Radiographic diagnosis of periodontal diseases - Current evidence versus innovations.

Accurate diagnosis of periodontal and peri-implant diseases relies significantly on radiographic examination, especially for assessing alveolar bone levels, bone defect morphology, and bone quality. This narrative review aimed to comprehensively outline the current state-of-the-art in radiographic diagnosis of alveolar bone diseases, covering both two-dimensional (2D) and three-dimensional (3D) modalities. Additionally, this review explores recent technological advances in periodontal imaging diagnosis, focusing on their potential integration into clinical practice. Clinical probing and intraoral radiography, while crucial, encounter limitations in effectively assessing complex periodontal bone defects. Recognizing these challenges, 3D imaging modalities, such as cone beam computed tomography (CBCT), have been explored for a more comprehensive understanding of periodontal structures. The significance of the radiographic assessment approach is evidenced by its ability to offer an objective and standardized means of evaluating hard tissues, reducing variability associated with manual clinical measurements and contributing to a more precise diagnosis of periodontal health. However, clinicians should be aware of challenges related to CBCT imaging assessment, including beam-hardening artifacts generated by the high-density materials present in the field of view, which might affect image quality. Integration of digital technologies, such as artificial intelligence-based tools in intraoral radiography software, the enhances the diagnostic process. The overarching recommendation is a judicious combination of CBCT and digital intraoral radiography for enhanced periodontal bone assessment. Therefore, it is crucial for clinicians to weigh the benefits against the risks associated with higher radiation exposure on a case-by-case basis, prioritizing patient safety and treatment outcomes.

Emergence of artificial intelligence for automating cone-beam computed tomography-derived maxillary sinus imaging tasks. A systematic review.

Cone-beam computed tomography (CBCT) imaging of the maxillary sinus is indispensable for implantologists, offering three-dimensional anatomical visualization, morphological variation detection, and abnormality identification, all critical for diagnostics and treatment planning in digital implant workflows. The following systematic review presented the current evidence pertaining to the use of artificial intelligence (AI) for CBCT-derived maxillary sinus imaging tasks. An electronic search was conducted on PubMed, Web of Science, and Cochrane up until January 2024. Based on the eligibility criteria, 14 articles were included that reported on the use of AI for the automation of CBCT-derived maxillary sinus assessment tasks. The QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool was used to evaluate the risk of bias and applicability concerns. The AI models used were designed to automate tasks such as segmentation, classification, and prediction. Most studies related to automated maxillary sinus segmentation demonstrated high performance. In terms of classification tasks, the highest accuracy was observed for diagnosing sinusitis (99.7%), whereas the lowest accuracy was detected for classifying abnormalities such as fungal balls and chronic rhinosinusitis (83.0%). Regarding implant treatment planning, the classification of automated surgical plans for maxillary sinus floor augmentation based on residual bone height showed high accuracy (97%). Additionally, AI demonstrated high performance in predicting gender and sinus volume. In conclusion, although AI shows promising potential in automating maxillary sinus imaging tasks which could be useful for diagnostic and planning tasks in implantology, there is a need for more diverse datasets to improve the generalizability and clinical relevance of AI models. Future studies are suggested to focus on expanding the datasets, making the AI model's source available, and adhering to standardized AI reporting guidelines.

Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis.

OBJECTIVES: This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and three-dimensional (3D) cone-beam computed tomographic (CBCT) images. SEARCH METHODS: An electronic search was conducted in the following databases: PubMed, Web of Science, Embase, and grey literature with search timeline extending up to January 2024. SELECTION CRITERIA: Studies that employed AI for 2D or 3D cephalometric landmark detection were included. DATA COLLECTION AND ANALYSIS: The selection of studies, data extraction, and quality assessment of the included studies were performed independently by two reviewers. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A meta-analysis was conducted to evaluate the accuracy of the 2D landmarks identification based on both mean radial error and standard error. RESULTS: Following the removal of duplicates, title and abstract screening, and full-text reading, 34 publications were selected. Amongst these, 27 studies evaluated the accuracy of AI-driven automated landmarking on 2D lateral cephalograms, while 7 studies involved 3D-CBCT images. A meta-analysis, based on the success detection rate of landmark placement on 2D images, revealed that the error was below the clinically acceptable threshold of 2 mm (1.39 mm; 95% confidence interval: 0.85-1.92 mm). For 3D images, meta-analysis could not be conducted due to significant heterogeneity amongst the study designs. However, qualitative synthesis indicated that the mean error of landmark detection on 3D images ranged from 1.0 to 5.8 mm. Both automated 2D and 3D landmarking proved to be time-efficient, taking less than 1 min. Most studies exhibited a high risk of bias in data selection (n = 27) and reference standard (n = 29). CONCLUSION: The performance of AI-driven cephalometric landmark detection on both 2D cephalograms and 3D-CBCT images showed potential in terms of accuracy and time efficiency. However, the generalizability and robustness of these AI systems could benefit from further improvement. REGISTRATION: PROSPERO: CRD42022328800.

Optimization of orofacial cleft imaging protocols using device-specific low-dose cone-beam computed tomography.

OBJECTIVE: The aim of this study was to present optimized device-specific low-dose cone-beam computed tomography (CBCT) protocols with sufficient image quality for pre-surgical diagnostics and three-dimensional (3D) modelling of cleft defects. METHODS: Six paediatric skulls were acquired, and an artificial bony cleft was created. A high-resolution CBCT scan acted as a reference standard (Accuitomo 170, Morita, Kyoto, Japan) for comparing eight low-dose protocols of Newtom VGi-evo (QR Verona, Cefla, Verona, Italy), which included Eco and Regular protocols with different field of views (FOVs). Delineation of lamina dura, cementoenamel junction (CEJ), trabecular bone and bony bridge were assessed. A 3D model of the defect was also evaluated. RESULT: The dose area product of low-dose protocols ranged from 31 to 254 mGy*cm2. Despite the dose difference of up to eight times between applied protocols, trabecular bone and CEJ exhibited appropriate image quality in all scans. However, Regular small FOV protocols (5 × 5 and 8 × 5 cm2), for both lamina dura and bony bridge, demonstrated a significant improvement in image quality compared to Eco FOV counterparts. Based on 3D defect analysis, no significant difference existed between low-dose protocols and the reference standard. CONCLUSION: The findings highlight the possibility of achieving a considerable reduction (up to eight times) in the radiation dose using low-dose CBCT protocols while maintaining sufficient image quality for assessing anatomical structures and 3D modelling in cleft cases.

Prediction of wound healing status following dental extraction using Adapted-University of Connecticut osteonecrosis numerical scale: A retrospective study.

Background and Aims: There is a scarcity of evidence concerning the use of a prognostic instrument for predicting normal healing, delayed healing, and medication-related osteonecrosis of the jaw (MRONJ) occurrence following tooth extraction in medically compromised patients. The present study aimed to predict healing outcomes following tooth extraction in medically compromised patients using an Adapted-University of Connecticut osteonecrosis numerical scale (A-UCONNS). Methods: The digital medical records of medically compromised patients were reviewed, who underwent tooth extraction. The A-UCONNS parameters included the initial pathological condition, dental procedures, comorbidities (smoking habits, type and duration of medication, and type of intervention), and administered antiresorptive (AR) medications. Each parameter was assigned a different weight, and the scores were then accumulated and classified into three categories: minimal risk (less than 10), moderate risk (10-15), and significant risk (16 or more). The patient's healing status was categorized as normal healing, delayed healing, or MRONJ. Results: A total of 353 male patients (mean age: 67.4 years) were recruited from a pool of 3977 patients, where 12.46% of patients had delayed wound healing, and 18.69% developed MRONJ. The median A-UCONNS scores for MRONJ were higher based on initial pathology, comorbidity, and AR drugs compared to normal or delayed healing. In addition, a significant relationship existed between A-UCONNS and healing outcomes (p < 0.05), with a unit increase in A-UCONNS associated with 1.347 times higher odds of experiencing MRONJ compared to normal healing. In contrast, a low score was linked to an increased likelihood of normal wound healing. Conclusion: The A-UCONNS could act as a promising tool for predicting wound healing outcomes. It can provide clinicians the ability to pinpoint patients at high risk and allow tailoring of patient-specific strategies for improving healing outcomes following tooth extraction.

Progress of Artificial Intelligence-Driven Solutions for Automated Segmentation of Dental Pulp Space on Cone-Beam Computed Tomography Images. A Systematic Review.

INTRODUCTION: Automated segmentation of 3-dimensional pulp space on cone-beam computed tomography images presents a significant opportunity for enhancing diagnosis, treatment planning, and clinical education in endodontics. The aim of this systematic review was to investigate the performance of artificial intelligence-driven automated pulp space segmentation on cone-beam computed tomography images. METHODS: A comprehensive electronic search was performed using PubMed, Web of Science, and Cochrane databases, up until February 2024. Two independent reviewers participated in the selection of studies, data extraction, and evaluation of the included studies. Any disagreements were resolved by a third reviewer. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess the risk of bias. RESULTS: Thirteen studies that met the eligibility criteria were included. Most studies demonstrated high accuracy in their respective segmentation methods, although there was some variation across different structures (pulp chamber, root canal) and tooth types (single-rooted, multirooted). Automated segmentation showed slightly superior performance for segmenting the pulp chamber compared to the root canal and single-rooted teeth compared to multi-rooted ones. Furthermore, the second mesiobuccal (MB2) canalsegmentation also demonstrated high performance. In terms of time efficiency, the minimum time required for segmentation was 13 seconds. CONCLUSION: Artificial intelligence-driven models demonstrated outstanding performance in pulp space segmentation. Nevertheless, these findings warrant careful interpretation, and their generalizability is limited due to the potential risk and low evidence level arising from inadequately detailed methodologies and inconsistent assessment techniques. In addition, there is room for further improvement, specifically for root canal segmentation and testing of artificial intelligence performance in artifact-induced images.

Deep learning driven segmentation of maxillary impacted canine on cone beam computed tomography images.

The process of creating virtual models of dentomaxillofacial structures through three-dimensional segmentation is a crucial component of most digital dental workflows. This process is typically performed using manual or semi-automated approaches, which can be time-consuming and subject to observer bias. The aim of this study was to train and assess the performance of a convolutional neural network (CNN)-based online cloud platform for automated segmentation of maxillary impacted canine on CBCT image. A total of 100 CBCT images with maxillary canine impactions were randomly allocated into two groups: a training set (n = 50) and a testing set (n = 50). The training set was used to train the CNN model and the testing set was employed to evaluate the model performance. Both tasks were performed on an online cloud-based platform, 'Virtual patient creator' (Relu, Leuven, Belgium). The performance was assessed using voxel- and surface-based comparison between automated and semi-automated ground truth segmentations. In addition, the time required for segmentation was also calculated. The automated tool showed high performance for segmenting impacted canines with a dice similarity coefficient of 0.99 ± 0.02. Moreover, it was 24 times faster than semi-automated approach. The proposed CNN model achieved fast, consistent, and precise segmentation of maxillary impacted canines.

A retrospective longitudinal assessment of artificial intelligence-assisted radiographic prediction of lower third molar eruption.

Prediction of lower third molar eruption is crucial for its timely extraction. Therefore, the primary aim of this study was to investigate the prediction of lower third molar eruption and its uprighting with the assistance of an artificial intelligence (AI) tool. The secondary aim was identifying the incidence of fully erupted lower third molars with hygienic cleansability. In total, 771 patients having two panoramic radiographs were recruited, where the first radiograph was acquired at 8-15 years of age (T1) and the second acquisition was between 16 and 23 years (T2). The predictive model for third molar eruption could not be obtained as few teeth reached full eruption. However, uprighting model at T2 showed that in cases with sufficient retromolar space, an initial angulation of < 32° predicted uprighting. Full eruption was observed for 13.9% of the teeth, and only 1.7% showed hygienic cleansability. The predictions model of third molar uprighting could act as a valuable aid for guiding a clinician with the decision-making process of extracting third molars which fail to erupt in an upright fashion. In addition, a low incidence of fully erupted molars with hygienic cleansability suggest that a clinician might opt for prophylactic extraction.

Risk of healing impairment following tooth extraction in patients administered with antiresorptive and non-antiresorptive polypharmacy.

INTRODUCTION: Lack of evidence existed related to the essential role by which anticancer medications alone or in combination with other polypharmacy would be accountable for wound healing impairment post-dental extraction. The following study was conducted to assess the influence of antiresorptive (AR) and non-antiresorptive (non-AR) drugs and other patient-related risk factors on wound healing status following tooth extraction. MATERIAL AND METHODS: A total of 353 patients (age range: 40-90 years, average age: 67.4 years, clinical and radiological follow-up) were recruited. All the patients were divided into three groups, which included, patients used polypharmacy with non-AR drugs, polypharmacy with a combination of AR + non-AR drugs, and the control group. Based on time of healing, the outcome was defined as, normal healing, delayed healing, and Medication-related osteonecrosis of the jaw (MRONJ). The polypharmacy score was categorized depending on the sum of the number of administered medications. RESULTS: The odds of delayed healing were significantly higher in 80+ years old patients (OR=6.98, 95 %CI:2.45-19.88, p = < 0.001) administered with AR+ non-AR drugs (OR=14.68, 95 %CI:4.67-46.14, p = < 0.001), having a major polypharmacy score (OR= 15.37, 95 %CI:4.83-48.91, p = < 0.001). On the contrary, patient administered with non-AR drugs (OR=11.52, 95 %CI: 4.45-29.83, p = < 0.001) with hyper polypharmacy (OR=58.86, 95 %CI:25.03-138.40, p = < 0.001) were significantly more likely to develop MRONJ. Smoking and extraction sites showed no significant impact on wound healing impairment. DISCUSSION: Wound healing status in patients administered with both non-AR and AR+ non-AR polypharmacy was significantly impaired following tooth extraction. Other risk factors, such as increased age and high polypharmacy scoring, also significantly contributed towards the occurrence of delayed healing and MRONJ.

Non-invasive oral implant position assessment: An ex vivo study using a 3D industrial scan as the reference model to mimic the clinical situation.

AIM: To introduce an objective method to evaluate the accuracy of implant position assessment in partially edentulous patients by comparing different techniques (conventional impression, intraoral scan, CBCT) to a reference 3D model obtained with an industrial scanner, the latter mimicking the clinical situation. MATERIALS AND METHODS: Twenty-nine implants were placed in four human cadaver heads using a fully guided flapless protocol. Implant position was assessed using (a) a conventional impression, (b) an intraoral scan, and (c) CBCT and compared to an industrial scan. Three-dimensional models of intraoral scan body and implant were registered to the arch models and the deviation at implant shoulder, apex, and the angle of deviation were compared to each other as well as to the reference model. RESULTS: The three assessment techniques showed statistically significant deviations (p < .01) from the industrial scan, for all measurements, with no difference between the techniques. The maximum deviation at the implant shoulder was 0.16 mm. At the implant apex this increased to 0.38 mm. The intraoral scan deviated significantly more than the CBCT (0.12 mm, p < .01) and the conventional impression (0.10 mm, p = .02). The maximum implant angle deviation was 1.0°. The intraoral scan deviated more than the conventional impression (0.3°, p = .02). CONCLUSION: All assessment techniques deviated from the reference industrial scan, but the differences were relatively small. Intraoral scans were slightly less accurate than both conventional impressions and CBCT. Depending on the application, however, this inaccuracy may not be clinically relevant.

Morphological characteristics of coronoid process and revisiting definition of coronoid hyperplasia.

The aim of this study was to assess the morphological characteristics of the coronoid process (CP) and define coronoid hyperplasia (CH) using cadaveric mandibles of a Caucasian population. A sample of 151 adult dry cadaveric mandibles (302 CPs) was acquired. Three distances were measured, which included the width, height, and length of CP. The surface area measurements involved area A: above the width distance line; area B: between incisura mandibulae-Alveolar ridge line and width distance line; area C: between distance lines of width and height. Finally, angulations of the CP and gonial angles were identified. Both length and surface area A + B acted as hyperplastic indicators. Based on the selection criteria, a sample of 197 CPs was included. The hooked shape (59%) was most commonly observed. No significant difference existed between left and right sides (p > 0.05). The mean values of length and surface area A + B were 2.2 ± 0.3 cm and 3.3 ± 0.8 cm2, and any values above 2.7 cm (n = 5 CPs- 2.5%) and 5.0 cm2 (n = 9 CPs- 4.6%) were described as hyperplastic, respectively. The presented data could act as quantitative reference for differentiating between normal and hyperplastic conditions.

Bibliometric Analysis of the 100 Most-Cited Articles on Periodontics in the Arab World.

AIM: Periodontics is a branch of dentistry that deals with diseases of the supporting and investing structures of the teeth including the gingiva, cementum, periodontal ligament, and alveolar bone. The current study aimed to scrutinize the bibliometric indicators of the 100 most-cited articles on periodontics contributed by authors affiliated with the Arab countries. RESEARCH METHODOLOGY: The bibliometric research method was used for the 100 most-cited articles retrieved from the Scopus database on May 9, 2023. The keywords periodontitis, periodontology, gingivitis, periodontal, gingiva, and periodontium were inserted, and then the Arab countries were selected from the country's filter. The 100 most-cited articles were accessed. The bibliometric indicators such as periodic distribution of articles, their study design, nature of research, contributed by different Arab countries, international research collaboration, and author's used keywords were analyzed. Microsoft Excel version 16 (Microsoft Corporation, Washington, United States), VOSviewer (Centre for Science and Technology Studies, Leiden University, The Netherlands), and SPSS Statistics version 27 (IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp.) were used for data analysis. RESULTS: A slow progress (4.50%) was found in periodontics literature before 2000 in the Arab countries, but a significant growth (75%) was recorded during the past 10 years (January 1, 2014, to May 9, 2023). The 100 most-cited articles on periodontics by the Arab countries were published from 1995 to 2021, and these articles gained citations with an average of 92.18 citations per articles. Case-control and review studies were the preferred study designs, and periodontology and implantology were the top two subject categories. About one-third of the articles were published in the Journal of Periodontology and the Journal of Clinical Periodontology. Saudi Arabia contributed the highest number of the most-cited articles, followed by Egypt and Jordan, but the articles contributed by the United Arab Emirates were the most influential. The United States and Germany were the top-ranked countries in international research collaboration. CONCLUSION: The findings demonstrate that periodontics research increased remarkably during the past 10 years. Saudi Arabia stands not only on the topmost rank in overall research productivity in the Arab countries but also surpasses the rest of the Arab countries in the 100 most-cited articles. Saudi Arabia contributed 26 articles with the United States, while Jordan contributed the highest ratio of indigenous research.

Are medication-induced salivary changes the culprit of osteonecrosis of the jaw? A systematic review.

Purpose: This systematic review was performed to assess the potential influence of medication-induced salivary changes on the development of medication-related osteonecrosis of the jaw (MRONJ). Methods: An electronic search was conducted using PubMed, Web of Science, Cochrane, and Embase databases for articles published up to June 2023. A risk of bias assessment was performed according to the modified Newcastle-Ottawa Scale (NOS). Due to the heterogeneity of the selected studies in relation to the type of medications and outcomes evaluated, a meta-analysis could not be performed. Results: The initial search revealed 765 studies. Only 10 articles were found to be eligible based on the inclusion criteria that reported on the impact of salivary changes on MRONJ following the administration of different medications. A total of 272 cases of MRONJ (35% women, 32% men, and 32% with no gender reported) with a mean age of 66 years at the time of diagnosis were included. Patients administered with bisphosphonates, steroids, chemotherapy, thalidomide, interferon, and hormone therapy had a significantly higher association between decreased salivary flow and MRONJ occurrence. In addition, bisphosphonates, denosumab, and other bone-modifying agents showed a significantly higher risk of developing MRONJ owing to the changes in salivary microbiome profiles, cytokine profiles, interleukins, hypotaurine, and binding proteins. Conclusion: The reduction in salivary flow and changes in the concentration of salivary proteins were associated with the development of MRONJ. However, due to the availability of limited evidence, the findings of the review should be interpreted with caution. Prospero review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022327645.

Creation of Dimicleft radiological cleft phantom skulls using reversed virtual planning technique.

OBJECTIVES: The aim of this technical report was to develop customized pediatric phantoms for cone-beam computed tomography (CBCT)-related research in cleft patients. METHODS: Six human pediatric skulls (age: 5-10 years) were recruited. A cone-beam computed tomography (CBCT) scan was taken for each skull, followed by virtual modeling through the process of segmentation. An artificial cleft was designed and printed to be applied onto the skull for the creation of an artificial cleft. The skulls were covered with non-radiopaque tape and immersed in melted Mix-D soft tissue equivalent material. The resulting phantoms covered with Mix-D were assessed radiologically by two expert radiologists. These phantoms were referred to as Dimicleft pediatric skull phantoms. RESULTS: Dimicleft phantoms were able to appropriately mimic in vivo circumstances. No gaps existed between Mix-D and bony tissue. Virtual planning allowed the optimal designing of an artificial cleft onto the phantom. The artificially created cleft was suitable to determine the size, location, and extent of the cleft. CONCLUSIONS: Dimicleft phantoms could act as a viable alternative to other commercially available options for assessing image quality and optimizing CBCT protocols in cleft patients for diagnostics and three-dimensional treatment planning.

Deep learning-based segmentation of dental implants on cone-beam computed tomography images: A validation study.

OBJECTIVES: To train and validate a cloud-based convolutional neural network (CNN) model for automated segmentation (AS) of dental implant and attached prosthetic crown on cone-beam computed tomography (CBCT) images. METHODS: A total dataset of 280 maxillomandibular jawbone CBCT scans was acquired from patients who underwent implant placement with or without coronal restoration. The dataset was randomly divided into three subsets: training set (n = 225), validation set (n = 25) and testing set (n = 30). A CNN model was developed and trained using expert-based semi-automated segmentation (SS) of the implant and attached prosthetic crown as the ground truth. The performance of AS was assessed by comparing with SS and manually corrected automated segmentation referred to as refined-automated segmentation (R-AS). Evaluation metrics included timing, voxel-wise comparison based on confusion matrix and 3D surface differences. RESULTS: The average time required for AS was 60 times faster (<30 s) than the SS approach. The CNN model was highly effective in segmenting dental implants both with and without coronal restoration, achieving a high dice similarity coefficient score of 0.92±0.02 and 0.91±0.03, respectively. Moreover, the root mean square deviation values were also found to be low (implant only: 0.08±0.09 mm, implant+restoration: 0.11±0.07 mm) when compared with R-AS, implying high AI segmentation accuracy. CONCLUSIONS: The proposed cloud-based deep learning tool demonstrated high performance and time-efficient segmentation of implants on CBCT images. CLINICAL SIGNIFICANCE: AI-based segmentation of implants and prosthetic crowns can minimize the negative impact of artifacts and enhance the generalizability of creating dental virtual models. Furthermore, incorporating the suggested tool into existing CNN models specialized for segmenting anatomical structures can improve pre-surgical planning for implants and post-operative assessment of peri­implant bone levels.

Autoimmune diseases and orthognathic surgery: A case series of 12 patients.

Autoimmune diseases result from the immune system attacking native cells and tissues due to the recognition of "self" antigens as foreign antigens. This group of disorders is associated with an increased risk of complications after surgical interventions, as the immune system may cause tissue destruction. The study aimed to investigate the risk of surgical complications in patients with autoimmune diseases, who are at a higher risk of complications due to their condition. Among 886 patients who underwent orthognathic surgery, twelve types of autoimmune diseases with 22 patients were identified. For this case-series study, 12 patients were selected with a follow-up period of at least two years. The surgical procedures were executed by a single surgical team, which involved single or multi-piece Le Fort I osteotomy, Hunsuck/Epker modification of bilateral sagittal split osteotomy (BSSO), and/or genioplasty. The recorded outcome variables were postoperative adverse events, including respiratory or blood-related complications, wound infection, neurosensory disturbances, temporomandibular joint (TMJ) complications, and relapse. Only two patients recovered after surgery without any postoperative complications, whereas others had delayed recovery from neurosensory disturbance (5/12), infection (5/12), TMJ complications (2/12), and other complications. The findings of this study suggest that patients with autoimmune diseases undergoing orthognathic surgery are at higher risk of complications, highlighting the importance of careful consideration of patient selection and risk stratification before surgical intervention. The study also emphasizes the importance of close postoperative follow-up to detect and manage complications promptly.

Trueness of cone-beam computed tomography-derived skull models fabricated by different technology-based three-dimensional printers.

BACKGROUND: Three-dimensional (3D) printing is a novel innovation in the field of craniomaxillofacial surgery, however, a lack of evidence exists related to the comparison of the trueness of skull models fabricated using different technology-based printers belonging to different cost segments. METHODS: A study was performed to investigate the trueness of cone-beam computed tomography-derived skull models fabricated using different technology based on low-, medium-, and high-cost 3D printers. Following the segmentation of a patient's skull, the model was printed by: (i) a low-cost fused filament fabrication printer; (ii) a medium-cost stereolithography printer; and (iii) a high-cost material jetting printer. The fabricated models were later scanned by industrial computed tomography and superimposed onto the original reference virtual model by applying surface-based registration. A part comparison color-coded analysis was conducted for assessing the difference between the reference and scanned models. A one-way analysis of variance (ANOVA) with Bonferroni correction was applied for statistical analysis. RESULTS: The model printed with the low-cost fused filament fabrication printer showed the highest mean absolute error ([Formula: see text]), whereas both medium-cost stereolithography-based and the high-cost material jetting models had an overall similar dimensional error of [Formula: see text] and [Formula: see text], respectively. Overall, the models printed with medium- and high-cost printers showed a significantly ([Formula: see text]) lower error compared to the low-cost printer. CONCLUSIONS: Both stereolithography and material jetting based printers, belonging to the medium- and high-cost market segment, were able to replicate the skeletal anatomy with optimal trueness, which might be suitable for patient-specific treatment planning tasks in craniomaxillofacial surgery. In contrast, the low-cost fused filament fabrication printer could serve as a cost-effective alternative for anatomical education, and/or patient communication.

CBCT for Diagnostics, Treatment Planning and Monitoring of Sinus Floor Elevation Procedures.

Sinus floor elevation (SFE) is a standard surgical technique used to compensate for alveolar bone resorption in the posterior maxilla. Such a surgical procedure requires radiographic imaging pre- and postoperatively for diagnosis, treatment planning, and outcome assessment. Cone beam computed tomography (CBCT) has become a well-established imaging modality in the dentomaxillofacial region. The following narrative review is aimed to provide clinicians with an overview of the role of three-dimensional (3D) CBCT imaging for diagnostics, treatment planning, and postoperative monitoring of SFE procedures. CBCT imaging prior to SFE provides surgeons with a more detailed view of the surgical site, allows for the detection of potential pathologies three-dimensionally, and helps to virtually plan the procedure more precisely while reducing patient morbidity. In addition, it serves as a useful follow-up tool for assessing sinus and bone graft changes. Meanwhile, using CBCT imaging has to be standardized and justified based on the recognized diagnostic imaging guidelines, taking into account both the technical and clinical considerations. Future studies are recommended to incorporate artificial intelligence-based solutions for automating and standardizing the diagnostic and decision-making process in the context of SFE procedures to further improve the standards of patient care.

Moving toward patient specificity for devising cone-beam computed tomography field-of-views: A normative maxillary skeletal dimensional analysis.

BACKGROUND: A lack of evidence exists comprehensively assessing skeletal dimensions in a Caucasian population group at different ages. AIM: To provide age- and gender-specific normative skeletal dimensional measurements of the maxillary region using cone-beam computed tomography (CBCT) imaging. DESIGN: Cone-beam computed tomography images of Caucasian patients were acquired and divided into different age groups ranging from 8 to 20 years. Linear measurements were taken to evaluate seven distance-based variables, which included anterior nasal spine-posterior nasal spine (ANS-PNS) distance, bilateral maxillary first molar's central fossae (CF) distance, palatal vault depth (PVD), bilateral palatal cementoenamel junction (PCEJ) distance, bilateral vestibular CEJ (VCEJ) distance, bilateral jugulare distance (Jug) and arch length (AL). RESULTS: A total of 529 patients (243 male, 286 female) were selected. ANS-PNS and PVD showed the highest change in dimensions from 8 to 20 years of age. On the contrary, AL had the least variability in all age groups. Male patients had larger dimensions and showed a significant change in all dimensional measurements (p < .001) than female patients. CONCLUSION: The maxillary linear dimensions varied across different age groups. The presented maxillary normative data could serve as a reference guide for devising patient-specific CBCT field of views.