Tipik Servikal Omurlar Makine Öğrenimi Algoritmaları Kullanılarak Birbirinden Ayırt Edilebilir mi? Radyoanatomik Yeni Belirteçler

Abstract

Amaç: Bu çalışmanın amacı, bilgisayarlı tomografi (BT) görüntülerinde yapılan ölçümlerle makine algoritmaları (ML) kullanılarak çıplak gözle birbirinden ayrılamayan tipik servikal omurları ayırt etmek ve bu omurların farklılıklarını göstermektir. Metod: Bu çalışma 134 (20-55 yaş arası) bireyin 536 tipik servikal vertebra BT görüntüleri incelenerek yapılmıştır. Servikal vertebraların koronal, aksiyal ve sagital kesitlerinde ölçümleri yapıldı. Parametrelerle 6 farklı kombinasyon (Grup 1: C3 – C4, Grup 2: C3 – C5, Grup 3: C3 – C6, Grup 4: C4 – C5, Grup 5: C4 – C6, Grup 6: C5 – C6) oluşturulmuştur. her bir omurun ve ML algoritmalarında analiz edildi. Analiz sonucunda Doğruluk (Acc), Matthews korelasyon katsayısı (Mcc), Özgüllük (Spe), Duyarlılık (Sen) değerleri elde edilmiştir. Bulgular: Bu çalışma sonucunda en yüksek başarı Linear Discriminant Analysis (LDA) ve Logistic Regresyon (LR) algoritmaları ile elde edilmiştir. Grup 3 ve Grup 4'te en yüksek Acc oranı LDA ve LR algoritması ile 0.94, en yüksek Spe değeri Grup 5'te LDA ve LR algoritması ile 0.95, en yüksek Mcc değeri LDA ve LR algoritması ile 0.90 olarak bulundu. Grup 5'te en yüksek Sen değeri, Grup 3 ve 5'te LDA ve LR algoritması ile 0.94 olarak bulundu. Sonuç: Sonuç olarak, tipik servikal vertebraların ML algoritmaları kullanılarak birbirinden net bir şekilde ayırt edilebildiği bulundu.

Keywords

• Tipik servikal omurga
• makine öğrenimi algoritmaları
• bilgisayarlı tomografi

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

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 (ML) with measurements made on computerized tomography (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 (between the ages of 20 and 55) individuals. Measurements of cervical vertebrae were made on coronal, axial and sagittal section. 6 different combinations (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 (Acc), Matthews correlation coefficient (Mcc), Specificity (Spe), Sensitivity (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 (LDA) and Logistic Regression (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

• Typical cervical vertebrae
• machine learning algorithms
• computerized tomography

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