Novel AI-based automated virtual implant placement: Artificial versus human intelligence.

OBJECTIVES: To assess quality, clinical acceptance, time-efficiency, and consistency of a novel artificial intelligence (AI)-driven tool for automated presurgical implant planning for single tooth replacement, compared to a human intelligence (HI)-based approach. MATERIALS AND METHODS: To validate a novel AI-driven implant placement tool, a dataset of 10 time-matching cone beam computed tomography (CBCT) scans and intra-oral scans (IOS) previously acquired for single mandibular molar/premolar implant placement was included. An AI pre-trained model for implant planning was compared to human expert-based planning, followed by the export, evaluation and comparison of two generic implants-AI-generated and human-generated-for each case. The quality of both approaches was assessed by 12 calibrated dentists through blinded observations using a visual analogue scale (VAS), while clinical acceptance was evaluated through an AI versus HI battle (Turing test). Subsequently, time efficiency and consistency were evaluated and compared between both planning methods. RESULTS: Overall, 360 observations were gathered, with 240 dedicated to VAS, of which 95 % (AI) and 96 % (HI) required no major, clinically relevant corrections. In the AI versus HI Turing test (120 observations), 4 cases had matching judgments for AI and HI, with AI favoured in 3 and HI in 3. Additionally, AI completed planning more than twice as fast as HI, taking only 198 ± 33 s compared to 435 ± 92 s (p < 0.05). Furthermore, AI demonstrated higher consistency with zero-degree median surface deviation (MSD) compared to HI (MSD=0.3 ± 0.17 mm). CONCLUSION: AI demonstrated expert-quality and clinically acceptable single-implant planning, proving to be more time-efficient and consistent than the HI-based approach. CLINICAL SIGNIFICANCE: Presurgical implant planning often requires multidisciplinary collaboration between highly experienced specialists, which can be complex, cumbersome and time-consuming. However, AI-driven implant planning has the potential to allow clinically acceptable planning, significantly more time-efficient and consistent than the human expert.

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.

Smartphone applications for facial scanning: A technical and scoping review.

INTRODUCTION: Facial scanning through smartphone scanning applications (SSA) is increasingly being used for medical applications as cost-effective, chairside method. However, clinical validation is lacking. This review aims to address: (1) Which SSA could perform facial scanning? (2) Which SSA can be clinically used? (3) Which SSA have been reported and scientifically validated for medical applications? METHODS: Technical search for SSA designed for face or object scanning was conducted on Google, Apple App Store, and Google Play Store from August 2022 to December 2023. Literature search was performed on PubMed, Cochrane, EMBASE, MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, Clinicaltrials.gov, ICTRP (WHO) and preprints up to 2023. Eligibility criteria included English-written scientific articles incorporating at least one SSA for clinical purposes. SSA selection and data extraction were executed by one reviewer, validated by second, with third reviewer being consulted for discordances. RESULTS: Sixty-three applications designed for three-dimensional object scanning were retrieved, with 52 currently offering facial scanning capabilities. Fifty-six scientific articles, comprising two case reports, 16 proof-of-concepts and 38 experimental studies were analysed. Thirteen applications (123D Catch, 3D Creator, Bellus 3D Dental Pro, Bellus 3D Face app, Bellus 3D Face Maker, Capture, Heges, Metascan, Polycam, Scandy Pro, Scaniverse, Tap tap tap and Trnio) were reported in literature for digital workflow integration, comparison or proof-of-concept studies. CONCLUSION: Fifty-two SSA can perform facial scanning currently and can be used clinically, offering cost-effectiveness, portability and user-friendliness. Although clinical validation is crucial, only 13 SSA were scientifically validated, underlying awareness of potential pitfalls and limitations.

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.

Recommendations to minimize tooth root remodeling in patients undergoing maxillary osteotomies.

The purpose of this study was to report root remodeling/resorption percentages of maxillary teeth following the different maxillary osteotomies; i.e. one-piece, two-pieces, three-pieces Le Fort I, surgically assisted rapid palatal expansion (SARPE). The possibility of relationships between root remodeling and various patient- and/or treatment-related factors were further investigated. A total of 110 patients (1075 teeth) who underwent combined orthodontic and orthognathic surgery were studied retrospectively. The sample size was divided into: 30 patients in one-piece Le Fort I group, 30 patients in multi-pieces Le Fort I group, 20 patients in SARPE group and 30 patients in orthodontic group. Preoperative and 1 year postoperative cone beam computed tomography (CBCT) scans were obtained. A validated and automated method for evaluating root remodeling and resorption in three dimensions (3D) was applied. SARPE group showed the highest percentage of root remodeling. Spearman correlation coefficient revealed a positive relationship between maxillary advancement and root remodeling, with more advancement contributing to more root remodeling. On the other hand, the orthodontic group showed a negative correlation with age indicating increased root remodeling in younger patients. Based on the reported results of linear, volumetric and morphological changes of the root after 1 year, clinical recommendations were provided in the form of decision tree flowchart and tables. These recommendations can serve as a valuable resource for surgeons in estimating and managing root remodeling and resorption associated with different maxillary surgical techniques.

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.

Key insights into antiresorptive drug use and osteonecrosis in osteoporotic patients undergoing tooth extractions: A clinical and CBCT assessment.

This study investigates the effects of antiresorptive drugs and risk factors for medication-related osteonecrosis of the jaws in osteoporotic patients undergoing tooth extraction. Among the findings, antiresorptive-treated patients had thicker lamina dura and longer healing times. Additionally, corticosteroid intake and multi-rooted teeth carried a higher osteonecrosis risk. Bone sequestrum indicated osteonecrosis. PURPOSE: To describe the effects of antiresorptive drugs (ARD) in the maxilla and mandible and risk factors for medication-related osteonecrosis of the jaws (MRONJ) in osteoporotic patients undergoing tooth extractions using clinical data and cone beam computed tomography (CBCT). METHODS: This retrospective cohort study collected clinical and CBCT data from 176 patients. The study group (n = 78; 224 extractions) received ARD treatment, underwent tooth extraction, and had a pre-operative CBCT. Additionally, age-, sex-, and tooth-matched controls were selected (n = 98; 227 extractions). Radiographic examinations were performed independently by three calibrated examiners. Statistical analysis included Chi-square, Fisher's exact, Mann-Whitney U, and t-tests to contrast clinical and radiographic data between study and control, MRONJ + and MRONJ - , and bisphosphonate and denosumab patients/sites. Significance was set at p ≤ 0.05. RESULTS: From the study group, 4 patients (5%) and 5 sites (2%) developed MRONJ after tooth extraction. ARD-treated patients exhibited significantly more thickening of the lamina dura and a longer average mucosal healing time (4.4 weeks) than controls (2.6 weeks). In the study group, MRONJ risk significantly increased with corticosteroid intake and in multi-rooted teeth. No significant differences between bisphosphonates and denosumab use were seen in the tomographic features (p > 0.05). Lastly, bone sequestrum was exclusively observed in osteoporotic patients, who exhibited post-operative exposed bone or histological evidence of osteonecrosis. CONCLUSION: Osteoporotic patients under ARD may exhibit thickening of the lamina dura and prolonged post-operative healing. Among these patients, multi-rooted teeth are at higher risk for MRONJ than single-rooted teeth. Sequester formation is a radiographic indicator of osteonecrosis.

Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study.

BACKGROUND: Primary teeth segmentation on cone beam computed tomography (CBCT) scans is essential for paediatric treatment planning. Conventional methods, however, are time-consuming and necessitate advanced expertise. AIM: The aim of this study was to validate an artificial intelligence (AI) cloud-based platform for automated segmentation (AS) of primary teeth on CBCT. Its accuracy, time efficiency, and consistency were compared with manual segmentation (MS). DESIGN: A dataset comprising 402 primary teeth (37 CBCT scans) was retrospectively retrieved from two CBCT devices. Primary teeth were manually segmented using a cloud-based platform representing the ground truth, whereas AS was performed on the same platform. To assess the AI tool's performance, voxel- and surface-based metrics were employed to compare MS and AS methods. Additionally, segmentation time was recorded for each method, and intra-class correlation coefficient (ICC) assessed consistency between them. RESULTS: AS revealed high performance in segmenting primary teeth with high accuracy (98 ± 1%) and dice similarity coefficient (DSC; 95 ± 2%). Moreover, it was 35 times faster than the manual approach with an average time of 24 s. Both MS and AS demonstrated excellent consistency (ICC = 0.99 and 1, respectively). CONCLUSION: The platform demonstrated expert-level accuracy, and time-efficient and consistent segmentation of primary teeth on CBCT scans, serving treatment planning in children.

Developing Advanced Patient-Specific In Silico Models: A New Era in Biomechanical Analysis of Tooth Autotransplantation.

INTRODUCTION: As personalized medicine advances, there is an escalating need for sophisticated tools to understand complex biomechanical phenomena in clinical research. Recognizing a significant gap, this study pioneers the development of patient-specific in silico models for tooth autotransplantation (TAT), setting a new standard for predictive accuracy and reliability in evaluating TAT outcomes. METHODS: Development of the models relied on 6 consecutive cases of young patients (mean age 11.66 years ± 0.79), all undergoing TAT procedures. The development process involved creating detailed in silico replicas of patient oral structures, focusing on transplanting upper premolars to central incisors. These models underpinned finite element analysis simulations, testing various masticatory and traumatic scenarios. RESULTS: The models highlighted critical biomechanical insights. The finite element models indicated homogeneous stress distribution in control teeth, contrasted by shape-dependent stress patterns in transplanted teeth. The surface deviation in the postoperative year for the transplanted elements showed a mean deviation of 0.33 mm (±0.28), significantly higher than their contralateral counterparts at 0.05 mm (±0.04). CONCLUSIONS: By developing advanced patient-specific in silico models, we are ushering in a transformative era in TAT research and practice. These models are not just analytical tools; they are predictive instruments capturing patient uniqueness, including anatomical, masticatory, and tissue variables, essential for understanding biomechanical responses in TAT. This foundational work paves the way for future studies, where applying these models to larger cohorts will further validate their predictive capabilities and influence on TAT success parameters.

Validation and comparison of 2D grading scales and 3D volumetric measurements for outcome assessment of bone-grafted alveolar clefts in children.

BACKGROUND: Several methods have been proposed to assess outcome of bone-grafted alveolar clefts on cone beam computed tomography (CBCT), but so far these methods have not been compared and clinically validated. OBJECTIVES: To validate and compare methods for outcome assessment of bone-grafted clefts with CBCT and provide recommendations for follow-up. METHODS: In this observational follow-up study, two grading scales (Suomalainen; Liu) and the volumetric bone fill (BF) were used to assess the outcome of 23 autogenous bone-grafted unilateral alveolar clefts. The mean age at bone grafting was 9 years. The volumetric BF was assessed in five vertical sections. The bone-grafted cleft outcome was based on a binary coding (success or regraft) on a clinical multidisciplinary expert consensus meeting. Grading scales and volumetric assessment were compared in relation to the bone-grafted cleft outcome (success or regraft). Reliability for the different outcome variables was analyzed with intra-class correlation and by calculating kappa values. LIMITATIONS: The study had a limited sample size. Clinical CBCT acquisitions had a varying tube current and exposure time. RESULTS: Volumetric 3D measurements allowed for outcome assessment of bone-grafted alveolar clefts with high reliability and validity. The two grading scales showed highly reliable outcomes, yet the validity was high for the Suomalainen grading scale but low for the Liu grading scale. CONCLUSIONS: Volumetric 3D measurement as well as the Suomalainen grading can be recommended for outcome assessment of the bone-grafted cleft. Yet, one must always make a patient-specific assessment if there is a need to regraft.

Artificial intelligence serving pre-surgical digital implant planning: A scoping review.

OBJECTIVES: To conduct a scoping review focusing on artificial intelligence (AI) applications in presurgical dental implant planning. Additionally, to assess the automation degree of clinically available pre-surgical implant planning software. DATA AND SOURCES: A systematic electronic literature search was performed in five databases (PubMed, Embase, Web of Science, Cochrane Library, and Scopus), along with exploring gray literature web-based resources until November 2023. English-language studies on AI-driven tools for digital implant planning were included based on an independent evaluation by two reviewers. An assessment of automation steps in dental implant planning software available on the market up to November 2023 was also performed. STUDY SELECTION AND RESULTS: From an initial 1,732 studies, 47 met eligibility criteria. Within this subset, 39 studies focused on AI networks for anatomical landmark-based segmentation, creating virtual patients. Eight studies were dedicated to AI networks for virtual implant placement. Additionally, a total of 12 commonly available implant planning software applications were identified and assessed for their level of automation in pre-surgical digital implant workflows. Notably, only six of these featured at least one fully automated step in the planning software, with none possessing a fully automated implant planning protocol. CONCLUSIONS: AI plays a crucial role in achieving accurate, time-efficient, and consistent segmentation of anatomical landmarks, serving the process of virtual patient creation. Additionally, currently available systems for virtual implant placement demonstrate different degrees of automation. It is important to highlight that, as of now, full automation of this process has not been documented nor scientifically validated. CLINICAL SIGNIFICANCE: Scientific and clinical validation of AI applications for presurgical dental implant planning is currently scarce. The present review allows the clinician to identify AI-based automation in presurgical dental implant planning and assess the potential underlying scientific validation.

Development and characterization of colloidal pNIPAM-methylcellulose microgels with potential application for drug delivery in dentoalveolar tissue engineering strategies.

Incorporation of growth factors, signaling molecules and drugs can be vital for the success of tissue engineering in complex structures such as the dentoalveolar region. This has led to the development of a variety of drug release systems. This study aimed to develop pNIPAM-methylcellulose microgels with different synthesis parameters based on a 23 full factorial design of experiments for this application. Microgel properties, including volume phase transition temperature (VPTT), hydrodynamic size, drug loading and release, and cytocompatibility were systematically evaluated. The results demonstrated successful copolymerization and development of the microgels, a hydrodynamic size ranging from ∼200 to ∼500 nm, and VPTT in the range of 34-39 °C. Furthermore, loading of genipin, capable of inducing odontoblastic differentiation, and its sustained release over a week was shown in all formulations. Together, this can serve as a solid basis for the development of tunable drug-delivering pNIPAM-methylcellulose microgels for specific tissue engineering applications.

Three-dimensional evaluation of secondary alveolar bone grafting in patients with unilateral cleft lip and palate: A 2-3 year post-operative follow-up.

OBJECTIVES: The present study aims to quantitatively assess secondary alveolar bone graft (SABG) resorption in unilateral cleft lip, alveolus and palate (UCLAP) patients in a 2-3 year longitudinal follow-up setting by using a validated 3D protocol. Furthermore, the potential relation of SABG resorption with maxillary canine position and a number of patient-related factors was investigated. METHODS: UCLAP patients who underwent SABG and had good quality CBCT images at the following timepoints were included in the study: pre-operative (T0), immediate (T1), 6 months (T2) and either 1-2 years (T3) or 2-3 years (T4) post-operative. The final bone grafted region was defined on the T1 scans and refined in the registered T0 scans. The bone graft after resorption was determined by applying threshold-based segmentation on the registered T2, T3 or T4 scans within the segmented bone graft volume. The position of the canines was determined at every timepoint at the cleft and non-cleft side. RESULTS: Forty-five UCLAP patients (mean age 9.0 ± 1.3 years) were included. In the first 6 months after SABG, 43.6% bone resorption was recorded. 2-3 years post-operative, 56% bone resorption was found if the maxillary canine was not yet erupted and 42.7% if it erupted through the graft. The vertical position of the canines was significantly higher on the cleft side at T3. CONCLUSIONS: The present study reports significant SABG resorption over time. However, no correlation was found between SABG resorption and canine position, nor between other patient-related factors.

Development of 3D Printed pNIPAM-Chitosan Scaffolds for Dentoalveolar Tissue Engineering.

While available treatments have addressed a variety of complications in the dentoalveolar region, associated challenges have resulted in exploration of tissue engineering techniques. Often, scaffold biomaterials with specific properties are required for such strategies to be successful, development of which is an active area of research. This study focuses on the development of a copolymer of poly (N-isopropylacrylamide) (pNIPAM) and chitosan, used for 3D printing of scaffolds for dentoalveolar regeneration. The synthesized material was characterized by Fourier transform infrared spectroscopy, and the possibility of printing was evaluated through various printability tests. The rate of degradation and swelling was analyzed through gravimetry, and surface morphology was characterized by scanning electron microscopy. Viability of dental pulp stem cells seeded on the scaffolds was evaluated by live/dead analysis and DNA quantification. The results demonstrated successful copolymerization, and three formulations among various synthesized formulations were successfully 3D printed. Up to 35% degradability was confirmed within 7 days, and a maximum swelling of approximately 1200% was achieved. Furthermore, initial assessment of cell viability demonstrated biocompatibility of the developed scaffolds. While further studies are required to achieve the tissue engineering goals, the present results tend to indicate that the proposed hydrogel might be a valid candidate for scaffold fabrication serving dentoalveolar tissue engineering through 3D printing.

Tissue-engineered bone construct promotes early osseointegration of implants with low primary stability in oversized osteotomy.

OBJECTIVES: To evaluate the histological parameters and bone mechanical properties around implants with low primary stability (PS) in grafted bone substitutes within an oversized osteotomy. MATERIALS AND METHODS: An oversized osteotomy penetrating the double cortical bone layers was made on both femora of 24 New Zealand white rabbits. Bilaterally in the femur of all animals, 48 implants were installed, subdivided into four groups, corresponding to four prepared tissue-engineering bone complexes (TEBCs), which were placed between the implant surface and native bone wall: A: tricalcium phosphate ß (TCP-ß); B: autologous adipose derived-stem cells with TCP-ß (ASCs/TCP-ß); C: ASCs transfected with the enhanced-GFP gene with TCP-ß (EGFP-ASCs/TCP-ß); D: ASCs transfected with the BMP-2 gene with TCP-ß (BMP2-ASCs/TCP-ß). Trichrome fluorescent labeling was conducted. Animals were sacrificed after eight weeks. The trichromatic fluorescent labeling (%TFL), area of new bone (%NB), residual material (%RM), bone-implant contact (%BIC), and the removal torque force (RTF, N/cm) were assessed. RESULTS: ASCs were successfully isolated from adipose tissue, and the primary ASCs were induced into osteogenic, chondrogenic, and adipogenic differentiation. The BMP-2 overexpression of ASCs sustained for ten days and greatly enhanced the expression of osteopontin (OPN). At eight weeks post-implantation, increased %NB and RTF were found in all groups. The most significant value of %TFL, %BIC and lowest %RM was detected in group D. CONCLUSION: The low PS implants osseointegrate with considerable new bone in grafted TEBCs within an oversized osteotomy. Applying BMP-2 overexpressing ASCs-based TEBC promoted earlier osseointegration and more solid bone mechanical properties on low PS implants. Bone graft offers a wedging effect for the implant with low PS at placement and promotes osteogenesis on their surface in the healing period.

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.

Full virtual patient generated by artificial intelligence-driven integrated segmentation of craniomaxillofacial structures from CBCT images.

OBJECTIVES: To assess the performance, time-efficiency, and consistency of a convolutional neural network (CNN) based automated approach for integrated segmentation of craniomaxillofacial structures compared with semi-automated method for creating a virtual patient using cone beam computed tomography (CBCT) scans. METHODS: Thirty CBCT scans were selected. Six craniomaxillofacial structures, encompassing the maxillofacial complex bones, maxillary sinus, dentition, mandible, mandibular canal, and pharyngeal airway space, were segmented on these scans using semi-automated and composite of previously validated CNN-based automated segmentation techniques for individual structures. A qualitative assessment of the automated segmentation revealed the need for minor refinements, which were manually corrected. These refined segmentations served as a reference for comparing semi-automated and automated integrated segmentations. RESULTS: The majority of minor adjustments with the automated approach involved under-segmentation of sinus mucosal thickening and regions with reduced bone thickness within the maxillofacial complex. The automated and the semi-automated approaches required an average time of 1.1 min and 48.4 min, respectively. The automated method demonstrated a greater degree of similarity (99.6 %) to the reference than the semi-automated approach (88.3 %). The standard deviation values for all metrics with the automated approach were low, indicating a high consistency. CONCLUSIONS: The CNN-driven integrated segmentation approach proved to be accurate, time-efficient, and consistent for creating a CBCT-derived virtual patient through simultaneous segmentation of craniomaxillofacial structures. CLINICAL RELEVANCE: The creation of a virtual orofacial patient using an automated approach could potentially transform personalized digital workflows. This advancement could be particularly beneficial for treatment planning in a variety of dental and maxillofacial specialties.