A Critical Analysis on Vertebra Identification and Cobb Angle Estimation Using Deep Learning for Scoliosis Detection
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 calculatin...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10399479/ |
| _version_ | 1797348035309600768 |
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| author | Rakesh Kumar Meenu Gupta Ajith Abraham |
| author_facet | Rakesh Kumar Meenu Gupta Ajith Abraham |
| author_sort | Rakesh Kumar |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-08T11:57:39Z |
| format | Article |
| id | doaj.art-87eea05a7dc246c5a662d3a6e4d55abc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T11:57:39Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-87eea05a7dc246c5a662d3a6e4d55abc2024-01-24T00:00:46ZengIEEEIEEE Access2169-35362024-01-0112111701118410.1109/ACCESS.2024.335379410399479A Critical Analysis on Vertebra Identification and Cobb Angle Estimation Using Deep Learning for Scoliosis DetectionRakesh Kumar0https://orcid.org/0000-0002-2659-5941Meenu Gupta1https://orcid.org/0000-0001-7366-0841Ajith Abraham2https://orcid.org/0000-0002-0169-6738Department of Computer Science and Engineering, Chandigarh University, Chandigarh, Punjab, IndiaDepartment of Computer Science and Engineering, Chandigarh University, Chandigarh, Punjab, IndiaSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, IndiaScoliosis 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).https://ieeexplore.ieee.org/document/10399479/Vertebra identificationscoliosis detectionCA measurementDLconvolutional neural network (CNN) |
| spellingShingle | Rakesh Kumar Meenu Gupta Ajith Abraham A Critical Analysis on Vertebra Identification and Cobb Angle Estimation Using Deep Learning for Scoliosis Detection IEEE Access Vertebra identification scoliosis detection CA measurement DL convolutional neural network (CNN) |
| title | A Critical Analysis on Vertebra Identification and Cobb Angle Estimation Using Deep Learning for Scoliosis Detection |
| title_full | A Critical Analysis on Vertebra Identification and Cobb Angle Estimation Using Deep Learning for Scoliosis Detection |
| title_fullStr | A Critical Analysis on Vertebra Identification and Cobb Angle Estimation Using Deep Learning for Scoliosis Detection |
| title_full_unstemmed | A Critical Analysis on Vertebra Identification and Cobb Angle Estimation Using Deep Learning for Scoliosis Detection |
| title_short | A Critical Analysis on Vertebra Identification and Cobb Angle Estimation Using Deep Learning for Scoliosis Detection |
| title_sort | critical analysis on vertebra identification and cobb angle estimation using deep learning for scoliosis detection |
| topic | Vertebra identification scoliosis detection CA measurement DL convolutional neural network (CNN) |
| url | https://ieeexplore.ieee.org/document/10399479/ |
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