Computer-assisted Treatment Research Articles

Automatic feature‐based markerless calibration and navigation method for augmented reality assisted dental treatment

AbstractAugmented reality (AR) is gaining traction in the field of computer‐assisted treatment (CAT). Head‐mounted display (HMD)‐based AR in CAT provides dentists with enhanced visualisation by directly overlaying a three‐dimensional (3D) model on a real patient during dental treatment. However, conventional AR‐based treatments rely on optical markers and trackers, which makes them tedious, expensive, and uncomfortable for dentists. Therefore, a markerless image‐to‐patient tracking system is necessary to overcome these challenges and enhance system efficiency. This paper proposes a novel feature‐based markerless calibration and navigation method for an HMD‐based AR visualisation system. The authors address three sub‐challenges: firstly, synthetic RGB‐D data for anatomical landmark detection is generated to train a deep convolutional neural network (DCNN); secondly, the HMD is automatically calibrated using detected anatomical landmarks, eliminating the need for user input or optical trackers; and thirdly, a multi‐iterative closest point (ICP) algorithm is developed for effective 3D‐3D real‐time navigation. The authors conduct several experiments on a commercially available HMD (HoloLens 2). Finally, the authors compare and evaluate the approach against state‐of‐the‐art methods that employ HoloLens. The proposed method achieves a calibration virtual‐to‐real re‐projection distance of (1.09 ± 0.23) mm and navigation projection errors and accuracies of approximately (0.53 ± 0.19) mm and 93.87%, respectively.

2D Medical Image Segmentation Combining Multi-Scale Channel Attention and Boundary Enhancement

2D medical image segmentation plays an important role in disease diagnosis and computer-assisted treatment. Aiming at the problem that 2D medical image is difficult to accurately segment due to target size, shape and blurred boundaries, a method combining multi-scale channel attention and boundary enhancement is proposed. First, 2D medical image is received as input, and the encoder and boundary enhancement module are used to extract the high-level feature maps and boundary segmentation result respectively. Then the multi-scale channel attention module extracts context information of different scales from the high-level feature map, enhances the useful features and suppresses the useless feature response. Finally, the context information obtained in the previous step is passed into the decoder to obtain the region segmentation result, and integrated with the boundary segmentation result to obtain the final segmentation result. In order to resolve the problem of data imbalance in medical images, a custom loss function is proposed. Experimental results on the four datasets of tooth panoramas containing residual roots, tooth panoramas containing caries, retinal vessels, and skin lesions show that the segmentation precision of proposed method has reached 85.63%, 70.15%, 75.86% and 85.92%, respectively. Compared with other medical image segmentation methods, proposed method performs better.

Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals.

Electroencephalography (EEG) records the electrical activity of the brain, which is an important tool for the automatic detection of epileptic seizures. It is certainly a very heavy burden to only recognize EEG epilepsy manually, so the method of computer-assisted treatment is of great importance. This paper presents a seizure detection algorithm based on variational modal decomposition (VMD) and a deep forest (DF) model. Variational modal decomposition is performed on EEG recordings, and the first three variational modal functions (VMFs) are selected to construct the time–frequency distribution of the EEG signals. Then, the log−Euclidean covariance matrix (LECM) is computed to represent the EEG properties and form EEG features. The deep forest model is applied to complete the EEG signal classification, which is a non-neural network deep model with a cascade structure that performs feature learning through the forest. In addition, to improve the classification accuracy, postprocessing techniques are performed to generate the discriminant results by moving average filtering and adaptive collar expansion. The algorithm was evaluated on the Bonn EEG dataset and the Freiburg long−term EEG dataset, and the former achieved a sensitivity and specificity of 99.32% and 99.31%, respectively. The mean sensitivity and specificity of this method for the 21 patients in the Freiburg dataset were 95.2% and 98.56%, respectively, with a false detection rate of 0.36/h. These results demonstrate the superior performance advantage of our algorithm and indicate its great research potential in epilepsy detection.

LOCL-08 SAFETY AND FEASIBILITY OF RHENIUM-186 NANOLIPOSOME (186RNL) IN RECURRENT GLIOMA: THE RESPECT™ PHASE 1 TRIAL

Abstract BACKGROUND Liposomal rhenium-186 (186RNL) is a potent source of electrons with short path length, low dose rate, high radiation density and gamma emission. Preclinically, 186RNL via convection enhanced delivery (CED) achieves very high doses of targeted radiation and a wide therapeutic index. We report the updated results of ReSPECT, the first in man, dose escalation phase 1 trial of 186RNL in recurrent glioma. METHODS Following computer assisted treatment planning and placement of intracranial catheter(s), we performed a single administration of 186RNL by CED. Whole body planar and SPECT/CT imaging was obtained on days 1-8 following treatment for dosimetry and distribution. Patients were followed for safety, progression and survival. RESULTS Twenty-one patients across 7 cohorts received 1.0-22.3mCi in a tumor volume of 0.6-8.80mL. Mean tumor volume was 8.3mL (0.9-22.8mL). Patients had a mean of 1.7 recurrences, 5 with prior bevacizumab. 19 (91%) were grade 4 gliomas, and 100% were after cohort 4. We used a CED rate of 5-20µl/min per catheter, with 1-4 catheters per patient. Tumor mean absorbed radiation dose was 255Gy (8.9-740Gy) while exposure outside the brain was negligible. The mean percentage tumor in the treated volume (Tu/Tv) was 60.3% (19.8%-100%). Thus far, we have observed no dose limiting toxicities, one grade 3 treatment related adverse event (AEs), and the majority of AEs were mild in intensity. The incidence and severity of AEs did not correlate with increasing dose. Mean Tu/Tv in patients not receiving prior bevacizumab was 75% vs. 48% in those that had. Thus far, overall survival (OS) in 16 bevacizumab naïve patient is 49 weeks with 7 patients still alive and a positive correlation of OS to Tu/Tv. CONCLUSIONS 186RNL achieves high absorbed doses without significant toxicity with favorable overall survival. Updated delivery feasibility, safety and overall survival will be presented.

Personalised statistical modelling of soft tissue structures in the ankle

Background and objectiveRevealing the complexity behind subject-specific ankle joint mechanics requires simultaneous analysis of three-dimensional bony and soft-tissue structures. 3D musculoskeletal models have become pivotal in orthopedic treatment planning and biomechanical research. Since manual segmentation of these models is time-consuming and subject to manual errors, (semi-) automatic methods could improve the accuracy and enlarge the sample size of personalised ‘in silico’ biomechanical experiments and computer-assisted treatment planning. Therefore, our aim was to automatically predict ligament paths, cartilage topography and thickness in the ankle joint based on statistical shape modelling. MethodsA personalised cartilage and ligamentous prediction algorithm was established using geometric morphometrics, based on an ‘in-house’ generated lower limb skeletal model (N = 542), tibiotalar cartilage (N = 60) and ankle ligament segmentations (N = 10). For cartilage, a population-averaged thickness map was determined by use of partial least-squares regression. Ligaments were wrapped around bony contours based on iterative shortest path calculation. Accuracy of ligament path and cartilage thickness prediction was quantified using leave-one-out experiments. The novel personalised thickness prediction was compared with a constant cartilage thickness of 1.50 mm by use of a paired sample T-test. ResultsMean distance error of cartilage and ligament prediction was 0.12 mm (SD 0.04 mm) and 0.54 mm (SD 0.05 mm), respectively. No significant differences were found between the personalised thickness cartilage and segmented cartilage of the tibia (p = 0.73, CI [-1.60 .10−17, 1.13 .10−17]) and talus (p = 0.95, CI[ -1.35 .10−17, 1.28 .10−17]). For the constant thickness cartilage, a statistically significant difference was found in 89% and 92% of the tibial (p < 0.001, CI [0.51, 0.58]) and talar (p < 0.001, CI [0.33, 0.40]) cartilage area. ConclusionsIn this study, we described a personalised prediction algorithm of cartilage and ligaments in the ankle joint. We were able to predict cartilage and main ankle ligaments with submillimeter accuracy. The proposed method has a high potential for generating large (virtual) sample sizes in biomechanical research and mitigates technological advances in computer-assisted orthopaedic surgery.

TMSF-Net: Multi-series fusion network with treeconnect for colorectal tumor segmentation

PurposeColorectal tumors are common clinical diseases. Automatic segmentation of colorectal tumors captured in computed tomography (CT) images can provide numerous possibilities for computer-assisted treatment. Obtaining large datasets is expensive, and completing labeling is time- and manpower-consuming. To solve the challenge using a limited pathological dataset, this paper proposes a multi-series fusion network with treeconnect (TMSF-Net), which can automatically achieve colorectal tumor segmentation using CT images. MethodsTo drive the TMSF-Net, three-series enhanced CT images were collected from all patients to improve the data characteristics. In the TMSF-Net, the coding path was designed as a three-branch structure to realize the feature extraction of the different series. Subsequently, the three branches were merged to start the feature analysis in the decoding path. To achieve the objective of feature fusion, different layers in the decoding path fused feature maps from the upper layer in the encoding path to achieve a cross-scale fusion. In addition, to reduce the problem of parameter redundancy, this study adopted a three-dimensional treeconnect to complete data connection on three branches. ResultsA total of 22 cases were conducted by ablation and comparative experiments to test the TMSF-Net. The results showed that the TMSF-Net can improve the network performance by multiseries fusion, and its expressiveness is better than many classic networks. ConclusionThe TMSF-Net is a many-to-one structure network, which can enhance the network learning ability and improve the analysis of potential features. Therefore, it yields good results in colorectal tumor segmentation. It can provide a new direction for neural network models based on feature fusion.

CTNI-29. SAFETY AND FEASIBILITY OF RHENIUM-186 NANOLIPOSOME (186RNL) IN RECURRENT GLIOMA: THE RESPECT™ PHASE 1 TRIAL

Abstract BACKGROUND Liposomal rhenium-186 (186RNL) is a potent source of beta particles with short path length, variable dose rate, high radiation density and gamma emission. Preclinically, 186RNL delivered via convection enhanced delivery (CED) achieves very high doses of targeted radiation with a wide therapeutic index. We report the results of ReSPECT, the first in man, dose escalation phase 1 trial of 186RNL in recurrent glioma. METHODS Following computer assisted treatment planning and placement of intracranial catheter(s), we performed a single administration of 186RNL by CED. We obtained whole body planar and SPECT/CT imaging on days 1-8 following treatment for dosimetry and distribution and followed patients for safety, progression and survival. RESULTS Twenty-one patients across 7 cohorts received 1.0-22.3mCi in a volume of 0.6-8.80mL. Mean tumor volume was 8.3mL (0.9-22.8mL). Patients had a mean of 1.7 recurrences, 5 received prior bevacizumab. Overall, 19/21 patients and all after cohort 4 had grade 4 glioma (glioblastoma). We used a CED rate of 5-20µl/min per catheter, with 1-4 catheters used per patient. Mean absorbed radiation dose to the tumor was 255Gy (8.9-740Gy) while exposure outside the brain was negligible. The mean percentage tumor in the treated volume (Tu/Tv) was 60.3% (19.8%-100%). Thus far, we have observed no dose limiting toxicities, one grade 3 treatment related adverse event (AEs), and the majority of AEs were mild in intensity. The incidence and severity of AEs did not correlate with increasing dose. Mean Tu/Tv in patients not receiving prior bevacizumab was 75% vs. 48% in those that had. Thus far, overall survival (OS) in 16 bevacizumab naïve patients is 49 weeks with 7 (44%) patients still alive and a positive correlation of OS to Tu/Tv. CONCLUSIONS 186RNL achieves high absorbed doses without significant toxicity with favorable overall survival.

Classification of Individual’s discrete emotions reflected in facial microexpressions using electroencephalogram and facial electromyogram

Facial microexpressions are defined as brief, subtle, and involuntary movements of facial muscles reflecting genuine emotions that a person tries to conceal. Because microexpressions are involuntary and uncontrollable, automatic detection of microexpressions and recognition of emotions reflected in the microexpressions can be used in various applications. With the advancement of artificial-intelligence-based non-face-to-face interviews and computer-assisted treatment of mood disorders, the need for developing a technique to precisely detect microexpressions is gradually increasing. In this study, we developed facial electromyography (fEMG)- and electroencephalography (EEG)-based methods for the detection of microexpressions and recognition of emotions reflected in microexpressions as a potential alternative to computer vision-based methods. We first assessed the performance of microexpression detection, and then evaluated the performance of classification of the emotions reflected in the microexpressions. In our experiments with 16 participants, six discrete emotions could be classified using support vector machine with the best F1 score of 0.971 when optimal fEMG and EEG channels were selected, demonstrating the potential usability of the fEMG- and EEG-based emotion recognition method in practical scenarios. It is noteworthy that EEG was more useful for classifying discrete emotions compared to fEMG (best F1 scores: EEG–0.962; fEMG–0.797). To the best of our knowledge, this is the first study to estimate emotions reflected in facial microexpressions using EEG.

DGFAU-Net: Global feature attention upsampling network for medical image segmentation

Medical image segmentation plays an important role in many clinical medicines, such as medical diagnosis and computer-assisted treatment. However, due to the large quality differences, variable lesion areas and their complex shapes, medical image segmentation is a very challenging task. However, most of the recent deep learning methods ignore the global context information as well as the receptive fields of pixels and do not consider the reuse of pixel features during the feature extraction stage. In this paper, we propose DGFAU-Net, an encoder–decoder structured 2D segmentation model, to overcome the shortcomings aforementioned. In the encoder, DenseNet and AtrousCNN networks are leveraged to extract image features. The DenseNet network is mainly used to achieve the reuse of pixel features, and AtrousCNN is utilized to enhance the receptive field of pixels. In the decoder, two modules, global feature attention upsample (GFAU) and pyramid pooling attention squeeze-excitation (PPASE), are proposed. GFAU combines low-level and high-level features to generate new features with richer information for improving the perceptions of global contextual information of pixels. PPASE employs a multi-scale pooling layer to enhance the pixel’s acceptance field. In addition, Focal loss is used to balance the difference between samples of the lesion and non-lesioned areas. Extensive experiments are conducted on one local dataset and two public datasets, which are the local dataset of MRI images of carotid plaque, DRIVE vessel segmentation dataset and CHASE_DB1 vessel segmentation dataset, and the experimental results demonstrate the effectiveness of our proposed model.

Associations between baseline opioid use disorder severity, mental health and biopsychosocial functioning, with clinical responses to computer-assisted therapy treatment

ABSTRACT Background: Increasing rates of opioid-related overdose have been identified globally. Treatment for opioid use disorders (OUD) includes medications for opioid use disorder (MOUD) alongside behavioral support. Novel approaches to behavioral support should be explored, including computer-assisted therapy (CAT) programs. Objectives: Examine differences between baseline and post-treatment measures of opioid use and biopsychosocial functioning for individuals with OUD engaging with the CAT program ‘Breaking Free Online,’ and the extent to which participant characteristics may be associated with post-treatment measures. Methods: 1107 individuals engaged with CAT and provided baseline and post-treatment data – 724 (65.4%) were male, 383 (34.6%) were female. Results: Significant differences between baseline and post-treatment measures were identified (all p <.0001, effect sizes range:15 –.50). Participant characteristics were associated with post-treatment measures of opioid use, opioid dependence, mental health issues, quality of life, and biopsychosocial impairment (all p <.0001). An aggregated consensus measure of clinical impairment was found to be associated with changes in opioid use and post-treatment biopsychosocial functioning measures, with those participants with greater baseline clinical impairment demonstrating a greater magnitude of improvement from baseline to post-treatment than those with lower clinical impairment. Conclusion: CAT may reduce opioid use and improve biopsychosocial functioning in individuals with OUD. CAT could therefore provide a solution to the global opioid crisis if delivered as combination behavioral support alongside MOUD. Findings also indicate that it may be important for treatment systems to identify individuals with psychosocial complexity who might require behavioral support and MOUD.

Towards real-time finite-strain anisotropic thermo-visco-elastodynamic analysis of soft tissues for thermal ablative therapy

Background and objectivesAccurate and efficient prediction of soft tissue temperatures is essential to computer-assisted treatment systems for thermal ablation. It can be used to predict tissue temperatures and ablation volumes for personalised treatment planning and image-guided intervention. Numerically, it requires full nonlinear modelling of the coupled computational bioheat transfer and biomechanics, and efficient solution procedures; however, existing studies considered the bioheat analysis alone or the coupled linear analysis, without the fully coupled nonlinear analysis. MethodsWe present a coupled thermo-visco-hyperelastic finite element algorithm, based on finite-strain thermoelasticity and total Lagrangian explicit dynamics. It considers the coupled nonlinear analysis of (i) bioheat transfer under soft tissue deformations and (ii) soft tissue deformations due to thermal expansion/shrinkage. The presented method accounts for anisotropic, finite-strain, temperature-dependent, thermal, and viscoelastic behaviours of soft tissues, and it is implemented using GPU acceleration for real-time computation. ResultsThe presented method can achieve thermo-visco-elastodynamic analysis of anisotropic soft tissues undergoing large deformations with high computational speeds in tetrahedral and hexahedral finite element meshes for surgical simulation of thermal ablation. We also demonstrate the translational benefits of the presented method for clinical applications using a simulation of thermal ablation in the liver. ConclusionThe key advantage of the presented method is that it enables full nonlinear modelling of the anisotropic, finite-strain, temperature-dependent, thermal, and viscoelastic behaviours of soft tissues, instead of linear elastic, linear viscoelastic, and thermal-only modelling in the existing methods. It also provides high computational speeds for computer-assisted treatment systems towards enabling the operator to simulate thermal ablation accurately and visualise tissue temperatures and ablation zones immediately.

Information Technology-Assisted Treatment Planning and Performance Assessment for Severe Thalassemia Care in Low- and Middle-Income Countries: Observational Study.

BackgroundSuccessful models of information and communication technology (ICT) applied to cost-effective delivery of quality care in low- and middle-income countries (LMIC) are an increasing necessity. Severe thalassemia is one of the most common life-threatening noncommunicable diseases of children globally.ObjectiveThe aim was to study the impact of ICT on quality of care for severe thalassemia patients in LMIC.MethodsA total of 1110 patients with severe thalassemia from five centers in India were followed over a 1-year period. The impact of consistent use of a Web-based platform designed to assist comprehensive management of severe thalassemia (ThalCare) on key indicators of quality of care such as minimum (pretransfusion) hemoglobin, serum ferritin, liver size, and spleen size were assessed.ResultsOverall improvements in initial hemoglobin, ferritin, and liver and spleen size were significant (P<.001 for each). For four centers, the improvement in mean pretransfusion hemoglobin level was statistically significant (P<.001). Four of five centers achieved reduction in mean ferritin levels, with two displaying a significant drop in ferritin (P=.004 and P<.001). One of the five centers did not record liver and spleen size on palpation, but of the remaining four centers, two witnessed a large drop in liver and spleen size (P<.01), one witnessed moderate drop (P=.05 for liver; P=.03 for spleen size), while the fourth witnessed a moderate increase in liver size (P=.08) and insignificant change in spleen size (P=.12).ConclusionsImplementation of computer-assisted treatment planning and performance assessment consistently and positively impacted indexes reflecting effective delivery of care to patients suffering from severe thalassemia in LMIC.

Extraction Method of Coronary Artery Blood Vessel Centerline in CT Coronary Angiography

Aiming at the problem that the traditional coronary artery centerline extraction method is computationally intensive and requires a large number of manual interventions, a fully automatic coronary artery centerline tracking extraction method is proposed. First, adaptive adaptation of coronary vessels was performed based on different Fresenius norms designed for coronary scale. A multi-scale differential operator is constructed based on the vascular gray-scale distribution to establish a discriminant function, thereby obtaining the initial position and tracking direction of the seed point. Then, the new ridge point and tracking direction are optimally detected within the local arc length of the initial direction to ensure that the correct ridge position can still be obtained with large error interference. Finally, the pseudo vascular centerline is removed using the vascular topology feature detection method. The experimental results show that the method can accurately extract the blood vessel center line, direction vector and other information in the coronary angiography image without manual intervention, and can be used in the computer-assisted diagnosis and treatment process of clinical cardiovascular disease.

The smiling scan technique: Facially driven guided surgery and prosthetics

PurposeTo introduce a proof of concept technique and new integrated workflow to optimize the functional and esthetic outcome of the implant-supported restorations by means of a 3-dimensional (3D) facially-driven, digital assisted treatment plan. MethodsThe Smiling Scan technique permits the creation of a virtual dental patient (VDP) showing a broad smile under static conditions. The patient is exposed to a cone beam computed tomography scan (CBCT), displaying a broad smile for the duration of the examination. Intraoral optical surface scanning (IOS) of the dental and soft tissue anatomy or extraoral optical surface scanning (EOS) of the study casts are achieved. The superimposition of the digital imaging and communications in medicine (DICOM) files with standard tessellation language (STL) files is performed using the virtual planning software program permitting the creation of a VDP. ConclusionsThe smiling scan is an effective, easy to use, and low-cost technique to develop a more comprehensive and simplified facially driven computer-assisted treatment plan, allowing a prosthetically driven implant placement and the delivery of an immediate computer aided design (CAD) computer aided manufacturing (CAM) temporary fixed dental prostheses (CAD/CAM technology).

Proposal of Computer Assisted Treatment of Morphological Disorders of Mandibular Structure

This article presents a proposal of computer assisted treatment of morphological disorders of mandibular bone structure. Cephalometric analysis according to Schwarz was conducted with the application of CorelDRAW software, generally available software for editing of vector graphics and an alternative to expensive, specialized software dedicated for computer assisted pre-operational planning. An approach to cranio- and gnatometric measurements based on cephalometric planes, lines and points is presented. Thanks to the proposed approach to computer assisted planning of treatment, cephalometric analysis is more precise and faster in comparison to traditional analysis conducted manually on a radiograph.

Feasibility and effectiveness of cognitive remediation in the treatment of borderline personality disorder

ABSTRACTSeveral studies have demonstrated that borderline personality disorder (BPD) is associated with neuropsychological deficits and there is evidence that the neurocognitive profile of patients with BPD may be related to the outcome of this disorder. The aim of this study was to investigate the feasibility and the effectiveness of a cognitive remediation intervention in patients with BPD. Thirty patients with a DSM-IV-TR diagnosis of BPD were assessed on clinical, neuropsychological and functional outcome measures at baseline and after 16 weeks of a computer-assisted cognitive remediation (CACR) intervention or treatment as usual (TAU). Patients who received CACR showed a greater improvement in working memory and psychosocial functioning measures than patients treated with TAU. Symptom severity was not significantly affected by CACR treatment. The findings of this pilot study suggest the feasibility and potential effectiveness on specific cognitive domains, but modest clinical usefulness of a computerised modality of cognitive remediation in the treatment of BPD.

Developing a Prototype Naming Therapy Application in Malay for People with Aphasia

Developing a Prototype Naming Therapy Application in Malay for People with Aphasia

Development and evaluation of an online tool for management of overweight children in primary care: a pilot study

ObjectiveTo explore the acceptability of implementing an online tool for the assessment and management of childhood obesity (Computer-Assisted Treatment of CHildren, CATCH) in primary care.Design and settingAn uncontrolled pilot study...

A 3D computer-assisted treatment planning system for breast cancer brachytherapy treatment.

Purpose Brachytherapy is an option for treatment of breast cancer in some cases. This modality requires patient-specific dosimetry based on CT simulator scans. A 3D computer-assisted breast brachytherapy treatment planning system called Vision was developed and tested.Methods The brachytherapy treatment planning system used volume estimation and dose analysis with advanced 3D visualization. The patient treatment volume reconstruction was designed to ensure high-volume accuracy requirement of radioactive seed implantation procedure for this treatment. The system enables interactive placement of radioactive seeds embedded in original patient CT images with 3D display.Results The system achieved 99.73 % accuracy in volume estimation measured against the true volume and is statistically significantly more accurate than current existing commercial software at the p=0.05 level.Conclusion A virtual 3D environment was developed to perform volume measurements, seed placements, and dose distribution planning and analysis based on 2D contours on patient CT images. This system was demonstrated to be feasible and accurate in a clinical setting.

Cone Beam Computed Tomography in Implant Dentistry: A Systematic Review Focusing on Guidelines, Indications, and Radiation Dose Risks

The aim of the paper is to identify, review, analyze, and summarize available evidence in three areas on the use of cross-sectional imaging, specifically maxillofacial cone beam computed tomography (CBCT) in pre- and postoperative dental implant therapy: (1) Available clinical use guidelines, (2) indications and contraindications for use, and (3) assessment of associated radiation dose risk. Three focused questions were developed to address the aims. A systematic literature review was performed using a PICO-based search strategy based on MeSH key words specific to each focused question of English-language publications indexed in the MEDLINE database retrospectively from October 31, 2012. These results were supplemented by a hand search and gray literature search. Twelve publications were identified providing guidelines for the use of cross-sectional radiography, particularly CBCT imaging, for the pre- and/or postoperative assessment of potential dental implant sites. The publications discovered by the PICO strategy (43 articles), hand (12), and gray literature searches (1) for the second focus question regarding indications and contraindications for CBCT use in implant dentistry were either cohort or case-controlled studies. For the third question on the assessment of associated radiation dose risk, a total of 22 articles were included. Publication characteristics and themes were summarized in tabular format. The reported indications for CBCT use in implant dentistry vary from preoperative analysis regarding specific anatomic considerations, site development using grafts, and computer-assisted treatment planning to postoperative evaluation focusing on complications due to damage of neurovascular structures. Effective doses for different CBCT devices exhibit a wide range with the lowest dose being almost 100 times less than the highest dose. Significant dose reduction can be achieved by adjusting operating parameters, including exposure factors and reducing the field of view (FOV) to the actual region of interest.