Project description
Novel image analysis algorithms to identify causes of chronic back pain
Chronic back pain, a major source of disability worldwide, has biomechanical, inflammatory, neurological and psychological causes. Conventional imaging and evaluation cannot point to a particular cause of back pain, but biomechanical models are capable of distinguishing between different aetiologies. The EU-funded iBack-epic aims to use novel image analysis techniques to identify biomechanical and inflammatory causes of back pain in a large cohort of participants. The study will capitalise on the recently developed spine labelling and segmentation algorithms based on deep learning to calculate individual biomechanical, functional and morphometric parameters of the spine. The quantitative image-based parameters will help discriminate between normal ageing and pathologic degeneration and identify conditions linked to the development of chronic back pain.
Objective
Chronic back pain is a major burden and source of disability worldwide. It is primarily attributed to different biomechanical factors, but can also have inflammatory, neurological or psychological causes. Clinical findings and conventional imaging cannot reliably distinguish different causes of back pain. In contrast, individual biomechanical models can quantify diverse (pathologic) loading patterns and thus could be used to distinguish different aetiologies of back pain, to better understand individual pathophysiology and guide preventive strategies.
During my recent ERC-StG “iBack”, I developed quantitative imaging methods and deep-learning based image processing to automatically generate a fully individualized biomechanical model of the thoracolumbar spine. Simultaneously, two large-scale epidemiologic studies collected clinical and high-resolution imaging data of the spine of more than 15,000 participants so far, aiming at more than 35,000 participants by mid 2022
The high-level objective of iBack-epic is to use such novel image analysis techniques to identify different biomechanical and inflammatory causes of back pain in study participants.
I will adopt and extend my recently developed deep-learning based spine labelling and segmentation algorithms to fully automatically calculate individual biomechanical, functional and morphometric parameters of the spine. In this large-scale population data, I will identify different biomechanical loading patterns, use quantitative image-based parameters to discriminate normal ageing from pathologic degeneration and identify pathological conditions that are linked to back pain or subsequent development of chronic back pain.
Such a differentiation – for the first time based on quantitative image data – will allow for a better understanding of the underlying pathophysiology of back pain, an improved risk stratification, a tailored investigation of genetic causes and thus will help to better guide preventive strategies.
Fields of science
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-AG - HORIZON Action Grant Budget-BasedHost institution
81675 Muenchen
Germany