Background/Objectives: Periapical lesions (PLs) are frequently detected in dental radiology. Accurate diagnosis of these is essential for proper treatment planning. Imaging techniques such as orthopantomogram (OPG) and cone-beam CT (CBCT) imaging used to identify PLs. The aim this study was assess the diagnostic accuracy artificial intelligence (AI) software Diagnocat PL detection OPG CBCT images. Methods: included 49 patients, totaling 1223 teeth. Both images were analyzed by AI three...

Abstract 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 reconstruction model (DLM) iterative reconstructions (IR). CT scans 28 post EVAR patients were enrolled. The s delayed phase DECTA was evaluated. Objective [noise, contrast-to-noise ratio (CNR), signal-to-noise (SNR)] subjective (overall endoleak conspicuity – 3 blinded readers assessment) analyses...

Background: Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize and improve CBCT clarity. This study compares standard learning-enhanced images for quality detecting osteoarthritis-related degeneration TMJs (temporomandibular joints). analyzed of patients with suspected...

Background/Objectives: The purpose of this preliminary study was to evaluate the diagnostic performance an AI-driven platform, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA), for assessing endodontic treatment outcomes using panoramic radiographs (PANs). Materials and Methods: included 55 PAN images patients (15 males 40 females, aged 12–70) who underwent imaging at a private dental center. All were acquired Hyperion X9 PRO digital cephalometer evaluated Diagnocat, cloud-based AI...

Evaluation of the diagnostic value linearly blended (LB) and virtual monoenergetic images (VMI) reconstruction techniques with without metal artifacts reduction (MAR) adaptive statistical iterative reconstructions (ASIR) in assessment target vessels after branched/fenestrated endovascular aortic repair (f/brEVAR) procedures.CT scans 28 patients were used this study. Arterial phase examination was obtained using a dual-energy fast-kVp switching scanner. CT numbers aorta, celiac trunk,...

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 high sensitivity and specificity post-EVAR complication detection, CT angiography (CTA) reference imaging surveillance patients after EVAR. Many studies have shown advantages dual-energy (DECT) over standard polyenergetic CTA vascular applications. In this article, authors briefly discuss technical...

The nasal septum is believed to play a crucial role in the development of craniofacial skeleton. Nasal deviation (NSD) common condition, affecting 18-65% individuals. This study aimed assess prevalence NSD and its potential association with abnormalities detected through cephalometric analysis using artificial intelligence (AI) algorithms. included CT scans 120 consecutive, post-traumatic patients aged 18-30. Cephalometric was performed an AI web-based software, CephX. automatic comprised...

Background/Objectives: 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 dental implants. predictability implant relies on suitability both hard and soft tissue quality. aim this article present case report demonstrating secure protocol for procedures with requiring all teeth, management targeted at increasing keratinized mucosa zone, provision reliable...

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

Abstract 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 scans from patients aged 18-30 years. degree of was measured based on AI platform-predefined anatomical landmarks: sella (S), condylion (Co), anterior nasal spine (ANS), menton (Me). concordance between the results automatic reports linear 3D calculated. rate (AR) indicator determined both...

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

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

Purpose The aim of this study was to evaluate the diagnostic accuracy an artificial intelligence (AI) tool in detecting endoleaks patients undergoing endovascular aneurysm repair (EVAR) using dual-energy computed tomography angiography (CTA). Material and methods involved 95 who underwent EVAR subsequent CTA follow-up. Dual-energy scans were performed, images reconstructed as linearly blended (LB) 40 keV virtual mono­energetic (VMI) images. AI PRAEVAorta®2 used assess arterial phase for...

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

Background: This study evaluates the diagnostic accuracy of an AI-assisted tool in assessing proximity mandibular canal (MC) to root apices (RAs) teeth using computed tomography (CT). Methods: involved 57 patients aged 18–30 whose CT scans were analyzed by both AI and human experts. The primary aim was measure closest distance between MC RAs assess tool’s performance. results indicated significant variability RA-MC distances, with third molars showing smallest mean distances first greatest....

Background: Cephalometric analysis (CA) is an indispensable diagnostic tool in orthodontics for treatment planning and outcome assessment. Manual CA time-consuming prone to variability. Methods: This study aims compare the accuracy repeatability of results among three commercial AI-driven programs: CephX, WebCeph, AudaxCeph. involved a retrospective lateral cephalograms from single orthodontic center. Automated was performed using AI programs, focusing on common parameters defined by Downs,...

The advent of AI in medicine has transformed various medical specialties, including orthodontics. shown promising results enhancing the accuracy diagnoses, treatment planning, and predicting outcomes. With growing number applications commercially available tools, there is an increase their usage orthodontic practices worldwide. This review aims to explore principles artificial intelligence (AI), its diagnostic process modern practices, concerns associated with implementation algorithms...

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 CBCT scans 93 patients (41 males 52 females, mean age 41.2 years, SD 15.8 years) from single center using inclusion criteria standard radiation dose protocol images. Objective subjective assessed three predefined landmarks through...

<title>Abstract</title> 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. A retrospective study was conducted CBCT scans patients from single center, using inclusion criteria standard radiation dose protocol images. Objective assessed through contrast-to-noise ratio (CNR) measurements. Subjective evaluated by two experienced readers five-point scale. The...

Abstract 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 reconstruction model (DLM) adaptive statistical iterative reconstructions (ASIR). CT scans 28 post EVAR patients were enrolled. The s delayed phase DECTA was evaluated. Objective (noise, contrast-to-noise ratio (CNR), signal-to-noise (SNR)) subjective (overall endoleak conspicuity – 3 blinded readers...