A Critical Analysis on Vertebra Identification and Cobb Angle Estimation Using Deep Learning for Scoliosis Detection

Abstract

Scoliosis is a complicated spinal deformity, and millions of people are suffering from this disease worldwide. Early detection and accurate scoliosis assessment are vital for effective clinical management and patient outcomes. The Cobb Angle (CA) measurement is the most precise method for calculating scoliotic curvature, which plays an essential role in diagnosing and treating scoliosis. This letter has conducted a systematic review to analyze scoliosis detection by vertebra identification and CA estimation using the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) guidelines. The major scientific databases such as Scopus, Web of Science (WoS), and IEEE Xplorer are explored, where 2017–2023 publications are considered. The article selection process is based on keywords like “Vertebra Identification,” “CA Estimation,” “Scoliosis Detection,” “Deep Learning (DL),” etc. After rigorous analysis, 413 articles are extracted, and 44 are identified for final consideration. Further, several investigations based on the previous work are discussed along with its Proposed Solutions (PS).

Keywords

• Vertebra identification
• scoliosis detection
• CA measurement
• DL
• convolutional neural network (CNN)