Engels, Paula (2023): Automatisierte Erkennung und Kategorisierung von Seitenzahnrestaurationen mit Hilfe von künstlicher Intelligenz. Dissertation, LMU München: Faculty of Medicine |
PDF
Engels_Paula.pdf 422kB |
Abstract
An automated detection and categorization of posterior restoration using CNN could increase the efficiency in dental practice and create a good foundation for further research. The main objective of the present dissertation project was to establish the prerequisites for the development of an AI-based algorithm for the automated detection of posterior restorations and, furthermore, to determine the diagnostic accuracy of the developed AI-algorithm in comparison to the created expert or reference standard. The image set of a total 1,761 clinical photo- graphs was divided into a training set (N=1,407) and an independent test set (N=354). The expert diagnoses served as a reference standard for cyclic train- ing and repeated evaluation of the CNN (ResNeXt-101-32x8d), which was trained by using image augmentation and transfer learning. Further statistical analysis included the calculation of contingency tables, areas under the Receiv- er Operating Characteristic Curve (AUCs) and saliency maps. After completion of the training, the CNN was able to correctly differentiate and recognize poste- rior restorations on clinical photographs with an average value of 97.1 percent. Therefore, it can be stated that the aim to develop an AI-algorithm for automatic categorization and recognition of posterior restorations was successfully com- pleted. Dental diagnostics might be supported by artificial intelligence-based algorithms in the future. To develop an applied AI-assisted evaluation system on clinical images, further research in practicability needs to be done.
Item Type: | Theses (Dissertation, LMU Munich) |
---|---|
Subjects: | 600 Technology, Medicine 600 Technology, Medicine > 610 Medical sciences and medicine |
Faculties: | Faculty of Medicine |
Language: | German |
Date of oral examination: | 25. January 2023 |
1. Referee: | Kühnisch, Jan |
MD5 Checksum of the PDF-file: | 9ec93d25cdeb3e61bf19bac11cfd4ada |
Signature of the printed copy: | 0700/UMD 20968 |
ID Code: | 31414 |
Deposited On: | 06. Mar 2023 15:44 |
Last Modified: | 08. Mar 2023 12:15 |