Effect of a metal artifact reduction algorithm on dehiscence and fenestration detection around zirconia implants with cone beam computed tomography

Bernardo Barbosa Freire; Victor Aquino Wanderley; João Victor Frazão Câmara; Lethycia Almeida Santos; Carolina Ruis Ferrari; Tamara Teodoro Araujo; Israel Chilvarquer

doi:10.1016/j.oooo.2024.02.023

Effect of a metal artifact reduction algorithm on dehiscence and fenestration detection around zirconia implants with cone beam computed tomography

Oral Surg Oral Med Oral Pathol Oral Radiol. 2024 Mar 2:S2212-4403(24)00088-9. doi: 10.1016/j.oooo.2024.02.023. Online ahead of print.

Authors

Bernardo Barbosa Freire¹, Victor Aquino Wanderley², João Victor Frazão Câmara³, Lethycia Almeida Santos⁴, Carolina Ruis Ferrari⁴, Tamara Teodoro Araujo⁴, Israel Chilvarquer¹

Affiliations

¹ Department of Stomatology, School of Dentistry, University of São Paulo, São Paulo, Brazil.
² Department of Clinical and Preventive Dentistry, Federal University of Pernambuco, Recife, Brazil.
³ Clinic of Operative Dentistry, Periodontology and Preventive Dentistry, Saarland University Hospital, Saarland University, Homburg, Saarland, Germany. Electronic address: jvfrazao92@hotmail.com.
⁴ Department of Biological Sciences, Bauru School of Dentistry, University of São Paulo, Bauru, Brazil.

PMID: 38493025
DOI: 10.1016/j.oooo.2024.02.023

Abstract

Objective: To assess the efficacy of the metal artifact reduction algorithm (MARA) of the Cranex 3D cone beam computed tomography (CBCT) device in the detection of peri-implant dehiscence and fenestration around zirconia implants.

Study design: In total, 60 implants were placed in bovine ribs. Dehiscence and fenestration defects were created around the implants, after which 60 CBCT images were obtained with and 60 without activation of MARA. Three radiologists examined the images for the presence of defects. The area under the curve (AUC) from receiver operating characteristic analysis, sensitivity, and specificity were calculated to assess the ability to discriminate the presence vs absence of bone defects. One-way analysis of variance was employed to analyze outcome measures. The significance level was established at 5% (α = 0.05).

Results: AUC values indicated excellent discrimination of dehiscence on images with MARA activation and an excellent to outstanding range of discrimination with MARA deactivation. For fenestration, MARA activation and deactivation both led to outstanding discrimination. Sensitivity and specificity values revealed that activation of MARA was helpful in distinguishing the presence vs. absence of dehiscence, while both MARA conditions were helpful for fenestration. However, there were no statistically significant differences between MARA activation and deactivation for any outcome measure (P >.05).

Conclusion: CBCT is suitable for detecting peri-implant defects, but MARA application does not significantly affect peri-implant dehiscence and fenestration detection.