MIDDLE MESIAL CANAL DETECTION IN MANDIBULAR PERMANENT MOLARS USING MANUAL VERSUS SEMIAUTOMATIC SEGMENTATION OF CBCT SCANS (IN VITRO STUDY)

Abstract

*Background: Technological advancements are improving medical practices in dentistry, leading to favorable changes in dental operations. Cone beam computed tomography (CBCT) is a cutting-edge technique for creating three-dimensional images. It offers a noninvasive way to examine intricate root canal structures. Objectives: This study aimed to assess the accuracy of identifying the middle mesial canal (MMC) in mandibular molars using manual and semiautomated pulpal segmentation of CBCT scans, along with the clearing technique. *Methods: A diagnostic accuracy in vitro study was conducted on 48 extracted first and second mandibular permanent molars obtained from the Oral and Maxillofacial Surgery Department at Alexandria University. The molars were placed in a human cadaver mandible and analyzed to identify the MMC using periapical digital radiography, manual, semiautomated, and automated CBCT segmentation, and the clearing technique (gold standard). Sensitivity, specificity, and accuracy of all methods were collected and volumetric analysis and time were analyzed using repeated measures ANOVA, followed by multiple pairwise comparisons using Bonferroni correction, with a significance level set at P<0.05. *Results: A MMC was detected in two out of the 48 teeth. Digital radiography showed 0% sensitivity and 100% specificity, with an overall accuracy of 95.83%. Manual, semiautomated, and automated CBCT analyses achieved 100% sensitivity and specificity for detecting the two MMC compared to the clearing technique. Manual segmentation took significantly more time (71.56 minutes) compared to semiautomated (22.48 minutes) and automated (0.20 minutes) methods (p < 0.001). No significant differences in volume measurements were noted among the three methods. *Conclusion: CBCT provided greater sensitivity, specificity, and accuracy in identifying MMC compared to digital radiography. Semiautomated and automated segmentation offers significant time saving and accuracy over manual methods.