Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Sep;53(3):199-208.
doi: 10.5624/isd.20230109. Epub 2023 Aug 2.

Determining the reliability of diagnosis and treatment using artificial intelligence software with panoramic radiographs

Kaan Orhan  1 , Ceren Aktuna Belgin  2 , David Manulis  3 , Maria Golitsyna  3 , Seval Bayrak  4 , Secil Aksoy  5 , Alex Sanders  3 , Merve Önder  1 , Matvey Ezhov  3 , Mamat Shamshiev  3 , Maxim Gusarev  3 , Vladislav Shlenskii  3
Affiliations

Determining the reliability of diagnosis and treatment using artificial intelligence software with panoramic radiographs

Kaan Orhan et al. Imaging Sci Dent. 2023 Sep.

Abstract

Purpose: The objective of this study was to evaluate the accuracy and effectiveness of an artificial intelligence (AI) program in identifying dental conditions using panoramic radiographs (PRs), as well as to assess the appropriateness of its treatment recommendations.

Material and methods: PRs from 100 patients (representing 4497 teeth) with known clinical examination findings were randomly selected from a university database. Three dentomaxillofacial radiologists and the Diagnocat AI software evaluated these PRs. The evaluations were focused on various dental conditions and treatments, including canal filling, caries, cast post and core, dental calculus, fillings, furcation lesions, implants, lack of interproximal tooth contact, open margins, overhangs, periapical lesions, periodontal bone loss, short fillings, voids in root fillings, overfillings, pontics, root fragments, impacted teeth, artificial crowns, missing teeth, and healthy teeth.

Results: The AI demonstrated almost perfect agreement (exceeding 0.81) in most of the assessments when compared to the ground truth. The sensitivity was very high (above 0.8) for the evaluation of healthy teeth, artificial crowns, dental calculus, missing teeth, fillings, lack of interproximal contact, periodontal bone loss, and implants. However, the sensitivity was low for the assessment of caries, periapical lesions, pontic voids in the root canal, and overhangs.

Conclusion: Despite the limitations of this study, the synthesized data suggest that AI-based decision support systems can serve as a valuable tool in detecting dental conditions, when used with PR for clinical dental applications.

Keywords: Artificial Intelligence; Deep Learning; Dentistry; Radiography, Panoramic.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: The authors have no disclosures to report regarding funding, disclaimer statements, presentations of the research at conferences or symposia, or postings of the work on a preprint server, website, or other location. Kaan Orhan serves as a Scientific Advisor for Diagnocat Inc., based in San Francisco, CA, USA.

Figures

Fig. 1
Fig. 1. A representative panoramic image shows a diagnosis automatically generated by Diagnocat.
Fig. 2
Fig. 2. Image illustrating the predictions made by the trained model, used to define the mouth region as the area of interest within the panoramic radiograph.
Fig. 3
Fig. 3. Mosaic data augmentation teaches the model to recognize objects in different locations, eliminating the need for a single specific context. This method of data augmentation combines numerous training images into a single image, using random proportions.
See this image and copyright information in PMC

References

    1. Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. Radiographics. 2017;37:2113–2131. - PubMed
    1. Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: a systematic review. Dentomaxillofac Radiol. 2020;49:20190107. - PMC - PubMed
    1. Burt JR, Torosdagli N, Khosravan N, RaviPrakash H, Mortazi A, Tissavirasingham F, et al. Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. Br J Radiol. 2018;91:20170545. - PMC - PubMed
    1. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging. 2016;35:1285–1298. - PMC - PubMed
    1. Kim DH, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol. 2018;73:439–445. - PubMed