Diagnoses of autism spectrum disorders (ASD) are typically made after toddlerhood by examining behavioural patterns. Earlier identification of ASD enables earlier intervention and better outcomes. Machine learning provides a data-driven approach of diagnosing autism at an earlier age. This review aims to summarize recent studies and technologies utilizing machine learning based strategies to screen infants and children under the age of 18 months for ASD, and identify gaps that can be addressed in the future. We reviewed nine studies based on our search criteria, which includes primary studies and technologies conducted within the last 10 years that examine children with ASD or at high risk of ASD with a mean age of less than 18 months old. The studies must use machine learning analysis of behavioural features of ASD as major methodology. A total of nine studies were reviewed, of which the sensitivity ranges from 60.7% to 95.6%, the specificity ranges from 50% to 100%, and the accuracy ranges from 60.9% to 97.7%. Factors that contribute to the inconsistent findings include the varied presentation of ASD among patients and study design differences. Previous studies have shown moderate accuracy, sensitivity and specificity in the differentiation of ASD and non-ASD individuals under the age of 18 months. The application of machine learning and artificial intelligence in the screening of ASD in infants is still in its infancy, as observed by the granularity of data available for review. As such, much work needs to be done before the aforementioned technologies can be applied into clinical practice to facilitate early screening of ASD.

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This article provides an overview of somatoform disorders. It focuses on the symptoms presented and factors that contribute to the development of somatoform disorders. Discussion:-Somatoform disorders , now also known as somatic symptom disorder(SSD) are a group of physiological disorders in which a patient experiences physical symptoms that cannot be classified nor fully explained by any general medical or neurological condition. Medically unexplained physical symptoms account for as many as 50% of new medical outpatient visits (1). Emotional symptoms, anxiety disorders, depression and substance use as well as personality disorders and childhood abuse are often comorbid with both unexplained physical symptoms and somatoform disorders(2). Somatoform disorders cause excessive and disproportionate levels of distress. It can present with symptoms such as pain which is the most common symptom, weakness, paralysis, abnormal movements such as tremor or unsteady gait, blindness, hearing loss or loss of sensation and numbness. Somatoform disorders are observed worldwide, more often in woman, with first symptoms appearing by age 25(3).