Teeth and prostheses detection in dental panoramic X-rays using CNN-based object detector and a priori knowledge-based algorithm.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

Tooth*/diagnostic imaging ; Artificial Limbs*; Humans ; X-Rays ; Algorithms

Deep learning techniques for automatically detecting teeth in dental X-rays have gained popularity, providing valuable assistance to healthcare professionals. However, teeth detection in X-ray images is often hindered by alterations in tooth appearance caused by dental prostheses. To address this challenge, our paper proposes a novel method for teeth detection and numbering in dental panoramic X-rays, leveraging two separate CNN-based object detectors, namely YOLOv7, for detecting teeth and prostheses, alongside an optimization algorithm to refine the outcomes. The study utilizes a dataset of 3138 radiographs, of which 2553 images contain prostheses, to build a robust model. The tooth and prosthesis detection algorithms perform excellently, achieving mean average precisions of 0.982 and 0.983, respectively. Additionally, the trained tooth detection model is verified using an external dataset, and six-fold cross-validation is conducted to demonstrate the proposed method's feasibility and robustness. Moreover, the investigation of performance improvement resulting from the inclusion of prosthesis information in the teeth detection process reveals a marginal increase in the average F1-score, rising from 0.985 to 0.987 compared to the sole teeth detection method. The proposed method is unique in its approach to numbering teeth as it incorporates prosthesis information and considers complete restorations such as dental implants and dentures of fixed bridges during the teeth enumeration process, which follows the universal tooth numbering system. These advancements hold promise for automating dental charting processes.
(© 2023. Springer Nature Limited.)

Int J Legal Med. 2000;113(4):236-9. (PMID: 10929240)
Oral Surg Oral Med Oral Pathol Oral Radiol. 2020 Oct;130(4):464-469. (PMID: 32507560)
J Clin Med. 2020 Feb 01;9(2):. (PMID: 32024114)
Sensors (Basel). 2021 Apr 29;21(9):. (PMID: 33946991)
Sci Rep. 2020 May 5;10(1):7531. (PMID: 32372049)
Sci Rep. 2019 Mar 7;9(1):3840. (PMID: 30846758)
Nat Biomed Eng. 2018 Mar;2(3):158-164. (PMID: 31015713)
BMC Med Imaging. 2021 Aug 13;21(1):124. (PMID: 34388975)
Dentomaxillofac Radiol. 2019 May;48(4):20180051. (PMID: 30835551)
Dent Pract (Ewell). 1971;3(4):6 passim. (PMID: 5281811)
Sci Rep. 2020 Jun 9;10(1):9297. (PMID: 32518413)
J Dent Res. 2019 Oct;98(11):1227-1233. (PMID: 31449759)
Indian J Dent Res. 2013 May-Jun;24(3):395-6. (PMID: 24025897)
Clin Oral Investig. 2021 Apr;25(4):2257-2267. (PMID: 32844259)
J Med Imaging (Bellingham). 2022 May;9(3):034503. (PMID: 35756973)
Sci Rep. 2020 Nov 6;10(1):19261. (PMID: 33159125)
J Dent Res. 2022 Feb;101(2):158-165. (PMID: 34416824)
J Dent. 2019 Dec;91:103226. (PMID: 31704386)
J Dent. 1999 Nov;27(8):565-71. (PMID: 10528974)
Sci Rep. 2021 Jun 11;11(1):12342. (PMID: 34117279)
Periodontol 2000. 2022 Feb;88(1):130-144. (PMID: 35103329)
Sci Rep. 2019 Jun 11;9(1):8495. (PMID: 31186466)
Artif Intell Med. 2021 Jan;111:101996. (PMID: 33461689)
Radiol Phys Technol. 2020 Mar;13(1):6-19. (PMID: 31898014)
J Adv Prosthodont. 2013 Feb;5(1):9-15. (PMID: 23508203)
Sci Rep. 2021 Feb 3;11(1):2885. (PMID: 33536451)

Date Created: 20231002 Date Completed: 20231004 Latest Revision: 20231121

20250114

PMC10545749

10.1038/s41598-023-43591-z

37783773