OBJECTIVE: The aim of this work is to present a new protocol for implant surgical planning which involves the combined use of artificial intelligence (AI) and mixed reality (MR).
METHODS: This protocol involves the acquisition of three-dimensional (3D) patient data through intraoral scanning (IOS) and cone beam computed tomography (CBCT). These data are loaded into AI software which automatically segments and aligns the patient\'s 3D models. These 3D models are loaded into MR software and used for planning implant surgery through holography. The files are then exported and used to design surgical guides via open-source software, which are 3D printed and used to prepare the implant sites through static computer-assisted implant surgery (s-CAIS). The case is finalized prosthetically through a fully digital protocol. The accuracy of implant positioning is verified by comparing the planned position with the actual position of the implants after surgery.
RESULTS: As a proof of principle, the present protocol seems to be to be reliable and efficient when used for planning simple cases of s-CAIS in partially edentulous patients. The clinician can plan the implants in an authentic 3D environment without using any radiology-guided surgery software. The precision of implant placement seems clinically acceptable, with minor deviations.
CONCLUSIONS: The present study suggests that AI and MR technologies can be successfully used in s-CAIS for an authentic 3D planning. Further clinical studies are needed to validate this protocol.

Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.

OBJECTIVE: To investigate the accuracy of artificial intelligence (AI)-based segmentation of the mandibular canal, compared to the conventional manual tracing, implementing implant planning software.
METHODS: Localization of the mandibular canals was performed for 104 randomly selected patients. A localization was performed by three experienced clinicians in order to serve as control. Five tracings were performed: One from a clinician with a moderate experience with a manual tracing (I1), followed by the implementation of an automatic refinement (I2), one manual from a dental student (S1), and one from the experienced clinician, followed by an automatic refinement (E). Subsequently, two fully automatic AI-driven segmentations were performed (A1,A2). The accuracy between each method was measured using root mean square error calculation.
RESULTS: The discrepancy among the models of the mandibular canals, between the experienced clinicians and each investigated method ranged from 0.21 to 7.65 mm with a mean of 3.5 mm RMS error. The analysis of each separate mandibular canal\'s section revealed that mean RMS error was higher in the posterior and anterior loop compared to the middle section. Regarding time efficiency, tracing by experienced users required more time compared to AI-driven segmentation.
CONCLUSIONS: The experience of the clinician had a significant influence on the accuracy of mandibular canal\'s localization. An AI-driven segmentation of the mandibular canal constitutes a time-efficient and reliable procedure for pre-operative implant planning. Nevertheless, AI-based segmentation results should always be verified, as a subsequent manual refinement of the initial segmentation may be required to avoid clinical significant errors.

This study explored the average length of the incisive branch (IB) of the inferior alveolar nerve on cone-beam computerized tomography (CBCT) with regard to patient demographics in patients with edentulous mandibles. CBCT was used in a retrospective study of edentulous mandibles to assess the presence and anatomical variation for the IB. Three independent observers measured bilateral IB lengths. In addition to demographics, IB length and port of exit data were obtained. A 1-way analysis of variance was used to test whether IB length varied by sex or port of exit, and a standard Pearson correlation was used to test for IB length and age significance, with a significance level of P < .05. Intraclass correlation coefficients showed significant agreement in IB length across all observers. No significant difference was noted between the exit port and IB length. An important effect was reported for sex, indicating women have generally shorter IB lengths (9.43 ± 3.99 vs 10.55 ± 3.92). There was a significant correlation with age, but the relationship was weak. Edentulous mandibles have an altered anatomic landscape, and establishing predictive IB dimensions aids practitioners in surgical planning.

This study explored the average length of the incisive branch of the inferior alveolar nerve on cone-beam computed tomography (CBCT) regarding patient demographics in patients with edentulous mandibles. CBCT was utilized in a retrospective study of edentulous mandibles to assess the presence and anatomical variation for the incisive branch (IB). Three independent observers measured bilateral IB lengths. In addition to demographics, IB length and port of exit data were obtained. A one-way ANOVA was used to test IB length varied by sex or port of exit, and a standard Pearson\'s correlation was used to test for IB length and age significance with a significance level of a p-value<0.05. Intraclass correlation coefficients show significant agreement in IB length across all observers. No significant difference was noted between the exit port and IB length. An important effect was reported for sex, indicating women have generally shorter IB lengths (9.43 ± 3.99 vs 10.55 ± 3.92). There was a significant correlation with age, but the relationship was weak. Edentulous mandibles have an altered anatomic landscape and establishing predictive incisive branch dimensions aids practitioners in surgical planning.

OBJECTIVE: To compare the standard 360-degree CBCT acquisition protocol to the low dose 180-degree CBCT protocol for implant planning.
METHODS: Two groups of patients, each consisting of 35 patients, were included in the study. The first group was imaged with the conventional 360-degree CBCT protocol, and the second group was imaged with the low dose 180-degree CBCT protocol. The primary outcome of this study was the number of scans that needed to be repeated due to poor image quality. In addition, six secondary parameters were evaluated quantitatively and qualitatively.
RESULTS: The results showed that there was no need to repeat any of the CBCT scans that were obtained in either group, which showed that 360-degree and 180-degree protocols had comparable image quality. As for the secondary parameters, the results showed that the evaluators were able to evaluate the six chosen parameters in a comparable manner.
CONCLUSIONS: The 180-degree low dose CBCT scan is a viable option for dental implant treatment planning in the posterior mandible as it provides comparable and adequate information regarding accuracy of measurements, identification of critical structures, evaluation of bone quality, and any pathology.

OBJECTIVE: Diagnostic imaging is crucial for implant dentistry. This review provides an up-to-date perspective on the application of digital diagnostic imaging in implant dentistry.
METHODS: Electronic searches were conducted in PubMed focusing on the question \'when (and why) do we need diagnostic imaging in implant dentistry?\' The search results were summarised to identify different applications of digital diagnostic imaging in implant dentistry.
RESULTS: The most used imaging modalities in implant dentistry include intraoral periapical radiographs, panoramic views and cone beam computed tomography (CBCT). These are dependent on acquisition standardisation to optimise image quality. Particularly for CBCT, other technical parameters (i.e., tube current, tube voltage, field-of-view, voxel size) are relevant minimising the occurrence of artefacts. There is a growing interest in digital workflows, integrating diagnostic imaging and automation. Artificial intelligence (AI) has been incorporated into these workflows and is expected to play a significant role in the future of implant dentistry. Preliminary evidence supports the use of ionising-radiation-free imaging modalities (e.g., MRI and ultrasound) that can add value in terms of soft tissue visualisation.
CONCLUSIONS: Digital diagnostic imaging is the sine qua non in implant dentistry. Image acquisition protocols must be tailored to the patient\'s needs and clinical indication, considering the trade-off between radiation exposure and needed information. growing evidence supporting the benefits of digital workflows, from planning to execution, and the future of implant dentistry will likely involve a synergy between human expertise and AI-driven intelligence. Transiting into ionising-radiation-free imaging modalities is feasible, but these must be further developed before clinical implementation.

OBJECTIVE: The purpose of this prospective study was to determine the inter- and intraindividual variability in virtual single-tooth implant positioning based on the level of expertise, specialty, total time spent, and the use of a prosthetic tooth setup.
METHODS: Virtual implant planning was performed on matched pre- and post-extraction intraoral scans (IOS), and cone-beam computed tomography scans of 15 patients. Twelve individual examiners, involving six novices and experts from oral surgery and prosthodontics positioned the implants, first based on anatomical landmarks utilizing only the post-extraction, and second with the use of the pre-extraction IOS as a setup. The time for implant positioning was recorded. After 1 month, all virtual plannings were performed again. The individual implant positions were superimposed to obtain 3D deviations using a software algorithm.
RESULTS: An interindividual variability with mean angular, crestal, and apical positional deviations of 3.8 ± 1.94°, 1.11 ± 0.55, and 1.54 ± 0.66 mm, respectively, was found. When assessing intraindividual variability, deviations of 3.28 ± 1.99°, 0.78 ± 0.46, and 1.12 ± 0.61 mm, respectively, were observed. Implants planned by experts exhibited statistically lower deviations compared to those planned by novices. Longer planning times resulted in lower deviations in the experts\' group but not in the novices. Oral surgeons demonstrated lower crestal, but not angular and apical deviations than prosthodontists. The use of a setup only led to minor adjustments.
CONCLUSIONS: Substantial inter- and intraindividual variability exists during implant positioning utilizing specialized software planning. The level of expertise and the time invested influenced the deviations of the implant position during the planning sequence.

BACKGROUND: The success of any dental implant surgery depends on the correct diagnosis and treatment planning.
OBJECTIVE: The aim of this study was to compare the dimensions of the alveolar ridge width using different techniques for implant placement.
METHODS: The study involved 27 partially edentulous subjects aged 18-50, including males and females. In this study, the dimensions of the ridge were evaluated by ridge mapping on a cast, ridge mapping using a bone caliper, and ridge mapping with the help of an occlusal radiograph. All three methods were compared with ridge mapping by cone beam computed tomography (CBCT). For each subject, the site of implant placement was marked on the study model. Alveolar ridge measurement was done in the mouth by a bone caliper under local anesthesia with the help of a stent with a hole. Ridge mapping on a cast was done after sectioning the cast and marking with the help of a periodontal probe and stent. Ridge mapping was done on an occlusal radiograph by converting an acetate stent into a radiographic stent. Finally, CBCT was taken for each patient for ridge mapping. All four readings were tabulated.
RESULTS: Comparing the mean alveolar ridge width of four groups, ANOVA showed significantly different alveolar ridge width among the groups (F=7.89, p<0.001). The validity (accuracy and precision) of ridge mapping on a cast, ridge mapping using a bone caliper, and occlusal radiograph against the CBCT (gold standard) was done using concordance correlation analysis. The concordance correlation analysis showed the highest association (ρ=0.8196) and precision (ϸ=82.61%) of ridge mapping using a bone caliper with CBCT. However, the accuracy of ridge mapping on a cast (Cb=99.42%) was the highest, followed by ridge mapping using a bone caliper (Cb=82.61%). The analysis concluded that both techniques are equivalent to CBCT and can be used interchangeably.
CONCLUSIONS: The mean alveolar ridge width of the occlusal radiograph was the highest, followed by CBCT, ridge mapping on a cast, and ridge mapping using a bone caliper the least (occlusal radiograph > CBCT > ridge mapping on cast >ridge mapping using bone caliper). But at the same time, it can also be used interchangeably.

OBJECTIVE: To determine the effect of restoration artifact (\'metal artifact\') on registration accuracy of an intraoral scan and cone-beam computed tomography (CBCT) scan, comparing fiducial marker-based registration with markerless registration.
METHODS: A maxillary model was fitted with multiple configurations of zirconia crowns to simulate various states of oral rehabilitation. Intraoral scans and CBCT scans (half and full rotation) were acquired. Registration was performed using markerless (point-based registration with surface-based refinement) and fiducial marker-based registration. Each experimental condition was repeated 10 times (n = 320). The absolute deviation was measured at the canines and first molars, and the average and maximum values were analysed using multiple linear regression.
RESULTS: R2 was 0.874 for average error and 0.858 for maximum error. For markerless registration, there were 0.041 mm (p < .001) and 0.045 mm (p < .001) increases in average and maximum error per crown, respectively. For fiducial marker-based registration, the effect of additional crowns was not statistically significant for average (p = .067) or maximum (p = .438) error. For a full arch of crowns, the regression model predicted average and maximum errors of 0.581 and 0.697 mm for the markerless technique, and 0.185 and 0.210 mm for the fiducial marker-based technique. Overall, the fiducial marker-based technique was more accurate for four or more crowns. The half rotation scan increased average error by 0.021 mm (p = .001) and maximum error by 0.029 mm (p < .001).
CONCLUSIONS: Under the present study\'s experimental conditions, the fiducial marker-based technique should be considered if four or more full-coverage highly radiopaque restorations are present.