Version 1
: Received: 11 August 2024 / Approved: 12 August 2024 / Online: 13 August 2024 (10:48:47 CEST)
How to cite:
Khalid, A.; Ahmad Farhan, A.; Zafar, K.; Tamoor, M. Automated Cobb’s Angle Measurement for Scoliosis Diagnosis Using Deep Learning Technique. Preprints2024, 2024080902. https://doi.org/10.20944/preprints202408.0902.v1
Khalid, A.; Ahmad Farhan, A.; Zafar, K.; Tamoor, M. Automated Cobb’s Angle Measurement for Scoliosis Diagnosis Using Deep Learning Technique. Preprints 2024, 2024080902. https://doi.org/10.20944/preprints202408.0902.v1
Khalid, A.; Ahmad Farhan, A.; Zafar, K.; Tamoor, M. Automated Cobb’s Angle Measurement for Scoliosis Diagnosis Using Deep Learning Technique. Preprints2024, 2024080902. https://doi.org/10.20944/preprints202408.0902.v1
APA Style
Khalid, A., Ahmad Farhan, A., Zafar, K., & Tamoor, M. (2024). Automated Cobb’s Angle Measurement for Scoliosis Diagnosis Using Deep Learning Technique. Preprints. https://doi.org/10.20944/preprints202408.0902.v1
Chicago/Turabian Style
Khalid, A., Kashif Zafar and Maria Tamoor. 2024 "Automated Cobb’s Angle Measurement for Scoliosis Diagnosis Using Deep Learning Technique" Preprints. https://doi.org/10.20944/preprints202408.0902.v1
Abstract
BACKGROUND:Scoliosis is a widespread musculoskeletal disorder ofbending and twisting of spine. In this medical ailment, spine curves to the side andeven in severe cases it can twist and can take several bends. For the diagnosis andtreatment of scoliotic patients, the Cobb’s angle is a critical marker of the body’scurvature.OBJECTIVE:There are many researches that have been conducted to automatethe manual measurement of the angle and every investigation has their own limita-tions.METHODS:This paper presents a method for precisely measuring the Cobb’s an-gle using deep learning based techniques. Mainly it comprises of feature enhance-ment of augmented dataset, a bespoke code for landmark estimation on the spine,segmentation model based on the U-Net architecture and a custom code for Cobb’sangle measurement. These measured angles are then compared to the given anglesfor the segmentation on biomedical (X-ray) images.RESULTS:The findings demonstrate that the proposed technique offers an auto-mated and impartial way for precisely measuring this angle with an overall accu-racy of 97% and Root Mean Square Error (RMSE) of 4.99, which can lower thevariability, facilitate early detection, accurate diagnosis, and monitoring of scolio-sis progression. Additionally, it aids in the treatment planning and evaluating treat-ment outcomes.CONTRIBUTION:By leveraging the applications of Cobb’s angle measurement,healthcare professionals can enhance the quality of care and can improve long-termoutcomes for individuals with scoliosis.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
