Descripción del proyecto
Nuevos algoritmos de análisis de imágenes para identificar las causas de la dorsalgia crónica
El dolor de espalda crónico es una de las principales causas de discapacidad en todo el mundo y cuyo origen puede ser biomecánico, inflamatorio, neurológico o psicológico. Las técnicas de imagenología y evaluación convencionales no permiten identificar una causa concreta de esta afección, pero los modelos biomecánicos son capaces de distinguir entre diferentes causas. El objetivo del proyecto iBack-epic, financiado con fondos europeos, es utilizar nuevas técnicas de análisis de imágenes para identificar las causas biomecánicas e inflamatorias del dolor de espalda en una gran cohorte de participantes. En esta investigación se emplearán nuevos algoritmos de segmentación y marcaje de la columna basados en el aprendizaje profundo para calcular variables biomecánicas, funcionales y morfométricas individuales de la columna vertebral. Las variables cuantitativas derivadas de imágenes ayudarán a diferenciar entre el envejecimiento normal y la degeneración patológica, así como a identificar las afecciones relacionadas con el desarrollo del dolor de espalda crónico.
Objetivo
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.
Ámbito científico
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-AG - HORIZON Action Grant Budget-BasedInstitución de acogida
81675 Muenchen
Alemania