Koutsouleris N, Yoo H, Falter-Wagner C. Classifying autism in a clinical population based on motion synchrony: a proof-of-concept study using real-life diagnostic interviews. Scientific Reports 2024;14(1) View
  32. Kim H, Leventhal B, Koh Y, Gennatas E, Kim Y. Development and Validation of Prediction Models for the Diagnosis of Autism Spectrum Disorder in a Korean General Population. JAACAP Open 2024 View
  33. Chawla M, Panda S, Khullar V. SMILEY—assistive application to support social and emotional skills in SPCD individuals. Medical & Biological Engineering & Computing 2024;62(11):3507 View
  34. El-Ashram R, Aldaghmi O, Mohammed S. Requirements for the Application of Artificial Intelligence in Diagnosing Autism Spectrum Disorder: Specialists’ Perception. Journal of Disability Research 2024;3(7) View
  35. Xu X, Li Y, Ding N, Zang Y, Sun S, Shen G, Song X. Quantitative assessment of brain structural abnormalities in children with autism spectrum disorder based on artificial intelligence automatic brain segmentation technology and machine learning methods. Psychiatry Research: Neuroimaging 2024;345:111901 View
  36. Ding Y, Zhang H, Qiu T. Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis. BMC Psychiatry 2024;24(1) View

Books/Policy Documents

  1. Bedi P, Goyal S, Kumar J, Kumar S. Artificial Intelligence for Accurate Analysis and Detection of Autism Spectrum Disorder. View
  2. Voruganti H, Endla A, Vaibhavi B, Vatadi K, Chalichemala J. Sustainable Science and Intelligent Technologies for Societal Development. View
  3. Wagels L, Habel U, Nickl-Jockschat T. Tasman’s Psychiatry. View
  4. Wagels L, Habel U, Nickl-Jockschat T. Tasman’s Psychiatry. View