A Review of Developments in Generative AI, Machine Learning, and Neuroimaging for the Diagnosis of Autism.
DOI:
https://doi.org/10.21015/vtse.v13i2.2089Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with be- havioral and neurological variations. Diagnosing ASD is complicated because behavioral assessments vary from person to person, and it is challenging as it relies on subjective behavioral assessments rather than objective medical tests. Following the PRISMA guide- lines for a systematic review, 53 papers from ScienceDirect, IEEE Xplore, and PubMed databases published between 2015 and 2024 have been selected, to determine how ML (Machine Learning) and GANs (Generative Adversarial Network) enhance the use of neuroimaging techniques for the early detection of ASD. The study addresses significant dataset issues by combining discriminative machine learning models, such as SVMs (Sup- port Vector Machine) and CNNs (Convolutional Neural Network), for pattern recognition in sMRI (Structural Magnetic Resonance Imaging), fMRI (Functional Magnetic Resonance Imaging), and EEG (Electroencephalography) with GANs for data augmentation and en- hanced feature learning. The review highlights how AI algorithms can be helpful in the diagnosis of ASD, particularly in areas with limited medical facilities. For example, due to limited resources, just 1 in 628 children in Pakistan are diagnosed with autism. Future re- search will focus on methods that combine explainable AI, generative and discriminative techniques, and structured datasets, while improving model transparency, generalizabil- ity, and avoiding biased data. By integrating AI with clinical information, this research helps to design treatment protocols that may be customized to each patient’s specific requirements and enables earlier identification of autism.
References
M. W. Azeem, N. Imran, and A. Nazeer, “Pakistan and autism,” in *Encyclopedia of Autism Spectrum Disorders*, F. R. Volkmar, Ed. Cham, Switzerland: Springer, 2021, pp. 3260–3262.
T. D. Dhamale, S. U. Bhandari, V. K. Harpale, and D. Nandan, “Autism spectrum disorder detection using parallel deep convolution neural network and generative adversarial networks,” *Traitement du Signal*, vol. 41, no. 2, pp. 643–652, Apr. 2024.
Y. Zhou, G. Jia, Y. Ren, Z. Xiao, and Y. Wang, “Advancing ASD identification with neuroimaging: A novel GARL methodology integrating deep Q-learning and generative adversarial networks,” *BMC Med. Imaging*, vol. 24, no. 1, Dec. 2024.
Y. S. Kim *et al.*, “Prevalence of autism spectrum disorders in a total population sample,” *Am. J. Psychiatry*, vol. 168, no. 9, pp. 904–912, Sep. 2011. DOI: https://doi.org/10.1176/appi.ajp.2011.10101532
J. Baio, “Prevalence of autism spectrum disorder among children aged 8 years,” *Centers for Disease Control and Prevention*, Surveillance Rep., 2014. [Online]. Available: https://stacks.cdc.gov/view/cdc/22182
D. Sasayama, R. Kuge, Y. Toibana, and H. Honda, “Trends in autism spectrum disorder diagnoses in Japan, 2009 to 2019,” *JAMA Netw. Open*, vol. 4, no. 5, Art. e219234, May 2021. DOI: https://doi.org/10.1001/jamanetworkopen.2021.9234
Golden Steps ABA, “What country has the highest rate of autism?” https://www.goldenstepsaba.com/resources/what-country-has-the-highest-rate-of-autism, accessed Jan. 9, 2025.
H. Zhou *et al.*, “Prevalence of autism spectrum disorder in China: A nationwide multi-center population-based study among children aged 6-12 years,” *Neurosci. Bull.*, vol. 36, no. 9, pp. 961–971, Sep. 2020.
Autism Society of Pakistan, “Autism Society of Pakistan,” https://autismpak.com/, accessed Jan. 9, 2025.
M. L. Scattoni *et al.*, “Autism spectrum disorder prevalence in Italy: A nationwide study promoted by the Ministry of Health,” *Child Adolesc. Psychiatry Ment. Health*, vol. 17, Art. 1, Dec. 2023.
R. A. Bahathiq, H. Banjar, A. K. Bamaga, and S. K. Jarraya, “Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging,” *Front. Neuroinformatics*, vol. 16, Art. 949926, Sep. 2022.
O. Gurau, W. J. Bosl, and C. R. Newton, “How useful is electroencephalography in the diagnosis of autism spectrum disorders and the delineation of subtypes: A systematic review,” *Front. Psychiatry*, vol. 8, Art. 121, Jul. 2017. DOI: https://doi.org/10.3389/fpsyt.2017.00121
J. Kang, X. Han, J. Song, Z. Niu, and X. Li, “Identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data,” *Comput. Biol. Med.*, vol. 120, Art. 103722, 2020.
F. C. Peck *et al.*, “Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months,” *J. Neurodev. Disord.*, vol. 13, no. 1, Art. 57, Dec. 2021.
F. A. Alturki, M. Aljalal, A. M. Abdurraqeeb, K. Alsharabi, and A. A. Al-Shamma’a, “Common spatial pattern technique with EEG signals for diagnosis of autism and epilepsy disorders,” *IEEE Access*, vol. 9, pp. 24334–24349, 2021.
Y. Xu *et al.*, “Autism spectrum disorder diagnosis with EEG signals using time-series maps of brain functional connectivity and a combined CNN–LSTM model,” *Comput. Methods Programs Biomed.*, vol. 250, Jun. 2024.
T. Tang *et al.*, “A hybrid graph network model for ASD diagnosis based on resting-state EEG signals,” *Brain Res. Bull.*, vol. 206, Jan. 2024.
H. Dong, D. Chen, L. Zhang, H. Ke, and X. Li, “Subject-sensitive EEG discrimination with a fast reconstructable CNN driven by reinforcement learning: A case study of ASD evaluation,” *Neurocomputing*, vol. 449, pp. 136–145, Aug. 2021.
J. J. Esqueda-Elizondo *et al.*, “Attention measurement of an autism spectrum disorder user using EEG signals: A case study,” *Math. Comput. Appl.*, vol. 27, no. 2, Art. 21, Mar. 2022.
M. Rakić, M. Cabezas, K. Kushibar, A. Oliver, and X. Lladó, “Improving the detection of autism spectrum disorder by combining structural and functional MRI information,” *NeuroImage Clin.*, vol. 25, Jan. 2020.
M. T. Ali *et al.*, “The role of structure MRI in diagnosing autism,” *Diagnostics*, vol. 12, no. 1, Art. 165, Jan. 2022.
A. Ashraf, Z. Qingjie, W. H. K. Bangyal, and M. Iqbal, “Analysis of brain imaging data for early-age autism spectrum disorder detection using transfer learning approaches for the Internet of Things,” *IEEE Trans. Consum. Electron.*, vol. 70, no. 1, pp. 4478–4489, Feb. 2024.
H. S. Nogay and H. Adeli, “Diagnosis of autism spectrum disorder from structural brain MRI using grid-search optimization and convolutional neural networks,” *Biomed. Signal Process. Control*, vol. 79, Art. 104234, 2023.
E. Courchesne *et al.*, “Hypoplasia of cerebellar vermal lobules VI and VII in autism,” in *The Science of Mental Health: Vol. 2 – Autism*. Reprinted from *N. Engl. J. Med.*, pp. 197–202, May 1988.
D. J. Brenner and E. J. Hall, “Computed tomography—an increasing source of radiation exposure,” *N. Engl. J. Med.*, vol. 357, no. 22, pp. 2277–2284, Nov. 2007. DOI: https://doi.org/10.1056/NEJMra072149
A. S. Manglunia and A. D. Puranik, “FDG PET/CT findings in a clinically diagnosed case of childhood autism,” *Indian J. Nucl. Med.*, vol. 31, no. 2, pp. 138–140, Apr. 2016. DOI: https://doi.org/10.4103/0972-3919.178302
E. E. Kim, T. Inoue, M. C. Lee, and W. H. Wong, *Clinical PET and PET/CT: Principles and Applications*. New York, NY, USA: Springer, 2013. DOI: https://doi.org/10.1007/978-1-4419-0802-5
C. H. McCollough *et al.*, “Achieving routine sub-millisievert CT scanning: Report from the summit on management of radiation dose in CT,” *Radiology*, vol. 264, no. 2, pp. 567–580, Aug. 2012. DOI: https://doi.org/10.1148/radiol.12112265
T. Eslami, F. Almuqhim, J. S. Raiker, and F. Saeed, “Machine-learning methods for diagnosing autism spectrum disorder and ADHD using functional and structural MRI: A survey,” *Front. Neuroinformatics*, vol. 14, Art. 575999, Jan. 2021.
C. Wang, Z. Xiao, and J. Wu, “Functional-connectivity-based classification of autism using SVM-RFECV on rs-fMRI data,” *Phys. Med.*, vol. 65, pp. 99–105, Sep. 2019.
M. Xu, V. Calhoun, R. Jiang, W. Yan, and J. Sui, “Brain-imaging-based machine learning in autism spectrum disorder: Methods and applications,” *J. Neurosci. Methods*, vol. 363, Art. 109271, Sep. 2021.
I. Jeon *et al.*, “Reliable autism spectrum disorder diagnosis for pediatrics using machine learning and explainable AI,” *Diagnostics*, vol. 14, no. 22, Art. 2504, Nov. 2024.
C. Chen, Y. Wang, L. Zhang, X. Li, *et al.*, “XGBoost-based machine-learning test improves hemorrhage-prediction accuracy among geriatric patients on rivaroxaban,” *BMC Geriatr.*, vol. 23, no. 1, Dec. 2023.
K. Bhavna, A. Akhter, R. Banerjee, and D. Roy, “Explainable deep-learning framework: Decoding brain tasks and predicting performance in false-belief tasks at early childhood,” *bioRxiv*, Mar. 2024, Preprint.
J. Zhang, J. Guo, D. Lu, and Y. Cao, “ASD-SWNet: A shared-weight feature-extraction network for autism spectrum disorder diagnosis,” *Sci. Rep.*, vol. 14, Art. 875, Dec. 2024.
K. Gopinath *et al.*, “Synthetic data for generalizable learning-based neuroimaging,” *Imaging Neurosci.*, vol. 2, pp. 1–22, Nov. 2024.
B. O. Turner, E. J. Paul, M. B. Miller, and A. K. Barbey, “Small sample sizes reduce the replicability of task-based fMRI studies,” *Commun. Biol.*, vol. 1, Art. 62, Dec. 2018. DOI: https://doi.org/10.1038/s42003-018-0073-z
A. Di Martino *et al.*, “The autism brain imaging data exchange: Toward a large-scale evaluation of intrinsic brain architecture in autism,” *Mol. Psychiatry*, vol. 19, no. 6, pp. 659–667, 2014. DOI: https://doi.org/10.1038/mp.2013.78
M.-C. Lai, M. V. Lombardo, and S. Baron-Cohen, “Autism,” *Lancet*, vol. 383, no. 9920, pp. 896–910, Mar. 2014. DOI: https://doi.org/10.1016/S0140-6736(13)61539-1
R. A. Poldrack *et al.*, “Scanning the horizon: Toward transparent and reproducible neuroimaging research,” *Nat. Rev. Neurosci.*, vol. 18, no. 2, pp. 115–126, 2017. DOI: https://doi.org/10.1038/nrn.2016.167
C. Lord, M. Elsabbagh, G. Baird, and J. Veenstra-Vanderweele, “Autism spectrum disorder,” *Lancet*, vol. 392, no. 10146, pp. 508–520, Aug. 2018.
R. Botvinik-Nezer *et al.*, “Variability in the analysis of a single neuroimaging dataset by many teams,” *Nature*, vol. 582, no. 7810, pp. 84–88, 2020.
Q. Yao and H. Lu, “Brain functional-connectivity augmentation for mental-disease classification with a GAN,” in *Pattern Recognition and Computer Vision*, Z. Lin *et al.* Eds. Cham, Switzerland: Springer, 2019, pp. 444–455.
X. Geng, Q. Yao, K. Jiang, and Y. Zhu, “A VAE-GAN-based deep generative model for disorder diagnosis,” in *Proc. 2020 Int. Conf. Internet Things Intell. Appl. (ITIA)*, 2020, pp. 1–7.
G. Bouallegue and R. Djemal, “EEG data augmentation using Wasserstein GAN,” in *Proc. 2020 20th Int. Conf. Sci. Tech. Autom. Control Comput. Eng. (STA)*, 2020, pp. 40–45.
S. Bhat and E. Hortal, “GAN-based data augmentation for improving EEG-signal classification,” in *Proc. 14th PErvasive Technol. Relat. Assist. Environ. Conf. (PETRA ’21)*, New York, NY, USA, 2021, pp. 453–458.
D. Yan *et al.*, “Improving brain dysfunction prediction with a functional-connectivity generator GAN,” in *Proc. 2021 IEEE Int. Conf. Big Data*, 2021, pp. 1514–1522.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” *arXiv* preprint arXiv:1701.07875, Jan. 2017.
I. J. Goodfellow *et al.*, “Generative adversarial networks,” in *Advances in Neural Information Processing Systems 27*, 2014, pp. 2672–2680.
W. Samek, T. Wiegand, and K.-R. Müller, “Explainable artificial intelligence: Understanding, visualizing and interpreting deep-learning models,” *Proc. IEEE*, vol. 109, no. 3, pp. 247–278, 2021.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in *Lecture Notes in Computer Science*, vol. 3876. Berlin, Germany: Springer, 2006, pp. 265–284. DOI: https://doi.org/10.1007/11681878_14
S. C. Sanderson *et al.*, “Opening the ‘black box’ of informed-consent appointments for genome sequencing: A multisite observational study,” *Genet. Med.*, vol. 21, pp. 1083–1091, May 2018.
W. N. Price and I. G. Cohen, “Privacy in the age of medical big data,” *Nat. Med.*, vol. 25, no. 1, pp. 37–43, Jan. 2019.
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