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Updated: Dec 20, 2025

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation

Published on: December 13, 2024

Using artificial intelligence algorithms to identify existing knowledge within the back pain literature.

G N Kawchuk¹, R Guan², C Keen²

¹Department of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Canada. greg.kawchuk@ualberta.ca.

European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society

|May 24, 2020

Summary

This summary is machine-generated.

Artificial intelligence (AI) analyzed back pain literature to uncover hidden patterns. The AI identified 13 known associations, offering a new tool for researchers investigating the causes of low back pain (LBP).

Keywords:

Artificial intelligence Low back pain Word2vec

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Computational linguistics
Medical informatics
Spine research

Background:

The etiology of low back pain (LBP) remains unknown in approximately 90% of cases.
Artificial intelligence (AI) algorithms demonstrate capability in identifying patterns within scientific literature.
Previous AI applications successfully identified scientific discoveries in chemistry literature.

Purpose of the Study:

To apply AI algorithms to the extensive body of back pain literature.
To identify potential unknown associations and underlying causes of LBP.
To develop a novel tool for hypothesis generation in spine-related research.

Main Methods:

A subset of PubMed abstracts containing "back" and "pain" was curated.
The Word2vec algorithm was trained to predict word proximity within the abstracts.
High-proximity word pairings across anatomy, pathology, and treatment domains were analyzed using analogy testing.

Main Results:

The analysis encompassed 50,038 abstracts, yielding 27,984 unique words and 108,252 instances of "back pain".
Analogy testing successfully identified 13 known word associations within the back pain literature.
Unique proximity representations were visualized between spinal anatomy, pathology, and treatment domains.

Conclusions:

AI algorithms can effectively extract complex concepts from back pain literature.
The study provides a novel tool for generating hypotheses regarding LBP origins.
A free, web-based application is available to facilitate investigator-driven queries and further research.