ArticleBMC oral health2024
Cone beam computed tomography findings as prognostic factors for maxillary sinus membrane perforation during maxillary sinus augmentation.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Letter to the Editor on AI-Driven CBCT Analysis for Surgical Decision by Deng et at.International dental journal · 2026Article
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3 authors.
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Abstract
backgroundMaxillary Schneiderian membrane (SM) perforation is one of the complications of maxillary sinus (MS) augmentation. The aim of the present study was to analyse cone beam computed tomography (CBCT) findings to identify prognostic factors for SM perforation. MATERIALS AND
methods50 CBCT scans were recruit for the analysis, 39 were suitable for analysis and met the inclusion criteria. Retrospective analysis of clinical and radiological CBCT findings of patients who underwent MS augmentation by lateral approach, divided into study group with SM perforation and control group without. Several radiological parameters were included MS lateral wall width, MS floor width, SM width, shape of the SM, level of MS ventilation, presence of septae, presence of blood vessels in the MS wall, the geometrical shape of the MS and sinus floor, presence of polyps and oroantral communication.
resultsA thin MS floor, the presence of a Retention Pseudocyst, the presence of MS septae, a concave sinus floor and a triangular sinus shape are risk factors for perforation of the SM during open MS lift. Statistical significant difference between the groups were found regarding MS floor width.
conclusionBased on the results of this study, it can be concluded that careful analysis of CBCT reveals a number of significant radiological features that can be used to predict potential risks.
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