Can Typical Cervical Vertebrae Be Distinguished From One Another By Using Machine Learning Algorithms? Radioanatomic New Markers

Authors : Deniz ŞENOL, Yusuf SEÇGİN, Şeyma TOY, Serkan ÖNER, Zülal ÖNER

Pages : 210-218

Doi:10.18521/ktd.1177279

View : 31 | Download : 42

Publication Date : 2023-06-22

Article Type : Research Paper

Abstract :Objective: The aim of this study is to distinguish the typical cervical vertebrae that cannot be separated from one another with the naked eye by using machine algorithms insert ignore into journalissuearticles values(ML); with measurements made on computerized tomography insert ignore into journalissuearticles values(CT); images and to show the differences of these vertebrae. Method: This study was conducted by examining the 536 typical cervical vertebrae CT images of 134 insert ignore into journalissuearticles values(between the ages of 20 and 55); individuals. Measurements of cervical vertebrae were made on coronal, axial and sagittal section. 6 different combinations insert ignore into journalissuearticles values(Group 1: C3 – C4, Group 2: C3 – C5, Group 3: C3 – C6, Group 4: C4 – C5, Group 5: C4 – C6, Group 6: C5 – C6); were formed with parameters of each vertebrae and they were analyzed in ML algorithms. Accuracy insert ignore into journalissuearticles values(Acc);, Matthews correlation coefficient insert ignore into journalissuearticles values(Mcc);, Specificity insert ignore into journalissuearticles values(Spe);, Sensitivity insert ignore into journalissuearticles values(Sen); values were obtained as a result of the analysis. Results: As a result of this study, the highest success was obtained with Linear Discriminant Analysis insert ignore into journalissuearticles values(LDA); and Logistic Regression insert ignore into journalissuearticles values(LR); algorithms. The highest Acc rate was found as 0.94 with LDA and LR algorithm in Groups 3 and Group 4, the highest Spe value was found as 0.95 with LDA and LR algorithm in Group 5, the highest Mcc value was found as 0.90 with LDA and LR algorithm in Group 5 and the highest Sen value was found as 0.94 with LDA and LR algorithm in Groups 3 and 5. Conclusion: As a conclusion, it was found that typical cervical vertebrae can be clearly distinguished from one another by using ML algorithms.
Keywords : Tipik servikal omurga, makine öğrenimi algoritmaları, bilgisayarlı tomografi