Fig. 2: Overview of our AI approach.
From: Automated AI based identification of autism spectrum disorder from home videos
Each home video first undergoes a selection process to ensure protocol compliance and quality. Selected videos are then processed through deep learning (DL)-based modules such as STT (speech-to-text), Key-point Detector (pose estimation), and Ball Detector (object detection) to extract sub-features. These sub-features are then transformed into clinically interpretable behavioral features. The extracted features are used to train machine learning (ML) classifiers for each of the three structured video tasks (name-response, imitation, and ball-playing). Finally, predictions from the task-specific models are integrated through an ensemble method based on confidence scores to yield the probability of ASD.
