Abstract
ObjectivesAnatomical structure classification is necessary task in medical field, but the inevitable variability of interpretation among experts makes reliable classification difficult. This study aims to introduce cluster analysis, unsupervised machine learning method, for classification of three-dimensional (3D) mandibular canal (MC) courses, and to visualize standard MC courses derived from cluster analysis in the Korean population.Materials and methodsA total of 429 cone-beam computed tomography images were used. Four sites in the mandible were selected for the measurement of the MC course and four parameters, two vertical and two horizontal parameters were measured per site. Cluster analysis was carried out as follows: parameter measurement, parameter normalization, cluster tendency evaluation, optimal number of clusters determination, and k-means cluster analysis. The 3D MC courses were classified into three types with statistically significant mean differences by cluster analysis.ResultsCluster 1 showed a smooth line running towards the lingual side in the axial view and a steep slope in the sagittal view. Cluster 2 ran in an almost straight line closest to the lingual and inferior border of mandible. Cluster 3 showed the pathway with a bent buccally in the axial view and an increasing slope in the sagittal view in the posterior area. Cluster 2 showed the highest distribution (42.1%), and males were more widely distributed (57.1%) than the females (42.9%). Cluster 3 comprised similar ratio of male and female cases and accounted for 31.9% of the total distribution. Cluster 1 had the least distribution (26.0%) Distributions of the right and left sides did not show a statistically significant difference.ConclusionThe MC courses were automatically classified as three types through cluster analysis. Cluster analysis enables the unbiased classification of the anatomical structures by reducing observer variability and can present representative standard information for each classified group.
Highlights
Assessing the course of the mandibular canal (MC) is one of the major considerations for dentists in that the MC contains important anatomical structure, inferior alveolar neurovascular bundle, that provides sensation and blood to the mandible [1, 2]
The MC courses were automatically classified as three types through cluster analysis
Cluster analysis enables the unbiased classification of the anatomical structures by reducing
Summary
Assessing the course of the mandibular canal (MC) is one of the major considerations for dentists in that the MC contains important anatomical structure, inferior alveolar neurovascular bundle, that provides sensation and blood to the mandible [1, 2]. The number of the MC courses reported in previous studies has inconsistency with two to four types This discrepancy may be due to the inevitable variability among researchers when interpreting and classifying anatomical structures [15]. Previous studies have been analyzed the location of the MC using two-dimensional (2D) panoramic image, or even if they used CBCT, the MC course was derived in one view such as horizontal or vertical. To overcome these shortcomings, it is necessary to introduce a new approach that enables objective classification of the 3D anatomical structure without observer bias
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