Deep learning-based automated detection of the dental crown finish line: An accuracy study

Statement of problem The marginal fit of dental prostheses is a clinically significant issue, and dental computer-aided design software programs use automated methods to expedite the extraction of finish lines. The accuracy of these automated methods should be evaluated. Purpose The purpose of this study was to compare the accuracy of a new hybrid method with existing software programs that extract finish lines using fully automated and semiautomated methods. Material and methods A total of 182 jaw scans containing at least 1 natural tooth abutment were collected and divided into 2 groups depending on how the digital data were created. Group DS used desktop scanners to scan casts trimmed for improved finish line visibility, while Group IS used intraoral scans. The method from Dentbird was compared using 3 software packages from 3Shape, exocad, and MEDIT. The Hausdorff and Chamfer distances were used in this study. Three dental laboratory technicians experienced in the digital workflow evaluated clinical finish line acceptance and its Hausdorff and Chamfer distances. For statistical analysis, t tests were performed after the outliers had been removed using the Tukey interquartile range method (α=.05). Results Outliers identified by using the Tukey interquartile range method were more numerous in the semiautomatic methods than in the automatic methods. When considering data without outliers, the software performance was found to be similar for desktop scans of the trimmed casts. However, the method from Dentbird demonstrated statistically better results (P<.05) for the posterior tooth with finish lines in concave regions than the 3Shape, exocad, and MEDIT software programs. Furthermore, thresholds coherent with clinical acceptance were determined for the Hausdorff and Chamfer distances. The Hausdorff distance threshold was 0.366 mm for desktop scans and 0.566 mm for intraoral scans. For the Chamfer distance, the threshold was 0.026 for desktop scans and 0.100 for intraoral scans. Conclusions The method from Dentbird demonstrated a comparable or better performance than the other software solutions, particularly excelling in finish line extraction for intraoral scans. Using a hybrid method combining deep learning and computer-aided design approaches enables the robust and accurate extraction of finish lines.