Abstract
This editorial comment refers to the article: “Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms” by Guo et al. (Eur Radiol, 2021).
Key Points
•Deep learning may help to uncover imaging features of autism spectrum disorder on MRI.
Avoid common mistakes on your manuscript.
Autism spectrum disorder (ASD) includes a wide range of conditions associated with some degree of difficulty with social communication and interaction. Although autism may be diagnosed in early childhood, some individuals are not diagnosed until much later, sometimes in adulthood, which can have ramifications on education and employment. It is estimated that about 1 in 59 children has an ASD, with the prevalence increasing over the last decade [1]. The exact aetiology is unclear with likely a mix of genetic and environmental factors [2].
Early detection of ASD can help in deciding therapy and improve quality of life for individuals with ASD and their families. Currently, screening and diagnosis of ASD includes assessing developmental milestones such as how children interact, speak, and act which can unfortunately be subjective. Imaging currently plays a limited role in the workup of ASD, predominantly assessing other conditions that may be associated with developmental delay such as tuberous sclerosis or neurofibromatosis. There are currently no diagnostic features of ASD on magnetic resonance imaging (MRI); however, there are some structural features that have been reported in patients with ASD. In younger patients, studies have demonstrated an increase in whole brain volume when compared with controls [3, 4]. Other features include increased cortical thickness in particular in the frontal lobes, increased gyrification, ventriculomegaly, and reduced corpus callosal volume [5, 6]. Many studies however have small sample sizes, with more research in this area still required [7]. Furthermore, many of these features can be subtle and are not routinely assessed in daily reporting.
Machine learning, and in particular deep learning, has been increasingly applied to imaging in different neurological conditions where imaging can have subtle or no features in early stages of the condition, for example in Alzheimer’s disease, Parkinson’s disease, and the topic of this paper, ASD. Several papers have demonstrated the development of superficial and deep neural networks in the evaluation of ASD trained on both functional MRI (fMRI) and structural MRI (sMRI) [8]. There still remain limited deep learning models evaluating ASD on sMRI. Furthermore, multiple studies using sMRI for deep learning algorithm development have used the Autism Brain Imaging Data Exchange (ABIDE) database [8].
Published in European Radiology, Guo et al. explores the development of a series of deep learning algorithms to distinguish between individuals with ASD from typically developing controls [9]. The authors used a unique dataset from their organization with cases including age-matched controls. The deep learning algorithms were trained on multiple MRI sequences with the primary model developed on ResNet-18, a well-known deep learning architecture, embedding a “Channel-Spatial” block into the 3D ResNet-18 model. The models were evaluated on an independent test set with metrics including AUC, accuracy, sensitivity, and specificity. Of note, the paper demonstrates a higher performance in FLAIR and ADC sequences highlighting that further work into these sequences may be beneficial. Importantly, the authors also look to visually understand the deep learning algorithms’ output through the use of gradient-weighted class activation heat maps, which highlighted several regions of the brain including the corpus callosum, cingulate gyrus, and middle cerebral peduncle.
As the applications of deep learning in radiology are increasing, researchers are beginning to explore conditions which may not traditionally use radiology for diagnosis. This in turn highlights the importance of the need for visually explainable deep learning models, which may enable clinicians and researchers to uncover new features associated with different medical conditions, potentially making radiology a useful screening or diagnostic tool in conditions such as autism spectrum disorder in the future.
References
Maenner MJ, Shaw KA, Baio J et al (2020) Prevalence of autism spectrum disorder among children aged 8 years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2016. MMWR Surveill Summ 69:1–12
Cerasa A, Ruta L, Marino F, Biamonti G, Pioggia G (2021) Brief report: Neuroimaging endophenotypes of social robotic applications in autism spectrum disorder. J Autism Dev Disord 51:2538–2542
Libero LE, Nordahl CW, Li DD, Ferrer E, Rogers SJ, Amaral DG (2016) Persistence of megalencephaly in a subgroup of young boys with autism spectrum disorder. Autism Res 9:1169–1182
Brun CC, Nicolson R, Leporé N et al (2009) Mapping brain abnormalities in boys with autism. Hum Brain Mapp 30:3887–3900
Patriquin MA, DeRamus T, Libero LE, Laird A, Kana RK (2016) Neuroanatomical and neurofunctional markers of social cognition in autism spectrum disorder. Hum Brain Mapp 37:3957–3978
Fredo ARJ, Jac Fredo AR, Kavitha G, Ramakrishnan S (2014) Analysis of sub-cortical regions in cognitive processing using fuzzy c-means clustering and geometrical measure in autistic MR images. 2014 40th Annual Northeast Bioengineering Conference (NEBEC)
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
Eslami T, Almuqhim F, Raiker JS, Saeed F (2020) Machine learning methods for diagnosing autism spectrum disorder and attention deficit/hyperactivity disorder using functional and structural MRI: a survey. Front Neuroinform 14:575999
Guo X, Wang J, Wang X et al (2021) Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms. Eur Radiol. https://doi.org/10.1007/s00330-021-08239-
Funding
The authors state that this work has not received any funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Guarantor
The scientific guarantor of this publication is Jennifer Tang.
Conflict of interest
The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Informed consent
Written informed consent was not required for this study because this is an editorial without any study subjects.
Ethical approval
Institutional review board approval was not required because this is an editorial without any study subjects.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This comment refers to the article available at https://doi.org/10.1007/s00330-021-08239-4
Rights and permissions
About this article
Cite this article
Tang, J. Editorial comment on “Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms”. Eur Radiol 32, 759–760 (2022). https://doi.org/10.1007/s00330-021-08371-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00330-021-08371-1