Detecting Prosthetic Restorations Using Artificial Intelligence on Panoramic Radiographs

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Altan B., Gunec H. G., Cinar S., Kutal S., Gulum S., Aydin K.

Scientific Programming, vol.2022, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 2022
  • Publication Date: 2022
  • Doi Number: 10.1155/2022/6384905
  • Journal Name: Scientific Programming
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Istanbul Medipol University Affiliated: Yes

Abstract

Aim. This study applied a CNN (convolutional neural network) algorithm to detect prosthetic restorations on panoramic radiographs and to automatically detect these restorations using deep learning systems. Materials and Methods. This study collected a total of 5126 panoramic radiographs of adult patients. During model training,.bmp,.jpeg, and.png files for images and.txt files containing five different types of information are required for the labels. Herein, 10% of panoramic radiographs were used as a test dataset. Owing to labeling, 2988 crowns and 2969 bridges were formed in the dataset. Results. The mAP and mAR values were obtained when the confidence threshold was set at 0.1. TP, FP, FN, precision, recall, and F1 score values were obtained when the confidence threshold was 0.25. The YOLOv4 model demonstrated that accurate results could be obtained quickly. Bridge results were found to be more successful than crown results. Conclusion. The detection of prosthetic restorations with artificial intelligence on panoramic radiography, which is widely preferred in clinical applications, provides convenience to physicians in terms of diagnosis and time management.