From Intradiscal Pressure to Multimodal Estimation of Lumbar Spinal Loads.

Area of Science:

• Biomechanics
• Spinal Load Estimation
• Low Back Pain Research

Background:

• Accurate estimation of lumbar spinal loads is vital for understanding low back pain, optimizing ergonomic interventions, and guiding surgical and rehabilitation strategies.
• Historically, intradiscal pressure (IDP) was a primary in vivo measure; newer methods include telemetry, musculoskeletal (MS) modeling, finite element (FE) analysis, hybrid approaches, and AI surrogates.

Purpose of the Study:

• To provide a narrative review of methods for estimating lumbar spinal loads.
• To synthesize the evolution and current state of spinal load estimation techniques, including invasive measures, computational modeling, and emerging AI-driven approaches.

Main Methods:

• A narrative literature review was conducted using PubMed, Scopus, and Web of Science.
• Search terms included spinal loads, IDP, telemeterized implants, MS modeling, FE analysis, hybrid MS-FE coupling, displacement/control-based methods, wearable/EMG-based approaches, and AI/machine learning surrogates.
• Included human lumbar studies and methodological contributions; excluded animal models.

Main Results:

• Invasive methods (IDP, telemeterized implants) serve as benchmarks for model validation.
• MS models estimate segmental forces (e.g., L4-L5 compression ~1-5 kN), while FE and hybrid models detail load distribution and internal stresses.
• Non-invasive methods like displacement-driven models and AI surrogates offer new pathways for estimating task-specific lumbar loads.

Conclusions:

• Invasive data support MS, FE, hybrid, and AI models, but challenges like muscle redundancy and parameter uncertainty persist.
• Intradiscal pressure is best viewed as an internal benchmark, not the sole metric of spinal load.
• A comprehensive understanding of spinal load requires considering compression, shear, moments, and internal stresses.