AI-Powered Measurement of Ultrasonic Axial-Transmission Velocity for Pediatric Skeletal Development Evaluation

Bone health assessment is commonly used to evaluate skeletal development and maturation and to diagnose neonatal growth disorders. Implementing an automated ultrasonic system for pediatric bone evaluation, which is radiation-free and cost-effective, to complement conventional radiography is significant for clinical applications. This study mainly proposes a method, which combines the ReliefF algorithm with fuzzy C-means clustering, for fully automatic detection of wave arrival time and estimation of ultrasonic velocity from axial transmission waveforms measured from the bone of infants without the need for signal preprocessing. The results of in-vivo velocity measurement show good consistency with the ground truth modality, indicating the feasibility of the proposed method for further clinical studies and usages.