Introduction: File Fracture is one of the most common problems during root canal treatment which can affect treatment procedure and prognosis, so it is important to diagnose and prevent it. The aim of this study was to evaluate and compare the diagnostic value of cone-beam computed tomography (CBCT) and digital periapical radiography for detection of separated instrument retained inside the canal.
Methods And Materials: Ninety single-rooted extracted human teeth were selected and randomly divided into 3 groups (=30). Group 1, separated file #10 at the 2-mm apical third of the root canal; group 2, separated file #35 at the 2-mm apical third of the root canal; and group 3, without a broken file (control group). The teeth were instrumented to size #30 and were shaped to size #55 and then the canals were obturated up to separated instrument, or the working length for the teeth without a separated instrument, with lateral condensation technique. After that all teeth were placed in dry skull, digital radiography and CBCT was taken. After data collection, data was analyzed using SPSS software by means of sensitivity, specificity, positive and negative predictive values, and frequency tables.
Results: Sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of digital periapical radiography in detection of a fractured file #10 in the canal was 96.7% and 63.3%, 76.7%, 73.1%, 67.6%, 70%, for CBCT, respectively. Sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of digital periapical radiography in detection of a fracture file #35 in the canal was 93.3%, 96.7%, 96.6%, 93.5% and 95%, and 36.7%, 76.7%, 61.1%, 54.8%, 56.66%, for CBCT, respectively.
Conclusion: Sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of digital periapical radiography was better than the CBCT technique in both sizes of broken files.
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http://dx.doi.org/10.22037/iej.v14i1.22590 | DOI Listing |
Dentomaxillofac Radiol
January 2025
Department of Stomatology, Division of Periodontics, School of Dentistry, University of São Paulo, São Paulo, Brazil.
Objectives: This meta-research assessed methodologies used for evaluating peri-implant marginal bone levels on digital periapical radiographs in randomised clinical trials published between 2019 and 2023.
Methods: Articles were searched in four databases. Data on methods for assessing peri-implant marginal bone levels were extracted.
Dent Traumatol
January 2025
Department of Endodontology, Maurice and Gabriela Goldschleger School of Dental Medicine, Tel Aviv University, Tel Aviv, Israel.
Background/aim: To explore transfer learning (TL) techniques for enhancing vertical root fracture (VRF) diagnosis accuracy and to assess the impact of artificial intelligence (AI) on image enhancement for VRF detection on both extracted teeth images and intraoral images taken from patients.
Materials And Methods: A dataset of 378 intraoral periapical radiographs comprising 195 teeth with fractures and 183 teeth without fractures serving as controls was included. DenseNet, ConvNext, Inception121, and MobileNetV2 were employed with model fusion.
J Craniofac Surg
January 2025
Department of Oral Medicine and Pediatric Dentistry, State University of Londrina, Londrina.
Orbital cellulitis happens when the region behind the orbital septum is affected. It consists an urgency because of its potential risks of complications, such as vision loss, cavernous sinus thrombosis, or Lemierre Syndrome. This article reports a case of a subperiosteal and orbital cellulitis, which had a periapical lesion in the left first molar as it´s focus.
View Article and Find Full Text PDFAm J Dent
December 2024
Department of Endodontics, Faculty of Dentistry, Marmara University, Istanbul, Turkey.
Purpose: To investigate the relationship between type 1 diabetes mellitus (T1-DM) and apical periodontitis (AP). The periapical and endodontic conditions of T1-DM individuals were compared with healthy people.
Methods: T1-DM subjects aged 18-45 with good glycemic control (HbA1c < 7) were included in this research.
Acta Odontol Scand
January 2025
Electronic and Department of Electronics and Automation, Tekirdag Namik Kemal University, Tekirdag, Turkey.
Objectives: Approximal caries diagnosis in children is difficult, and artificial intelligence-based research in pediatric dentistry is scarce. To create a convolutional neural network (CNN)-based diagnostic system for the prompt and efficient identification of approximal caries in pediatric patients aged 5-12 years.
Materials And Methods: Pediatric patients' digital periapical radiographic images were collected to create a unique dataset.
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