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

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Küçük Resim

Tarih

2023

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Korean Acad Oral & Maxillofacial Radiology

Erişim Hakkı

info:eu-repo/semantics/openAccess

Özet

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. Materials 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.

Açıklama

Anahtar Kelimeler

Artificial Intelligence, Radiography, Panoramic, Deep Learning, Dentistry, Convolutional Neural-Networks

Kaynak

Imaging Science In Dentistry

WoS Q Değeri

Q3

Scopus Q Değeri

Q2

Cilt

53

Sayı

3

Künye

Orhan, K., Belgin, C. A., Manulis, D., Golitsyna, M., Bayrak, S., Aksoy, S., ... & Shlenskii, V. (2023). Determining the reliability of diagnosis and treatment using artificial intelligence software with panoramic radiographs. Imaging Science in Dentistry, 53(3), 199.