In this project we intend to develop computer vision algorithms for the evaluation of the progression of the treatment of Idiopathic Scoliosis in its different stages.
Idiopathic Scoliosis is a pathology of undefined cause that produces an abnormal development in the curvature of the spine. This pathology mainly affects young adolescents, with a prevalence of up to 5.2% of the population. Its main treatment is the use of a corrective corset. If this does not work, the patient is treated by surgery to fix the curvature of the spine.
Currently, the follow-up of corset and surgical treatment is done by torso X-rays and manual analysis of spinal markers by expert surgeons. This includes known inter-expert variability, and the markers used are not yet sufficiently robust to assess disease progression. Furthermore, the need for successive X-rays carries risks of excessive radiation in young patients. For this reason, DICOMO was born as a project whose main objective is to develop tools based on artificial intelligence capable of supporting expert surgeons in this task. Specifically, it seeks to reduce variability and workload by obtaining markers such as Cobb angles automatically, and tries to find novel early markers of success in treatment by corset and surgery.