Автоматизация анализа рентгенограмм позвоночника для объективизации оценки степени тяжести сколиотической деформации при идиопатическом сколиозе (предварительное сообщение)
Background. A large number of studies have focused on automating the process of measuring the Cobb angle. Although there is no practical tool to assist doctors with estimating the severity of the curvature of the spine and determine the best suitable treatment type.Aim. We aimed to examine the algorithms used for distinguishing vertebral column, vertebrae, and for building a tan-gent on the X-ray photographs. The superior algorithms should be implemented into the clinical practice as an instru-ment of automatic analysis of the spine X-rays in scoliosis patients.Materials and methods. A total of 300 digital X-rays of the spine of children with idiopathic scoliosis were gathered. The X-rays were manually ruled by a radiologist to determine the Cobb angle. This data was included into the main dataset used for training and validating the neural network. In addition, the Sliding Window Method algorithm was implemen-ted and compared with the machine learning algorithms, proving it to be vastly superior in the context of this research.Results. This research can serve as the foundation for the future development of an automated system for analyzing spine X-rays. This system allows processing of a large amount of data for achieving >85% in training neural network to determine the Cobb angle.Conclusions. This research is the first step toward the development of a modern innovative product that uses artificial intelligence for distinguishing the different portions of the spine on 2D X-ray images for building the lines required to determine the Cobb angle.