Evaluation of a Vendor-Agnostic Deep Learning Model for Noise Reduction and Image Quality Improvement in Dental CBCT.

Background/objectives: To assess the impact of a vendor-agnostic deep learning model (DLM) on image quality parameters and noise reduction in dental cone-beam computed tomography (CBCT) reconstructions.

Methods: This retrospective study was conducted on CBCT scans of 93 patients (41 males and 52 females, mean age 41.2 years, SD 15.

Assessing the diagnostic accuracy of artificial intelligence in post-endovascular aneurysm repair endoleak detection using dual-energy computed tomography angiography.

Purpose: The aim of this study was to evaluate the diagnostic accuracy of an artificial intelligence (AI) tool in detecting endoleaks in patients undergoing endovascular aneurysm repair (EVAR) using dual-energy computed tomography angiography (CTA).

Material And Methods: The study involved 95 patients who underwent EVAR and subsequent CTA follow-up. Dualenergy scans were performed, and images were reconstructed as linearly blended (LB) and 40 keV virtual monoenergetic (VMI) images.

Endodontic Treatment Outcomes in Cone Beam Computed Tomography Images-Assessment of the Diagnostic Accuracy of AI.

The aim of this study was to assess the diagnostic accuracy of the AI-driven platform Diagnocat for evaluating endodontic treatment outcomes using cone beam computed tomography (CBCT) images. A total of 55 consecutive patients (15 males and 40 females, aged 12-70 years) referred for CBCT imaging were included. CBCT images were analyzed using Diagnocat's AI platform, which assessed parameters such as the probability of filling, adequate obturation, adequate density, overfilling, voids in filling, short filling, and root canal number.

Accuracy of Artificial Intelligence for Cervical Vertebral Maturation Assessment-A Systematic Review.

To systematically review and summarize the existing scientific evidence on the diagnostic performance of artificial intelligence (AI) in assessing cervical vertebral maturation (CVM). This review aimed to evaluate the accuracy and reliability of AI algorithms in comparison to those of experienced clinicians. Comprehensive searches were conducted across multiple databases, including PubMed, Scopus, Web of Science, and Embase, using a combination of Boolean operators and MeSH terms.

Comparison of Three Commercially Available, AI-Driven Cephalometric Analysis Tools in Orthodontics.

Cephalometric analysis (CA) is an indispensable diagnostic tool in orthodontics for treatment planning and outcome assessment. Manual CA is time-consuming and prone to variability. This study aims to compare the accuracy and repeatability of CA results among three commercial AI-driven programs: CephX, WebCeph, and AudaxCeph.

Reliability of the AI-Assisted Assessment of the Proximity of the Root Apices to Mandibular Canal.

: This study evaluates the diagnostic accuracy of an AI-assisted tool in assessing the proximity of the mandibular canal (MC) to the root apices (RAs) of mandibular teeth using computed tomography (CT). : This study involved 57 patients aged 18-30 whose CT scans were analyzed by both AI and human experts. The primary aim was to measure the closest distance between the MC and RAs and to assess the AI tool's diagnostic performance.

The Significance of Keratinized Mucosa in Implant Therapy: Narrative Literature Review and Case Report Presentation.

: Implant treatment in patients who require teeth extraction due to periodontitis presents a significant challenge. The consideration of peri-implantitis is crucial when planning the placement of dental implants. The predictability of implant treatment relies on the suitability of both hard and soft tissue quality.

Evaluating the Diagnostic Accuracy of an AI-Driven Platform for Assessing Endodontic Treatment Outcomes Using Panoramic Radiographs: A Preliminary Study.

The purpose of this preliminary study was to evaluate the diagnostic performance of an AI-driven platform, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA), for assessing endodontic treatment outcomes using panoramic radiographs (PANs). The study included 55 PAN images of 55 patients (15 males and 40 females, aged 12-70) who underwent imaging at a private dental center.

The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging.

Objective: This study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity.

Materials And Methods: This retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns.

Periapical Lesions in Panoramic Radiography and CBCT Imaging-Assessment of AI's Diagnostic Accuracy.

: Periapical lesions (PLs) are frequently detected in dental radiology. Accurate diagnosis of these lesions is essential for proper treatment planning. Imaging techniques such as orthopantomogram (OPG) and cone-beam CT (CBCT) imaging are used to identify PLs.

Noise-Optimized CBCT Imaging of Temporomandibular Joints-The Impact of AI on Image Quality.

Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) is effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize noise and improve CBCT image clarity.

Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions.

To assess the image quality parameters of dual-energy computed tomography angiography (DECTA) 40-, and 60 keV virtual monoenergetic images (VMIs) combined with deep learning-based image reconstruction model (DLM) and iterative reconstructions (IR). CT scans of 28 post EVAR patients were enrolled. The 60 s delayed phase of DECTA was evaluated.

AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review.

The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools.

Skeletal facial asymmetry: reliability of manual and artificial intelligence-driven analysis.

Objectives: To compare artificial intelligence (AI)-driven web-based platform and manual measurements for analysing facial asymmetry in craniofacial CT examinations.

Methods: The study included 95 craniofacial CT scans from patients aged 18-30 years. The degree of asymmetry was measured based on AI platform-predefined anatomical landmarks: sella (S), condylion (Co), anterior nasal spine (ANS), and menton (Me).

Review of Clinical Applications of Dual-Energy CT in Patients after Endovascular Aortic Repair.

Abdominal aortic aneurysms (AAAs) are a significant cause of mortality in developed countries. Endovascular aneurysm repair (EVAR) is currently the leading treatment method for AAAs. Due to the high sensitivity and specificity of post-EVAR complication detection, CT angiography (CTA) is the reference method for imaging surveillance in patients after EVAR.

Correlation Analysis of Nasal Septum Deviation and Results of AI-Driven Automated 3D Cephalometric Analysis.

The nasal septum is believed to play a crucial role in the development of the craniofacial skeleton. Nasal septum deviation (NSD) is a common condition, affecting 18-65% of individuals. This study aimed to assess the prevalence of NSD and its potential association with abnormalities detected through cephalometric analysis using artificial intelligence (AI) algorithms.

The value of metal artifact reduction and iterative algorithms in dual energy CT angiography in patients after complex endovascular aortic aneurysm repair.

Rationale And Objectives: Evaluation of the diagnostic value of linearly blended (LB) and virtual monoenergetic images (VMI) reconstruction techniques with and without metal artifacts reduction (MAR) and of adaptive statistical iterative reconstructions (ASIR) in the assessment of target vessels after branched/fenestrated endovascular aortic repair (f/brEVAR) procedures.

Materials And Methods: CT scans of 28 patients were used in this study. Arterial phase of examination was obtained using a dual-energy fast-kVp switching scanner.

Quality of virtual-non-contrast phases derived from arterial and delayed phases of fast-kVp switching dual-energy CT in patients after endovascular aortic repair.

Objective of this study is: to analyze CT numbers in arteries and endoleaks in true non-contrast (TNC) and virtual non-contrast phases derived from arterial (VNCa) and delayed (VNCd) phases of dual-energy CT (DECT) in patients after endovascular aneurysm repair (EVAR); to assess the impact of image noise on subjective image quality parameters and the degree of subtraction of calcifications; to calculate effective dose (ED) reduction following replacement of TNC with VNC. The study included 97 patients after EVAR procedure. An initial single-energy TNC acquisition was followed by two DECT acquisitions.

Improved Detection of Endoleaks in Virtual Monoenergetic Images in Dual-Energy CT Angiography Following EVAR.

Objectives: The objective of this prospective study was to evaluate the virtual monoenergetic images (VMI) and virtual noncontrast (VNC) phase in the detection of endoleaks after endovascular abdominal aortic repair (EVAR). The potential dose reduction of abbreviated examination protocols was calculated.

Materials And Methods: Ninety-seven patients after the EVAR procedure were enrolled in this study.

Contemporary imaging methods for the follow-up after endovascular abdominal aneurysm repair: a review.

Abdominal aortic aneurysm (AAA) is defined as a localized enlargement of the aortic cross-section where the diameter is greater than 3 cm or more than 50% larger than the diameter in a normal segment. The most important complication of AAA is rupture, which, if untreated, results in mortality rates of up to 90%. Conventional open surgical repair is associated with significant 30-day mortality.