A review of machine learning-based methods for automatically detecting autism spectrum disorder in children's faces

As a result of structural abnormalities in the brain, autism spectrum disorder (ASD) is a neurodevelopmental disorder. ASD has traditionally been diagnosed using a variety of methods, each with its own advantages and disadvantages. In previous research on improving automated ASD diagnosis, the focus has been on developing algorithms and processing methods, rather than identifying tools. Studies interested in developing statistically robust, computationally advanced, and clinically valid techniques for mining data related to ASD can study and explore machine learning-based methods for automatically detecting ASD in children's facial features. These methods involve using artificial intelligence algorithms to identify facial features associated with ASD. This article provides information about these methods, which can be found in the ASD literature. This implies that these methods might improve ASD detection accuracy in the future, while simultaneously reducing the need for time-consuming physical diagnoses. There is still a need for further research to improve the effectiveness and reliability of these methods. The tactics that were analyzed have the potential to significantly increase the rate of autism diagnosis in young children.