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Machine Learning Techniques Implementation for Classification of Patients with Scoliosis

Grant number: 19/09873-3
Support type:Scholarships abroad - Research
Effective date (Start): January 15, 2020
Effective date (End): January 14, 2021
Field of knowledge:Engineering - Biomedical Engineering
Principal researcher:Mateus Fernandes Réu Urban
Grantee:Mateus Fernandes Réu Urban
Host: Tomas Ward
Home Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
Research place: Dublin City University (DCU), Ireland  

Abstract

Scoliosis is a pathology characterized by abnormal curvatures of the vertebral column that influences individuals of different age groups. These curvatures can lead from pain and discomfort to influences on postural control, increasing the risk of falls in individuals. The fall is harmful to anyone because in more serious cases can lead to death. Health professionals through radiological examinations, in which the Cobb angle is characterized, perform the diagnosis and analysis of the treatment. Depending on the case, several tests are taken, exposing the patient to ionizing radiation, which in the long time can generate other pathologies due to excessive exposure to radiation that could be avoided. Although there are other ways to measure the curvature of the pathology, a very interesting and little explored form is the evaluation of the plantar pressure distribution by a baropodometer. In addition to the use of modern instruments, machine-learning algorithms can assist in the diagnosis and treatment of patients. Thus, the present work intends to implement algorithms of artificial intelligence for assisting the classification of patients with scoliosis. Therefore, will be possible to determine the severity of the pathology and assist health professionals in the treatment decision for patient.

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