Back pain classification based on ultrasound muscle image using machine learning Available to Purchase

Due to the COVID-19 pandemic, people who suffer from back pain try to avoid physical contact with physiotherapists. Ultrasound imaging plays a very significant role in the clinical diagnosis of central neuraxial blockade, which often leads to induced pain in thoracic vertebrae as well as promoting disorders in the normal function of the trapezius muscle. Ultrasound image classification could be useful for the identification and estimation of intervertebral levels and important physiological locations such as inter-laminar and middle line spaces, as well as the depth between intrathecal and epidural spaces. The main purpose of this research is to propose a deep learning-based approach for the classification of ultrasound images for the normal and abnormal trapezius muscle activity for the thoracic vertebrae along with a brief review of artificial intelligence (AI) based automatic image segmentation. In this research, we propose a method to identify muscle conditions based on ultra-sound images. We employ machine learning to classify the severity of the pain and help describe the medication. The proposed method will help people during the COVID-19 duration feel more safe and secure. Also, with such technology, therapeutic and diagnostic capabilities will be improved in the future. The preliminary findings of this study suggest that, based on ultrasound images of thoracic vertebrae, the normal and abnormal activity of the trapezius muscle can be autonomously classified.