The future of autism: Global & local achievements & challenges

Adequate Management of Autism Spectrum Disorder in Children in India

Despite the significant burden of neurodevelopmental disorders such as autism spectrum disorder (ASD) in India, there are areas of unmet needs at every level of the health care system. This includes screening and recognition, reliable and valid tools to evaluate, and to adequately manage ASD. There are also gaps in education and training of medical professionals, paraprofessionals, special education teachers and the related services. Lack of public awareness and cultural factors contribute to delays in early recognition and interventions. A framework is suggested to address these unmet needs at various levels to improve the care of these children with ASD. These include a) a focus on education of medical professionals, paraprofessionals, and teachers; b) setting up infrastructure at community, regional, and statewide levels, with adequate funding; and c) use of audiovisual technology and collaboration with international expertise.

Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review

Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks such as autism spectrum disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to the variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.