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

Background/Objectives: 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. The aim of this study was to assess the diagnostic accuracy of artificial intelligence (AI) software Diagnocat for PL detection in OPG and CBCT images. Methods: The study included 49 patients, totaling 1223 teeth. Both OPG and CBCT images were analyzed by AI software and by three experienced clinicians. All the images were obtained in one patient cohort, and findings were compared to the consensus of human readers using CBCT. The AI's diagnostic accuracy was compared to a reference method, calculating sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Results: The AI's sensitivity for OPG images was 33.33% with an F1 score of 32.73%. For CBCT images, the AI's sensitivity was 77.78% with an F1 score of 84.00%. The AI's specificity was over 98% for both OPG and CBCT images. Conclusions: The AI demonstrated high sensitivity and high specificity in detecting PLs in CBCT images but lower sensitivity in OPG images.

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

Background: 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. This study compares standard and deep learning-enhanced CBCT images for image quality in detecting osteoarthritis-related degeneration in TMJs (temporomandibular joints). This study analyzed CBCT images of patients with suspected temporomandibular joint degenerative joint disease (TMJ DJD). Methods: The DLM reconstructions were performed with ClariCT.AI software. Image quality was evaluated objectively via CNR in target areas and subjectively by two experts using a five-point scale. Both readers also assessed TMJ DJD lesions. The study involved 50 patients with a mean age of 28.29 years. Results: Objective analysis revealed a significantly better image quality in DLM reconstructions (CNR levels; p < 0.001). Subjective assessment showed high inter-reader agreement (κ = 0.805) but no significant difference in image quality between the reconstruction types (p = 0.055). Lesion counts were not significantly correlated with the reconstruction type (p > 0.05). Conclusions: The analyzed DLM reconstruction notably enhanced the objective image quality in TMJ CBCT images but did not significantly alter the subjective quality or DJD lesion diagnosis. However, the readers favored DLM images, indicating the potential for better TMD diagnosis with CBCT, meriting more study.

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. Objective [noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR)] and subjective (overall image quality and endoleak conspicuity - 3 blinded readers assessment) image quality analyses were performed. The following reconstructions were evaluated: VMI 40, 60 keV VMI; IR VMI 40, 60 keV; DLM VMI 40, 60 keV. The noise level of the DLM VMI images was approximately 50% lower than that of VMI reconstruction. The highest CNR and SNR values were measured in VMI DLM images. The mean CNR in endoleak in 40 keV was accounted for as 1.83 ± 1.2; 2.07 ± 2.02; 3.6 ± 3.26 in VMI, VMI IR, and VMI DLM, respectively. The DLM algorithm significantly reduced noise and increased lesion conspicuity, resulting in higher objective and subjective image quality compared to other reconstruction techniques. The application of DLM algorithms to low-energy VMIs significantly enhances the diagnostic value of DECTA in evaluating endoleaks. DLM reconstructions surpass traditional VMIs and IR in terms of image quality.

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. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.

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). The concordance between the results of automatic asymmetry reports and manual linear 3D measurements was calculated. The asymmetry rate (AR) indicator was determined for both automatic and manual measurements, and the concordance between them was calculated. The repeatability of manual measurements in 20 randomly selected subjects was assessed. The concordance of measurements of quantitative variables was assessed with interclass correlation coefficient (ICC) according to the Shrout and Fleiss classification. RESULTS: Erroneous AI tracings were found in 16.8% of cases, reducing the analysed cases to 79. The agreement between automatic and manual asymmetry measurements was very low (ICC < 0.3). A lack of agreement between AI and manual AR analysis (ICC type 3 = 0) was found. The repeatability of manual measurements and AR calculations showed excellent correlation (ICC type 2 > 0.947). CONCLUSIONS: The results indicate that the rate of tracing errors and lack of agreement with manual AR analysis make it impossible to use the tested AI platform to assess the degree of facial asymmetry.

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. Many studies have shown the advantages of dual-energy CT (DECT) over standard polyenergetic CTA in vascular applications. In this article, the authors briefly discuss the technical principles and summarize the current body of literature regarding dual-energy computed tomography angiography (DECTA) in patients after EVAR. The authors point out the most useful applications of DECTA in this group of patients and its advantages over conventional CTA. To conduct this review, a search was performed using the PubMed, Google Scholar, and Web of Science databases.

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. CT numbers in the aorta, celiac trunk, superior mesenteric artery, and renal arteries were measured in the following reconstructions: LB, VMI 60 keV, VMI MAR 60 keV, VMI ASIR 60 % 60 keV. Contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were calculated for each reconstruction. Luminal diameters (measurements at 2 levels of stent) and subjective image quality (5-point Likert scale) were assessed (2 readers, blinded to the type of reconstruction). Results: The highest mean values of CNR and SNR in vascular structures were obtained in VMI MAR 60 keV (CNR 12.526 ± 2.46, SNR 17.398 ± 2.52), lower in VMI 60 keV (CNR 11.508 ± 2.01, SNR 16.524 ± 2.07) and VMI ASIR (CNR 11.086 ± 1.78, SNR 15.928 ± 1.82), and the lowest in LB (CNR 6.808 ± 0.79, SNR 11.492 ± 0.79) reconstructions. There were no statistically significant differences in the measurements of the stent width between reconstructions (p > 0.05). The highest subjective image quality was obtained in the ASIR VMI (4.25 ± 0.44) and the lowest in the MAR VMI (1.57 ± 0.5) reconstruction. Conclusion: Despite obtaining the highest values of SNR and CNR in the MAR VMI reconstruction, the subjective diagnostic value was the lowest for this technique due to significant artifacts. The type of reconstruction did not significantly affect vessel diameter measurements (p > 0.05). Iterative reconstructions raised both objective and subjective image quality.

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 study included CT scans of 120 consecutive, post-traumatic patients aged 18-30. Cephalometric analysis was performed using an AI web-based software, CephX. The automatic analysis comprised all the available cephalometric analyses. NSD was assessed using two methods: maximum deviation from an ideal non-deviated septum and septal deviation angle (SDA). The concordance of repeated manual measurements and automatic analyses was assessed. Of the 120 cases, 90 met the inclusion criteria. The AI-based cephalometric analysis provided comprehensive reports with over 100 measurements. Only the hinge axis angle (HAA) and SDA showed significant (p = 0.039) negative correlations. The rest of the cephalometric analyses showed no correlation with the NSD indicators. The analysis of the agreement between repeated manual measurements and automatic analyses showed good-to-excellent concordance, except in the case of two angular measurements: LI-N-B and Pr-N-A. The CephX AI platform showed high repeatability in automatic cephalometric analyses, demonstrating the reliability of the AI model for most cephalometric analyses.

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. CT numbers of TNC, VNCa, VNCd were analyzed statistically. VNCd images were assessed qualitatively. The mean densities in endoleaks were 46.19 HU in TNC, 51.24 HU in VNCa, 42.24 HU in VNCd. The differences between them were statistically significant (p < 0.05). The mean signal-to-noise ratio (SNR) measured in the aorta and endoleaks was highest in VNCa, lowest in TNC images. No correlation between image noise, the results of qualitative analysis of VNCd, and the degree of subtraction of calcifications was found. Omitting TNC led to mean 6.54 ± 1.63 (SD) mSv (23.28% of total examination) ED reduction. VNC images have a higher SNR compared to TNC images with significant differences in the CT numbers between the TNC and VNC reconstructions. Image noise has no impact on the subjective image quality and the degree of subtraction of calcifications in VNCd images. The findings show a high diagnostic value of VNC images and suggest that VNCd images are optimal in the assessment of endoleaks with possible substantial ED reduction.

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. An initial single-source noncontrast acquisition was followed by two dual-energy acquisitions (arterial and 60 s delayed). Fast-kVp switching scanner was used. VNC images were reconstructed from the delayed phase. First examination session (reference) included a full triphasic study protocol consisting of true noncontrast (TNC) images and two postcontrast phases, the latter ones presented as classical polyenergetic reconstructions. Reading sessions II and III were performed by two independent and blinded readers evaluating VMIs in abbreviated protocols-biphasic (VNC + arterial, delayed phase), monophasic (VNC + delayed phase). The diagnostic accuracy of sessions II and III was calculated. RESULTS: The calculated sensitivity of the biphasic protocol with the use of VMIs in endoleak detection was 100%, with a statistically significant increase in the number of endoleaks detected in comparison with the reference study. The monophasic protocol showed 83.33% sensitivity. The use of abbreviated examination protocols led to a decrease in the mean effective dose (ED) of 23.28% (biphasic protocol) and 61.37% (monophasic protocol). CONCLUSION: The use of VMIs increases the number of endoleaks diagnosed with a possible radiation reduction by up to » (biphasic protocol). Further reduction to a monophasic protocol leads to over 60% dose reduction but with a decrease in diagnostic accuracy.

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. Endovascular aneurysm repair (EVAR) is a significantly less invasive procedure; it is related to a lower early mortality rate and a lower number of perioperative complications. Although EVAR is a minimally invasive technique, lifelong follow-up imaging is necessary due to possible late complications including endoleak, recurrent aneurysm formation, graft infection, migration, kinking and thrombosis. The total rate of complications after EVAR is estimated at approximately 30%, and the rate of complications that require intervention is 2-3%. Early detection and progression analysis of such situations is crucial for proper intervention.