Aim: To compare the in vivo accuracy of CBCT for the detection of fracture lines versus the diagnosis of vertical root fractures (VRFs) according to characteristic patterns of associated bone resorption.
Methodology: Eighty-eight patients with symptoms typical of VRFs in root filled teeth, who underwent a CBCT examination and later had the teeth extracted, were divided into two groups: the fracture group (n = 65) and the control group (n = 23). Five blinded observers assessed the CBCT images in two sessions. During the first session, they were asked to state the diagnosis according to the CBCT and clinical data. During the second session after 2 weeks, they assessed only axial slices and were asked to detect a fracture line. The mean CBCT specificity, sensitivity, accuracy values and area under the receiver operating characteristic (AUROC) curve were calculated and compared using the Wilcoxon signed-rank test.
Results: The average sensitivity of CBCT for the diagnosis of VRFs was 0.84 ± 0.2. The accuracy and AUC values were 0.81 ± 0.08 and 0.84 ± 0.17, respectively. The sensitivity, accuracy and AUC values for the detection of VRFs were significantly lower: 0.17 ± 0.24 (P = 0.042), 0.54 ± 0.07 (P = 0.043), and 0.52 ± 0.09 (P = 0.043), respectively. The specificity of CBCT for the detection and diagnosis of VRFs did not differ significantly (P = 0.50).
Conclusion: Cone-beam computed tomography was helpful in VRF diagnosis even when it was not possible to visualize the fracture line.
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http://dx.doi.org/10.1111/iej.13114 | DOI Listing |
Dentomaxillofac Radiol
January 2025
Division of Oral Radiology, Faculdade São Leopoldo Mandic.
Objectives: The aim of this technical report was to assess whether the "Radiological Report" tool within the Artificial Intelligence (AI) software Diagnocat can achieve a satisfactory level of performance comparable to that of experienced dentomaxillofacial radiologists in interpreting cone-beam CT scans.
Methods: Ten cone-beam CT scans were carefully selected and analyzed using the AI tool, and they were also evaluated by two dentomaxillofacial radiologists. Observations related to tooth numeration, alterations in dental crowns, roots, and periodontal tissues were documented and subsequently compared to the AI findings.
Dentomaxillofac Radiol
January 2025
Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, Yangsan, 50612, Korea.
Objectives: This study aimed to develop an automated method for generating clearer, well-aligned panoramic views by creating an optimized three-dimensional (3D) reconstruction zone centered on the teeth. The approach focused on achieving high contrast and clarity in key dental features, including tooth roots, morphology, and periapical lesions, by applying a 3D U-Net deep learning model to generate an arch surface and align the panoramic view.
Methods: This retrospective study analyzed anonymized cone-beam CT (CBCT) scans from 312 patients (mean age 40 years; range 10-78; 41.
J Thorac Dis
December 2024
Department of Radiologia d'Urgenza e Interventistica, Dipartimento di Diagnostica per Immagini e Radioterapia Oncologica, Fondazione Policlinico Universitario "A. Gemelli", IRCCS, Rome, Italy.
Background: Sometimes, the identification of ground-glass opacities (GGOs), small or deep pulmonary nodules can be difficult also in expert hands. Usually for these lesions pulmonary lobectomy is an overtreatment, so we developed a technique to identify easily these nodules. The objective of this research is to assess the effectiveness and safety of using preoperative cone beam computed tomography (CBCT) to guide the placement of micro-coils in the lung parenchyma near GGO and small lesions.
View Article and Find Full Text PDFOral Surg Oral Med Oral Pathol Oral Radiol
December 2024
Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA.
Objectives: This study evaluated an automated deep learning method for detecting calcifications in the extracranial and intracranial carotid arteries and vertebral arteries in cone beam computed tomography (CBCT) scans. Additionally, a model utilizing CBCT-derived radiomics imaging biomarkers was evaluated to predict the cardiovascular diseases (CVD) of stroke and heart attack.
Methods: Models were trained using the nn-UNet architecture to identify three locations of arterial calcifications: extracranial carotid calcification (ECC), intracranial carotid calcification (ICC), and vertebral artery calcification (VAC).
Dentomaxillofac Radiol
January 2025
Associate Professor, Division of Oral Diagnostic Sciences, School of Dentistry, Oregon Health & Science University, Portland, OR, USA.
Objectives: To compare a novel photon-counting sensor, two CBCT protocols and two CMOS sensors on the detection of gaps between a gutta-percha cone and root canal walls.
Methods: Twenty-five mandibular incisors were prepared to 45/.04 (size/taper) at working length.
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