ADLIFT – Realtime Ultrasound Imaging Framework Using Novel SSL Algorithm in IoMT
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Khan, Amjad Rehman, Tanzila Saba, Kamran Ahmad Awan, Faten S. Alamri, Abeer Rashad Mirdad, and Houbing Song. “ADLIFT – Realtime Ultrasound Imaging Framework Using Novel SSL Algorithm in IoMT.” IEEE Internet of Things Journal, August 4, 2025, 1–1. https://doi.org/10.1109/JIOT.2025.3595568.
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Subjects
Ultrasound Imaging
Computational efficiency
UMBC Security and Optimization for Networked Globe Laboratory (SONG Lab)
Semi-Supervised Learning
Real-time systems
Security
Feature extraction
Computational modeling
Artificial intelligence
Internet of Medical Things
Semisupervised learning
Training
Accuracy
Ultrasonic imaging
Computational efficiency
UMBC Security and Optimization for Networked Globe Laboratory (SONG Lab)
Semi-Supervised Learning
Real-time systems
Security
Feature extraction
Computational modeling
Artificial intelligence
Internet of Medical Things
Semisupervised learning
Training
Accuracy
Ultrasonic imaging
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.