LAYER: A Quantitative Explainable AI Framework for Decoding Tissue-Layer Drivers of Myofascial Low Back Pain

TL;DR

This paper introduces LAYER, an explainable AI framework analyzing six tissue layers in 3D ultrasound to identify tissue drivers of myofascial low back pain, revealing significant roles for non-muscle tissues.

Contribution

LAYER is the first anatomically grounded AI method that quantifies tissue layer contributions to pain, challenging muscle-centric views and offering new insights for diagnosis and therapy.

Findings

Non-muscle tissues significantly contribute to pain prediction.

Deep fascial membrane shows highest saliency in B-mode imaging.

Non-muscle layers nearly match muscle in combined imaging saliency.

Abstract

Myofascial pain (MP) is a leading cause of chronic low back pain, yet its tissue-level drivers remain poorly defined and lack reliable image biomarkers. Existing studies focus predominantly on muscle while neglecting fascia, fat, and other soft tissues that play integral biomechanical roles. We developed an anatomically grounded explainable artificial intelligence (AI) framework, LAYER (Layer-wise Analysis for Yielding Explainable Relevance Tissue), that analyses six tissue layers in three-dimensional (3D) ultrasound and quantifies their contribution to MP prediction. By utilizing the largest multi-model 3D ultrasound cohort consisting of over 4,000 scans, LAYER reveals that non-muscle tissues contribute substantially to pain prediction. In B-mode imaging, the deep fascial membrane (DFM) showed the highest saliency (0.420), while in combined B-mode and shear-wave images, the collective saliency of non-muscle layers (0.316) nearly matches that of muscle (0.317), challenging the conventional muscle-centric paradigm in MP research and potentially affecting the therapy methods. LAYER establishes a quantitative, interpretable framework for linking layer-specific anatomy to pain physiology, uncovering new tissue targets and providing a generalizable approach for explainable analysis of soft-tissue imaging.