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Article detail · 2023

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

Imaging Science in Dentistry

YÖKSİS OpenAlex Open access · gold SJR Q2 JCR Q2 Citations 57 Top 1% Percentile 99.2% FWCI 12.85
Year
2023
ISSN
2233-7822
Type
article

Data source split

  • YÖKSİS YÖKSİS article record
  • OpenAlex OpenAlex enrichment (abstract, citations, topics)

Abstract

English (OpenAlex)

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.

Topics

  • Dental Radiography and Imaging
  • Radiomics and Machine Learning in Medical Imaging
  • Dental Research and COVID-19

Primary topic Dental Radiography and Imaging

Authors

  1. KAAN ORHAN ANKARA ÜNİVERSİTESİ
  2. CEREN AKTUNA BELGİN
  3. David Manulis
  4. Maria Golitsyna
  5. SEVAL BAYRAK BOLU ABANT İZZET BAYSAL ÜNİVERSİTESİ
  6. SEÇİL AKSOY
  7. Alex Sanders
  8. MERVE ÖNDER KÖSE
  9. Matyev Ezhov
  10. Mamat Shamsiev
  11. Maxim Gusarev
  12. Vladislav Shlenskii