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

2025-11-25

AI Summary

Muscle fascial low back pain (MP) lacks a well-established pathophysiological mechanism and tissue-specific imaging biomarkers; current research overemphasizes skeletal muscle while neglecting critical non-muscular soft tissues—including fascia and adipose tissue. To address this, we propose LAYER, the first anatomy-informed, interpretable AI framework for quantitative decoding of six layered soft-tissue structures—spanning epidermis to paraspinal muscle—in 3D B-mode and shear-wave ultrasound. Leveraging inter-layer saliency analysis across a large-scale cohort (>4,000 cases), we demonstrate for the first time that non-muscular tissues—particularly the deep fascia—contribute comparably to pain prediction as muscle does, with deep fascia exhibiting the highest saliency in B-mode imaging. This work challenges the prevailing “muscle-centric” paradigm and establishes the first tissue-specific, interpretable, and quantifiable multimodal ultrasound analytics framework, offering novel mechanistic insights and a foundation for targeted therapeutic interventions in MP.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Medical and Biological Imaging
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Quantifies tissue-layer contributions to myofascial pain prediction
• Challenges muscle-centric paradigm by analyzing non-muscle tissue roles
• Provides interpretable AI framework for soft-tissue imaging analysis

Innovation

• Explainable AI framework analyzes six tissue layers
• Quantifies non-muscle tissue contributions to pain prediction
• Uses 3D ultrasound imaging for interpretable anatomical insights