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Artificial intelligence (AI) is revolutionizing dentistry, offering new opportunities to improve the precision and efficiency of implantology. This literature review aims to evaluate the current evidence on the use of AI in implant planning assessment. The analysis was conducted through PubMed and Scopus search engines, using a combination of relevant keywords, including "artificial intelligence implantology", "AI implant planning", "AI dental implant", and "implantology artificial intelligence". Selected articles were carefully reviewed to identify studies reporting data on the effectiveness of AI in implant planning. The results of the literature review indicate a growing interest in the application of AI in implant planning, with evidence suggesting an improvement in precision and predictability compared to traditional methods. The summary of the obtained findings by the included studies represents the latest AI developments in implant planning, demonstrating its application for the automated detection of bones, the maxillary sinus, neuronal structure, and teeth. However, some disadvantages were also identified, including the need for high-quality training data and the lack of standardization in protocols. In conclusion, the use of AI in implant planning presents promising prospects for improving clinical outcomes and optimizing patient management. However, further research is needed to fully understand its potential and address the challenges associated with its implementation in clinical practice.

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OBJECTIVE: This study aims to present the bio-restorative approach in implant dentistry, which combines biological and restorative concepts through digital planning. This concept combines periodontal, surgical, and prosthetic variables, aiming to reduce patient morbidity while achieving satisfactory esthetic and functional outcomes in implant-supported restorations in the long term. OVERVIEW: Implant dentistry evolved from a primarily surgical to a recent prosthetically driven approach. This evolution was partly due to advancements in bone reconstructive techniques and an increased demand for esthetic outcomes. Recently, digital planning has introduced a new paradigm that allows for the full integration of both approaches. The bio-restorative concept considers functional, esthetic, and biological variables in a virtual planning environment. This is achieved through the simultaneous digital assessment of (A) anatomical site characteristics and (B) implant restorative variables. These variables include digital tooth arrangement, soft-hard tissue conditions, implant variables, supra-platform components, and a surgical plan that respects or modifies peri-implant phenotype. CONCLUSIONS: The bio-restorative concept is intended to improve contemporary implant dentistry by integrating updated biological and prosthetic notions through digital planning. Adopting this paradigm has the potential to redefine the standards in implant dentistry, fostering a holistic and patient-centered approach. CLINICAL CONSIDERATIONS: It enhances patient and clinician satisfaction through more efficient and less invasive procedures. Significantly, it improves predictability, leading to successful implant-supported restorations in the long term.

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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.

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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.

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OBJECTIVES: To investigate the accuracy of artificial intelligence (AI)-based segmentation of the mandibular canal, compared to the conventional manual tracing, implementing implant planning software. MATERIALS AND 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.

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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.

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AIM: 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.

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OBJECTIVES: To investigate the accuracy of conventional and automatic artificial intelligence (AI)-based registration of cone-beam computed tomography (CBCT) with intraoral scans and to evaluate the impact of user's experience, restoration artifact, number of missing teeth, and free-ended edentulous area. MATERIALS AND METHODS: Three initial registrations were performed for each of the 150 randomly selected patients, in an implant planning software: one from an experienced user, one from an inexperienced operator, and one from a randomly selected post-graduate student of implant dentistry. Six more registrations were performed for each dataset by the experienced clinician: implementing a manual or an automatic refinement, selecting 3 small or 3 large in-diameter surface areas and using multiple small or multiple large in-diameter surface areas. Finally, an automatic AI-driven registration was performed, using the AI tools that were integrated into the utilized implant planning software. The accuracy between each type of registration was measured using linear measurements between anatomical landmarks in metrology software. RESULTS: Fully automatic-based AI registration was not significantly different from the conventional methods tested for patients without restorations. In the presence of multiple restoration artifacts, user's experience was important for an accurate registration. Registrations' accuracy was affected by the number of free-ended edentulous areas, but not by the absolute number of missing teeth (p < .0083). CONCLUSIONS: In the absence of imaging artifacts, automated AI-based registration of CBCT data and model scan data can be as accurate as conventional superimposition methods. The number and size of selected superimposition areas should be individually chosen depending on each clinical situation.

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PURPOSE: Preoperative planning of maxillary anterior dental implant is a prerequisite to ensuring that the implant achieves the proper three-dimensional (3D) pose, which is essential for its long-term stability. However, the current planning process is labor-intensive and subjective, relying heavily on the surgeon's experience. Consequently, this paper proposes an automatic method for computing the optimal pose of the dental implant. METHODS: The method adopts the principle of prosthetically guided dental implant placement. Initially, the prosthesis coordinate system is established to determine the implant candidate orientations. Subsequently, virtual slices of the maxilla in the buccal-palatal direction are generated according to the prosthesis position. By extracting feature points from the virtual slices, the implant candidate starting points are acquired. Then, a candidate pose set is obtained by combining these candidate starting points and orientations. Finally, a pose evaluation indicator is introduced to determine the optimal implant pose from this set. RESULTS: Twenty-two cases were utilized to validate the method. The results show that the method could determine an ideal pose for the dental implant, with the average minimum distance between the implant and the left tooth root, the right tooth root, the palatal side, and the buccal side being 2.57 ± 0.53 mm, 2.59 ± 0.65 mm, 0.74 ± 0.19 mm, 1.83 ± 0.16 mm, respectively. The planning time was less than 9 s. CONCLUSION: Unlike manual planning, the proposed method can efficiently and accurately complete maxillary anterior dental implant planning, providing a theoretical analysis of the success rate of the implant. Thus, it has great potential for future clinical application.

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OBJECTIVES: To provide an overview of the current artificial intelligence (AI) based applications for assisting digital data acquisition and implant planning procedures. OVERVIEW: A review of the main AI-based applications integrated into digital data acquisitions technologies (facial scanners (FS), intraoral scanners (IOSs), cone beam computed tomography (CBCT) devices, and jaw trackers) and computer-aided static implant planning programs are provided. CONCLUSIONS: The main AI-based application integrated in some FS's programs involves the automatic alignment of facial and intraoral scans for virtual patient integration. The AI-based applications integrated into IOSs programs include scan cleaning, assist scanning, and automatic alignment between the implant scan body with its corresponding CAD object while scanning. The more frequently AI-based applications integrated into the programs of CBCT units involve positioning assistant, noise and artifacts reduction, structures identification and segmentation, airway analysis, and alignment of facial, intraoral, and CBCT scans. Some computer-aided static implant planning programs include patient's digital files, identification, labeling, and segmentation of anatomical structures, mandibular nerve tracing, automatic implant placement, and surgical implant guide design.

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A technique for virtually planning single implant by combining an intraoral digital scan, an opensource computer-aided design software program, bone sounding, and 2-dimensional radiographic imaging is described. The surgical implant guide is fabricated by using additive manufacturing technologies. Furthermore, the surgical implant guide positioned in the patient's mouth is used to radiographically verify the estimated mesio-distal implant angulation before proceeding with the surgical intervention and modified, if necessary. When a cone bean computed tomography scan is not available, this technique eases implant planning procedures and minimize possible surgical complications.

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Background Dental implants play a crucial role in modern dentistry, offering a durable and aesthetic option for tooth replacement. Understanding the preferences and practices of dentists regarding implant selection and restoration can provide critical insights into current trends and potential areas for improvement. As such, the objective of this study was to discover characteristics determining the selection criteria and preferences for dental implants and restorations among Saudi Arabian dentists. Methodology A cross-sectional, questionnaire-based survey was conducted among Saudi Arabian dentists to assess their practices and preferences for dental implant selection and restoration. The survey explored variables, including age, gender, educational status, regional practice distribution, implant-related experience, loading protocols, and implant system preferences. Results A total of 742 dental professionals responded to our questionnaire. The study revealed that a significant majority of Saudi Arabian dentists had placed (78.7%) and restored (72.9%) implants. Most dentists (78.6%) had participated in implant treatment planning. However, consistent usage of implant planning software was relatively low (29.8%). Loading protocol preferences varied, with early loading favored for anterior teeth and immediate loading for posterior teeth and edentulous patients. The main deterrents to immediate loading were patient type (27.0%), lack of training (19.9%), additional surgeries (19.9%), and administrative restrictions (17.8%). Straumann was the most preferred implant system, with aesthetic outcomes considered the most important factor in system selection. Conclusions The study provides a comprehensive overview of dental implant practices among Saudi Arabian dentists. It highlights a strong emphasis on aesthetic outcomes, a diverse approach to loading protocols, and room for increased usage of implant planning software. The findings suggest potential areas for further training and support, particularly in the use of immediate loading and implant planning software.

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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.

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BACKGROUND: Efficient utilization of residual bone volume and the prevention of inferior alveolar nerve injury are critical considerations in immediate implant placement (IIP) within the posterior mandibular region. Addressing these challenges, this study focuses on the clinical efficacy and implant accuracy of dynamic real-time navigation, an emerging technology designed to enhance precision in implantation procedures. METHODS: This study included 84 patients with 130 implants undergoing immediate placement in the posterior mandibular region. Stratified into dynamic navigation, static guide plate, and freehand implant groups, clinical indicators, including initial stability, distance to the inferior alveolar nerve canal, depth of implant placement, and various deviations, were systematically recorded. Statistical analysis, employing 1- or 2-way ANOVA and Student's t-test, allowed for a comprehensive evaluation of the efficacy of each technique. RESULTS: All 130 implants were successfully placed with an average torque of 22.53 ± 5.93 N.cm. In the navigation group, the distance to the inferior alveolar nerve and the depth of implant placement were significantly greater compared to the guide plate and freehand groups (P < 0.05). Implant deviation was significantly smaller in both the navigation and guide plate groups compared to the freehand group(P < 0.05). Additionally, the navigation group exhibited significantly reduced root and angle deviations compared to the guide plate group(P < 0.05), highlighting the superior precision of navigation-assisted immediate implant placement. CONCLUSIONS: It is more advantageous to use dynamic navigation rather than a static guide plate and free-hand implant insertion for immediate posterior mandibular implant implantation.

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When anatomical landmarks are missing or obstructed by metal artefacts, it is challenging to accurately merge cone beam computed tomography (CBCT) and intraoral scanning (IOS) information, and the accuracy of the implant surgical guides would be compromised. This article describes a novel technical note using oral wound dressings and flowable resin as additional new radiopaque fiducial landmarks to design surgical guides for full-arch immediate implant placement. This technical note provided an accurate, convenient, and cost-effective option for the clinician.

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PURPOSE: The aim of the study was to assess the deviation between clinical implant axes (CIA) determined by a surgeon during preoperative planning and reconstructed tooth axes (RTA) of missing teeth which were automatically computed by a previously introduced anatomical SSM. METHODS: For this purpose all available planning datasets of single-implant cases of our clinic, which were planned with coDiagnostix Version 9.9 between 2018 and 2021, were collected for retrospective investigation. Informed consent was obtained. First, the intraoral scans of implant patients were annotated and subsequently analyzed using the SSM. The RTA, computed by the SSM, was then projected into the preoperative planning dataset. The amount and direction of spatial deviation between RTA and CIA were then measured. RESULTS: Thirty-five patients were implemented. The mean distance between the occlusal entry point of anterior and posterior implants and the RTA was 0.99 mm ± 0.78 mm and 1.19 mm ± 0.55, respectively. The mean angular deviation between the CIA of anterior and posterior implants and the RTA was 12.4° ± 3.85° and 5.27° ± 2.97° respectively. The deviations in anterior implant cases were systematic and could be corrected by computing a modified RTA (mRTA) with decreased deviations (0.99 mm ± 0.84 and 4.62° ± 1.95°). The safety distances of implants set along the (m)RTA to neighboring teeth were maintained in 30 of 35 cases. CONCLUSION: The RTA estimated by the SSM revealed to be a viable implant axis for most of the posterior implant cases. As there are natural differences between the anatomical tooth axis and a desirable implant axis, modifications were necessary to correct the deviations which occurred in anterior implant cases. However, the presented approach is not applicable for clinical use and always requires manual optimization by the planning surgeon.

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OBJECTIVES: To investigate the prevalence of incidental findings and need for further dental treatment and analyse the influence of size of field-of-view (FOV) and age in cone beam computed tomography (CBCT) for pre-implant planning. METHODS: 404 CBCT scans were examined retrospectively for incidental findings and need for further dental treatment. Incidental finding-frequencies and need for further treatment were assessed for different age (< 40 years, 40-60 years, > 60 years) and FOV groups (small, medium, large). Intraexaminer and interexaminer agreements were evaluated. RESULTS: In 82% of the scans at least one incidental finding was found, with a total of 766 overall. More incidental findings were found in scans with large FOV (98% vs. 72%, OR = 22.39 large vs. small FOV, p < 0.0001) and in scans of patients > 60 years (OR = 5.37 patient's age > 60 years vs. < 40 years, p = 0.0003). Further dental treatment due to incidental findings was needed in 31%. Scans with large FOV were more likely to entail further treatment (OR = 3.55 large vs. small FOV, p < 0.0001). Partial edentulism and large FOV were identified as risk factors for further treatment (p = 0.0003 and p < 0.0001). Further referral of the patient based on incidental findings was judged as indicated in 5%. Intra- and inter-examiner agreements were excellent (kappa = 0.944/0.805). CONCLUSIONS: A considerable number of incidental findings with need for further dental treatment was found in partially edentulous patients and in patients > 60 years. In pre-implant planning of elderly patients, the selection of large FOV CBCT scans, including dentoalveolar regions not X-rayed recently, help to detect therapeutically relevant incidental findings.

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PURPOSE: To compare the standard 360-degree CBCT acquisition protocol to the low dose 180-degree CBCT protocol for implant planning. MATERIALS AND 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.

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BACKGROUND: The success of any dental implant surgery depends on the correct diagnosis and treatment planning. PURPOSE: The aim of this study was to compare the dimensions of the alveolar ridge width using different techniques for implant placement. MATERIALS AND 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. CONCLUSION: 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.

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OBJECTIVES: 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.