Estimation of Spinal Curvature Using Machine Learning
DOI:
https://doi.org/10.61841/s1yghr76Keywords:
AIS-Adolescent Idiopathic Scoliosis, Cobb Angle, CNN-Convolutional Neural Networks, ANNArtificial Neural NetworksAbstract
One of the common types of scoliosis is Adolescent Idiopathic Scoliosis, which affects children between the ages of 10 and 18. Generally, AIS curves progress rapidly during the teenage years of patients. Growth progression of many curves grows significantly during skeletal maturity, but curves with more than 60° progress even during adulthood. Since this problem starts at an early age, it is difficult to diagnose since the angle of curvature is small and is generally recognized at older ages, say around 45 years. Symptoms of this are generally not observed at an early age and are generally visible during the late teens. It causes lower back pain, height asymmetry, a lean torso, and may cause problems to nerves. To solve this problem, generally we measure the Cobb angle manually, which is more time-consuming and unreliable. It is very challenging to achieve a highly accurate estimation of Cobb angles, as it is difficult to utilize the information of x-rays efficiently. This has sparked interest in developing methods for accurate automated spinal curvature estimation and error correction in spinal anterior-posterior x-ray images. This is done using convolutional neural networks (CNN) and other artificial neural networks (ANN). In order to estimate accurate spinal curvature and Cobb angle, we use machine learning.
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