ADLIFT – Realtime Ultrasound Imaging Framework Using Novel SSL Algorithm in IoMT

Abstract

Ultrasound imaging continues to play a critical role in prenatal diagnostics, but accurate interpretation remains hindered by limited labeled data, inconsistent pseudo label quality, and real-time processing constraints in Internet of Medical Things (IoMT) environments. Existing semi-supervised learning (SSL) frameworks fail to maintain reliable segmentation under these dynamic and resource-constrained conditions. This study proposes ADLIFT, a real-time SSL-based ultrasound processing framework designed to optimize diagnostic accuracy and computational efficiency. The approach integrates an Adaptive Dual-Layer Perception (ADLP) mechanism combining macro-level anatomical recognition with micro-level feature refinement, and a Dynamic Label Generation (DLG) module that iteratively improves pseudolabel reliability using confidence-driven feedback. Efficient Sparse Feature Extraction (ESFE) minimizes computational overhead by isolating high-activation regions, while the Temporal Contextualization Framework (TCF) ensures inter-frame consistency. Blockchain-enhanced edge computing supports secure and scalable IoMT deployment. Evaluations in HC18, FetalPlane18 and Kvasir-Segment datasets demonstrate precision of 93. 7%, decision stability of 92. 8%, interpretability index of 91. 5%, uncertainty handling efficiency of 89. 7%, trust reliability score of 95. 3%, and processing latency of 28.1 ms per frame.