Abstract:

Symptoms of Autistic Spectrum Disorders (ASD) can manifest as early as infancy, and the earlier detection and intervention can lead to better therapeutic results. The traditional screening and diagnosis of autism comes from professionals, which is highly subjective and time-consuming, leading to misdiagnosis or missing the optimal intervention time. In recent years, with the rapid development of artificial intelligence and the accumulation of clinical data on autism, intelligent recognition methods for autism and its early symptoms have developed rapidly. This paper summarizes the research on intelligent recognition of autistic infants in the past decade, and divides the research into six sub-areas based on the data types used in the research: 1) recognition based on classical task behavior data; 2) recognition based on facial expression and emotional data; 3) recognition based on eye gaze data; 4) recognition based on brain image data; 5) recognition based on motor control and movement pattern data; 6) recognition based on multimodal data. The current technology is to use non-contact vision system and sensory devices to collect infants’ behavioral data, such as facial expression, head and limb movement, eye movement, brain image. Researchers usually develop risk behavior detection algorithms and build machine learning or deep learning models for automatic recognition according to task objectives and data characteristics. At present, it can reach the precision of scale tools and manual evaluation. The current modeling trend is to use a multimodal fusion framework to build prediction models based on the complementary relationship between multimodal information, feature transformation and representation patterns of autistic infants, which is expected to further improve the recognition accuracy. In the future, researchers should focus on building an intelligent medical screening and diagnosis system for early autism, developing screening tools for infants and young children, establishing a refined diagnosis method for autism combined with brain imaging technology, and building an intelligent recognition model for autistic infants by integrating multimodal data. In addition, to carry out high-quality intelligent recognition research, a large-scale database of autism and high-risk infants and corresponding behavioral characteristic database should be established as soon as possible, and risk behaviors were marked in coarse and fine granularity according to the early behavioral diagnostic criteria of autism. Currently, in the face of the contradiction between the large demand for model training data and the lack of autistic samples, researchers can first try to use small sample learning methods, such as model fine-tuning, data augmentation, transfer learning.

Key words: autism spectrum disorder, early screening and diagnosis of autism, intelligent screening of autism, artificial intelligence, multimodal data