TY - JOUR

T1 - The efficiency of artificial intelligence methods for finding radiographic features in different endodontic treatments - a systematic review

AU - Ramezanzade, Shaqayeq

AU - Laurentiu, Tudor

AU - Bakhshandah, Azam

AU - Ibragimov, Bulat

AU - Kvist, Thomas

AU - EndoReCo

AU - Bjørndal, Lars

N1 - Publisher Copyright: © 2022 Acta Odontologica Scandinavica Society.

PY - 2023

Y1 - 2023

N2 - Objectives: To assess the efficiency of AI methods in finding radiographic features in Endodontic treatment considerations. Material and methods: This review was based on the PRISMA guidelines and QUADAS 2 tool. A systematic search was performed of the literature on cases with endodontic treatments, comparing AI algorithms (test) versus conventional image assessments (control) for finding radiographic features. The search was conducted in PubMed, Scopus, Google Scholar and the Cochrane library. Inclusion criteria were studies on the use of AI and machine learning in endodontic treatments using dental X-rays. Results: The initial search retrieved 1131 papers, from which 24 were included. High heterogeneity of the materials left out a meta-analysis.The reported subcategories were periapical lesion, vertical root fractures, predicting root/canal morphology, locating minor apical foramen, tooth segmentation and endodontic retreatment prediction. Radiographic features assessed were mostly periapical lesions. The studies mostly considered the decision of 1–3 experts as the reference for training their models. Almost half of the included materials campared their trained neural network model with other methods. More than 58% of studies had some level of bias. Conclusions: AI-based models have shown effectiveness in finding radiographic features in different endodontic treatments. While the reported accuracy measurements seem promising, the papers mostly were biased methodologically.

AB - Objectives: To assess the efficiency of AI methods in finding radiographic features in Endodontic treatment considerations. Material and methods: This review was based on the PRISMA guidelines and QUADAS 2 tool. A systematic search was performed of the literature on cases with endodontic treatments, comparing AI algorithms (test) versus conventional image assessments (control) for finding radiographic features. The search was conducted in PubMed, Scopus, Google Scholar and the Cochrane library. Inclusion criteria were studies on the use of AI and machine learning in endodontic treatments using dental X-rays. Results: The initial search retrieved 1131 papers, from which 24 were included. High heterogeneity of the materials left out a meta-analysis.The reported subcategories were periapical lesion, vertical root fractures, predicting root/canal morphology, locating minor apical foramen, tooth segmentation and endodontic retreatment prediction. Radiographic features assessed were mostly periapical lesions. The studies mostly considered the decision of 1–3 experts as the reference for training their models. Almost half of the included materials campared their trained neural network model with other methods. More than 58% of studies had some level of bias. Conclusions: AI-based models have shown effectiveness in finding radiographic features in different endodontic treatments. While the reported accuracy measurements seem promising, the papers mostly were biased methodologically.

KW - Artificial intelligence

KW - deep learning

KW - endodontic diagnosis

KW - endodontics

KW - machine learning

U2 - 10.1080/00016357.2022.2158929

DO - 10.1080/00016357.2022.2158929

M3 - Review

C2 - 36548872

AN - SCOPUS:85145169395

VL - 81

SP - 422

EP - 435

JO - Acta Odontologica Scandinavica

JF - Acta Odontologica Scandinavica

SN - 0001-6357

IS - 6

ER -