Impact of advanced noise reduction algorithms on the diagnostic accuracy of vertical root fractures in cone-beam computed tomography: An evaluation of the influence of intracanal post types
10.5624/isd.20250122
- Author:
Pouya HASSANI
;
Mohammad Mahdi MALEKI
;
Abbas SHOKRI
;
Foozie ZAHEDI
;
Leili TAPAK
- Publication Type:Original Articles
- From:Imaging Science in Dentistry
2026;56(1):11-19
- CountryRepublic of Korea
- Language:English
-
Abstract:
Purpose:Vertical root fractures (VRFs) remain a major diagnostic challenge in endodontics due to image noise and artifacts in cone-beam computed tomography (CBCT), particularly those caused by intracanal posts. This study evaluated the effect of an advanced noise reduction (ANR) algorithm on CBCT performance in detecting VRFs inmaxillary second premolars and examined how different post materials influenced diagnostic outcomes.
Materials and Methods:Seventy extracted maxillary second premolars with single canals were divided into 5groups according to post material (cast, fiberglass, titanium, stainless steel, and brass). VRFs were induced in half of the specimens using a universal testing machine under standardized conditions. CBCT scans were obtainedusing a Carestream 9600 device with fixed parameters, both with and without ANR. Two experienced radiologists independently evaluated the images using a 5-point scale. Sensitivity, specificity, predictive values, and interobserveragreement were analyzed using SPSS version 21 (IBM Corp., Armonk, NY, USA).
Results:The application of ANR increased overall sensitivity and interobserver agreement compared with conventional images. Specificity varied by post material: fiberglass posts demonstrated the highest diagnostic accuracy, while stainless steel and brass produced stronger artifacts and lower sensitivity.
Conclusion:Incorporating ANR into CBCT imaging improves VRF detection by improving sensitivity and observerconsistency, especially in cases with minimal metallic interference. These findings highlight the clinical benefits of ANR and support further research integrating noise and metal artifact reduction techniques with artificial intelligenceto optimize diagnostic precision.
