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Izbor referenčnih območij vpliva na registracijo z umetno inteligenco segmentiranih mandibul, pridobljenig s CBCT

Year: 2025

Type: article

Abstract: Precise registration of sequential 3D datasets is crucial for accurate dimensional analysis. Utilizing the Local Best-Fit (LBF) algorithm and stable Registration Reference Areas (RRAs) facilitates the accurate alignment of 3D surface models. Currently, Cone-beam Computed Tomography (CBCT) and Deep Learn-ing (DL) algorithms are at the forefront for segmenting CBCT scans to monitor morphological changes in the residual alveolar ridge. This study compares the effectiveness of different RRAs in registration sequen-tial 3D surface models of partially edentulous mandibles. DL-assisted software segmented two sequential CBCTs (T0 and T1) from 10 patients, producing sequential 3D mandibular models. These models were aligned using three distinct RRAs: (i) WHOLE, encompassing the entire surface model

Source: DiRROS repository (University of Maribor)

Authors: Leopold, Klemen, Fidler, Aleš, Selmani-Bukleta, Manushaqe, Kuhar, Milan

Language: English

Topic: Dental Radiography and Imaging

Subfield: Oral Surgery

Field: Dentistry

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7112576154

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