Marta Dyszkiewicz-Konwińska

Objectives: This study aimed to evaluate the influence of panoramic radiograph quality on the performance of an artificial intelligence (AI)-based tooth detection system and to identify specific image quality criteria associated with detection accuracy. Methods: A total of 424 panoramic radiographs were retrospectively selected from a clinical database. Radiographic quality was assessed using a modified Clinical Image Evaluation Chart, including criteria related to bite block presence, anteroposterior positioning, occlusal plane curvature, patient movement, anatomical coverage, overlapping contact points, air gap, contrast, cervical spine overlap, symmetry of the ascending mandibular ramus, and the number of visible teeth. Automated tooth detection was performed using a convolutional neural network based on the Mask R-CNN architecture (SynbrAIn, Italy). AI detection outputs were validated against expert human evaluation. Spearman’s rank correlation analyses were conducted to assess associations between individual image quality criteria and the number of AI detection errors per radiograph. Results: Significant negative associations were observed between AI detection errors and the number of visible teeth (ρ = −0.311, p < 0.001), presence of a bite block (ρ = −0.248, p < 0.001), reduced patient movement (ρ = −0.204, p < 0.001), correct anteroposterior positioning (ρ = −0.165, p < 0.001), and overall image quality score (ρ = −0.120, p = 0.010). In contrast, the presence of an air gap above the anterior teeth (ρ = 0.099, p = 0.042) and overlapping contact points (ρ = 0.122, p = 0.012) were positively associated with increased detection errors. No significant associations were identified for occlusal plane curvature, contrast, cervical spine overlap, anatomical coverage, or mandibular ramus symmetry. Overall, the AI system was more sensitive to indicators of anatomical completeness and patient positioning than to minor radiographic imperfections. Conclusions: Panoramic radiograph quality, particularly indicators of anatomical completeness and patient positioning, is associated with the performance of AI-based tooth detection. While the AI system demonstrated robustness to common image quality variations, adherence to standardized acquisition protocols remains important to minimize detection errors.

Background/Objectives: Orthognathic surgery represents a surgical modality for the correction of craniofacial skeletal deformities. These procedures help achieve stable occlusion and improve facial symmetry, which in turn enhances functional outcomes and overall quality of life. However, to date, no consensus has been reached regarding whether orthognathic surgery also induces changes in the relationship of articular surfaces within the temporomandibular joints (TMJs). The primary objective of this study was to conduct a systematic review of research evaluating joint space dimensions based on CBCT imaging performed before and after orthognathic surgery. Methods: A comprehensive literature search was carried out across four electronic databases: PubMed, Web of Science, Cochrane Library, and Scopus. Two independent reviewers screened titles and abstracts according to predefined inclusion criteria. Eligible studies were subjected to critical appraisal, and relevant data were systematically extracted and summarized in tabular form. Results: Fourteen studies published between 2010 and 2024 met the inclusion criteria. In all studies, CBCT-based joint space measurements were conducted at least twice once preoperatively and once postoperatively, across a total of 527 patients included in the review. Conclusions: The synthesized evidence suggests that orthognathic surgery produces measurable modifications in the spatial relationship of TMJ articular surfaces. Nonetheless, the clinical relevance of these alterations appears to be modulated by several variables, including the surgical technique employed and the patient’s individual adaptive capacity.

Purpose: This study aims to assess the accuracy of artificial intelligence (AI) in mandibular canal (MC) segmentation on cone-beam computed tomography (CBCT) compared to semi-automatic segmentation. The impact of third molar status (absent, erupted, impacted) on AI performance was also evaluated.Material and methods: A total of 150 CBCT scans (300 MCs) were retrospectively analysed. Semi-automatic MC segmentation was performed by experts using Romexis software, serving as the reference standard. AI-based segmentation was conducted using Diagnocat, an AI-driven cloud-based platform. Three-dimensional segmentation accuracy was assessed by comparing AI and semi-automatic segmentations through surface-to-surface distance metrics in Cloud Compare software. Statistical analyses included the intraclass correlation coefficient (ICC) for inter- and intra-rater reliability, Kruskal-Wallis tests for group comparisons, and Mann-Whitney U tests for post-hoc analyses.Results: The median deviation between AI and semi-automatic MC segmentation was 0.29 mm (SD: 0.25-0.37 mm), with 88% of cases within the clinically acceptable limit (≤ 0.50 mm). Inter-rater reliability for semi-automatic segmentation was 84.5%, while intra-rater reliability reached 95.5%. AI segmentation demonstrated the highest accuracy in scans without third molars (median deviation: 0.27 mm), followed by erupted third molars (0.28 mm) and impacted third molars (0.32 mm).Conclusions: AI demonstrated high accuracy in MC segmentation, closely matching expert-guided semi-automatic segmentation. However, segmentation errors were more frequent in cases with impacted third molars, probably due to anatomical complexity. Further optimisation of AI models using diverse training datasets and multi-centre validation is recommended to enhance reliability in complex cases.

Regenerative processes occur at various levels in all organisms, yet their complexity continues to raise new questions about their mechanisms. It has been demonstrated that small extracellular vesicles (sEVs), secreted by all cells and influencing their function, play a significant role in regeneration. In the context of regenerative processes, oral mucosal tissues consistently receive interest, as they are among the most rapidly healing tissues in the human body. In this study, we utilized spatial transcriptomics to map gene expression to specific spatial locations within the gingiva tissue section, using publicly available transcriptomic data. This analysis revealed new insights into this tissue and the biogenesis of sEVs within it. The identified clusters encompassed two main regions—the epithelium and lamina propria—as well as minor niches within them. Using Gene Ontology (GO) analysis, we identified two clusters most enriched in extracellular vesicle-related GO processes. These included the superficial and deeper layers of the sulcular epithelium, one of the most peripheral regions of the gingiva. Of the 43 genes identified in the literature as having a potential or documented role in sEVs biogenesis, 12 were selected for further analysis. MUC1, SDCBP2, and VPS37B showed clear specificity and the highest expression in the superficial layer of the sulcular epithelium. CHMP4C also exhibited high expression in this layer, though its levels were comparable to the outer layer of the oral epithelium. Other well-established sEVs marker genes, such as ANXA2, CD9, CD63, CD81, FLOT1, RAB22A, RAB27B, and RAB5A, were also expressed in the examined tissue; however, their expression was not specifically exclusive to the sulcular epithelium. Our study is the first to perform a meta-analysis of available gingival transcriptomic data in the specific context of sEVs biogenesis. The presented data and conclusions provide new insights into the role of different structures within healthy human gingiva and shed new light on both known and potential markers of sEVs biogenesis. These findings may contribute to the development of regeneration-targeted research, especially on oral tissues.

The inferior alveolar canal (IAC) is a fundamental mandibular structure. It is important to conduct a precise pre-surgical evaluation of the IAC to prevent complications. Recently, the use of artificial intelligence (AI) has demonstrated potential as a valuable tool for dentists, particularly in the field of oral and maxillofacial radiology. The aim of the study was to compare the segmentation time and accuracy of AI-based IAC segmentation with semi-automatic segmentation performed by a specialist. Thirty individual IACs from 15 anonymized cone-beam computed tomography (CBCT) scans of patients with at least 1 lower third molar were collected from the database of Poznan University of Medical Sciences, Poland. The IACs were segmented by a trainee in the field of oral and maxillofacial radiology using a semi-automatic method and automatically by an AI-based platform (Diagnocat). The resulting segmentations were overlapped with the use of Geomagic Studio, reverse engineering software, and then subjected to a statistical analysis. The AI-based segmentation closely matched the semi-automatic method, with an average deviation of 0.275 ±0.475 mm between the overlapped segmentations. The mean segmentation time for the AI-based method (175.00 s) was similar to that of the semi-automatic method (175.67 s). The results of the study indicate that AI-based tools may offer a reliable approach for the segmentation of the IAC in the context of dental pre-surgical planning. However, further comprehensive studies are required to compare the methods and consider their limitations more comprehensively.

Intracranial calcifications, particularly within the falx cerebri, serve as crucial diagnostic markers ranging from benign accumulations to signs of severe pathologies. The falx cerebri, a dural fold that separates the cerebral hemispheres, presents challenges in visualization due to its low contrast in standard imaging techniques. Recent advancements in artificial intelligence (AI), particularly in machine learning and deep learning, have significantly transformed radiological diagnostics. This study aims to explore the application of AI in the segmentation and detection of falx cerebri calcifications using Cone-Beam Computed Tomography (CBCT) images through a comprehensive literature review and a detailed case report. The case report presents a 59-year-old patient diagnosed with falx cerebri calcifications whose CBCT images were analyzed using a cloud-based AI platform, demonstrating effectiveness in segmenting these calcifications, although challenges persist in distinguishing these from other cranial structures. A specific search strategy was employed to search electronic databases, yielding four studies exploring AI-based segmentation of the falx cerebri. The review detailed various AI models and their accuracy across different imaging modalities in identifying and segmenting falx cerebri calcifications, also highlighting the gap in publications in this area. In conclusion, further research is needed to improve AI-driven methods for accurately identifying and measuring intracranial calcifications. Advancing AI applications in radiology, particularly for detecting falx cerebri calcifications, could significantly enhance diagnostic precision, support disease monitoring, and inform treatment planning.

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 15.8 years) from a single center using the inclusion criteria of standard radiation dose protocol images. Objective and subjective image quality was assessed in three predefined landmarks through contrast-to-noise ratio (CNR) measurements and visual assessment using a 5-point scale by three experienced readers. The inter-reader reliability and repeatability were calculated. Results: Eighty patients (30 males and 50 females; mean age 41.5 years, SD 15.94 years) were included in this study. The CNR in DLM reconstructions was significantly greater than in native reconstructions, and the mean CNR in regions of interest 1-3 (ROI1-3) in DLM images was 11.12 ± 9.29, while in the case of native reconstructions, it was 7.64 ± 4.33 (p < 0.001). The noise level in native reconstructions was significantly higher than in the DLM reconstructions, and the mean noise level in ROI1-3 in native images was 45.83 ± 25.89, while in the case of DLM reconstructions, it was 35.61 ± 24.28 (p < 0.05). Subjective image quality assessment revealed no statistically significant differences between native and DLM reconstructions. Conclusions: The use of deep learning-based image reconstruction algorithms for CBCT imaging of the oral cavity can improve image quality by enhancing the CNR and lowering the noise.

Although ongoing debates persist over the scope of phenomena classified as regenerative processes, the most up-to-date definition of regeneration is the replacement or restoration of damaged or missing cells, tissues, organs, or body parts to full functionality. Despite extensive research on this topic, new methods in regenerative medicine are continually sought, and existing ones are being improved. Small extracellular vesicles (sEVs) have gained attention for their regenerative potential, as evidenced by existing studies conducted by independent research groups. Of particular interest are sEVs derived from the oral mucosa, a tissue renowned for its rapid regeneration and minimal scarring. While the individual regenerative potential of both sEVs and the oral mucosa is somewhat understood, the combined potential of sEVs derived from the oral mucosa has not been sufficiently explored and highlighted in the existing literature. Serving as a broad compendium, it aims to provide scientists with essential and detailed information on this subject, including the nature of the materials employed, isolation and analysis methodologies, and clinical applications. The content of this survey aims to facilitate the comparison of diverse methods for working with sEVs derived from the oral mucosa, aiding in the planning of research endeavors and identifying potential research gaps.

Background/Objectives: 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. Methods: Comprehensive searches were conducted across multiple databases, including PubMed, Scopus, Web of Science, and Embase, using a combination of Boolean operators and MeSH terms. The inclusion criteria were cross-sectional studies with neural network research, reporting diagnostic accuracy, and involving human subjects. Data extraction and quality assessment were performed independently by two reviewers, with a third reviewer resolving any disagreements. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool was used for bias assessment. Results: Eighteen studies met the inclusion criteria, predominantly employing supervised learning techniques, especially convolutional neural networks (CNNs). The diagnostic accuracy of AI models for CVM assessment varied widely, ranging from 57% to 95%. The factors influencing accuracy included the type of AI model, training data, and study methods. Geographic concentration and variability in the experience of radiograph readers also impacted the results. Conclusions: AI has considerable potential for enhancing the accuracy and reliability of CVM assessments in orthodontics. However, the variability in AI performance and the limited number of high-quality studies suggest the need for further research.

Background: 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. Methods: This study aims to compare the accuracy and repeatability of CA results among three commercial AI-driven programs: CephX, WebCeph, and AudaxCeph. This study involved a retrospective analysis of lateral cephalograms from a single orthodontic center. Automated CA was performed using the AI programs, focusing on common parameters defined by Downs, Ricketts, and Steiner. Repeatability was tested through 50 randomly reanalyzed cases by each software. Statistical analyses included intraclass correlation coefficients (ICC3) for agreement and the Friedman test for concordance. Results: One hundred twenty-four cephalograms were analyzed. High agreement between the AI systems was noted for most parameters (ICC3 > 0.9). Notable differences were found in the measurements of angle convexity and the occlusal plane, where discrepancies suggested different methodologies among the programs. Some analyses presented high variability in the results, indicating errors. Repeatability analysis revealed perfect agreement within each program. Conclusions: AI-driven cephalometric analysis tools demonstrate a high potential for reliable and efficient orthodontic assessments, with substantial agreement in repeated analyses. Despite this, the observed discrepancies and high variability in part of analyses underscore the need for standardization across AI platforms and the critical evaluation of automated results by clinicians, particularly in parameters with significant treatment implications.