The efficiency of artificial intelligence methods for finding radiographic features in different endodontic treatments - a systematic review
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The efficiency of artificial intelligence methods for finding radiographic features in different endodontic treatments - a systematic review. / Ramezanzade, Shaqayeq; Laurentiu, Tudor; Bakhshandah, Azam; Ibragimov, Bulat; Kvist, Thomas; EndoReCo ; Bjørndal, Lars.
In: Acta Odontologica Scandinavica, Vol. 81, No. 6, 2023, p. 422-435.Research output: Contribution to journal › Review › Research › peer-review
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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 -
ID: 332041793