Effect of Lower Third Molar Segmentations on Automated Tooth Development Staging using a Convolutional Neural Network. Issue 2 (5th September 2019)

• Record Type: Journal Article
• Authors: Merdietio Boedi, Rizky; Banar, Nikolay; De Tobel, Jannick; Bertels, Jeroen; Vandermeulen, Dirk; Thevissen, Patrick Werner
• Is Part Of: Journal of forensic sciences. Volume 65:Issue 2(2020)
• Page Start: 481
• Page End: 486
• Publication Date: 2019-09-05
• Subjects: forensic science -- forensic odontology -- panoramic radiograph -- machine learning -- tooth segmentation -- third molar -- age estimation; Medical jurisprudence -- Periodicals; Forensic sciences -- Periodicals; Forensic Medicine -- Periodicals; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; 363.2505
• DOI: 10.1111/1556-4029.14182
• Languages: English
• ISSNs: 0022-1198

Abstract

Abstract: Staging third molar development is commonly used for age estimation in subadults. Automated developmental stage allocation to the mandibular left third molar in panoramic radiographs has been examined in a pilot study. This method used an AlexNet Deep Convolutional Neural Network (CNN) approach to stage lower left third molars, which had been selected by manually drawn bounding boxes around them. This method (bounding box AlexNet = BA) still contained parts of surrounding structures which may have affected the automated stage allocation performance. We hypothesize that segmenting only the third molar could further improve the automated stage allocation performance. Therefore, the current study aimed to determine and validate the effect of lower third molar segmentations on automated tooth development staging. Retrospectively, 400 panoramic radiographs were collected, processed and segmented in three ways: bounding box (BB), rough (RS), and full (FS) tooth segmentation. A DenseNet201 CNN was used for automated stage allocation. Automated staging results were compared with reference stages – allocated by human observers – overall and per stage. FS rendered the best results with a stage allocation accuracy of 0.61, a mean absolute difference of 0.53 stages and a Cohen's linear κ of 0.84. Misallocated stages were mostly neighboring stages, and DenseNet201 rendered better results than AlexNet by increasing the percentage of correctly allocated stages by 3% (BA compared to BB). FS increased the percentage of correctly allocated stages by 7% compared to BB. In conclusion, full tooth segmentation and a DenseNet CNN optimize automated dental stage allocation for age estimation.