Hybrid Convolution Neural Network Integrated with Pseudo-Newton Boosting for Lumbar Spine Degeneration Detection

The study introduces US-X Complete, a novel multi-modal deep learning method designed to enhance 3D ultrasound imaging of the lumbar spine. By integrating information from X-ray images, this approach addresses the limitations of ultrasound in visualizing complete vertebral anatomy, particularly in overcoming acoustic shadowing effects caused by bone. This advancement could significantly improve intraoperative guidance during spinal procedures.

Small Language Models (SLMs) are emerging as effective tools for specific medical imaging tasks, particularly in environments where privacy, latency, and cost are critical. This article presents a governance-ready framework that integrates prompt scaffolds, calibrated abstention, and compliance with Picture Archiving and Communication Systems (PACS). The focus is on AP/PA view tagging for chest radiographs, utilizing four deployable SLMs. The findings indicate that reflection-oriented prompts enhance performance in lighter models, while stronger models show less sensitivity to these prompts.