Description du projet
De nouveaux algorithmes d’analyse d’images pour identifier les causes du mal de dos chronique
Le mal de dos chronique, source majeure d’invalidité dans le monde, a des causes biomécaniques, inflammatoires, neurologiques et psychologiques. L’imagerie et l’évaluation conventionnelles ne permettent pas de mettre en évidence une cause particulière du mal de dos, mais les modèles biomécaniques sont capables de distinguer les différentes étiologies. Le projet iBack-epic, financé par l’UE, entend exploiter de nouvelles techniques d’analyse d’image pour identifier les causes biomécaniques et inflammatoires du mal de dos chez une grande cohorte de participants. L’étude s’appuiera sur les algorithmes d’étiquetage et de segmentation de la colonne vertébrale récemment développés et fondés sur l’apprentissage profond pour calculer les paramètres biomécaniques, fonctionnels et morphométriques individuels de la colonne vertébrale. Les paramètres quantitatifs basés sur l’imagerie permettront de faire la différence entre un vieillissement normal et une dégénérescence pathologique, et d’identifier les conditions liées au développement des maux de dos chroniques.
Objectif
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.
Champ scientifique
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régime de financement
HORIZON-AG - HORIZON Action Grant Budget-BasedInstitution d’accueil
81675 Muenchen
Allemagne