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.

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.

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

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

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

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

Three-dimensional maxillary virtual patient creation by convolutional neural network-based segmentation on cone-beam computed tomography images.

OBJECTIVE: To qualitatively and quantitatively assess integrated segmentation of three convolutional neural network (CNN) models for the creation of a maxillary virtual patient (MVP) from cone-beam computed tomography (CBCT) images. MATERIALS AND METHODS: A dataset of 40 CBCT scans acquired with different scanning parameters was selected. Three previously validated individual CNN models were integrated to achieve a combined segmentation of maxillary complex, maxillary sinuses, and upper dentition. Two experts performed a qualitative assessment, scoring-integrated segmentations from 0 to 10 based on the number of required refinements. Furthermore, experts executed refinements, allowing performance comparison between integrated automated segmentation (AS) and refined segmentation (RS) models. Inter-observer consistency of the refinements and the time needed to create a full-resolution automatic segmentation were calculated. RESULTS: From the dataset, 85% scored 7-10, and 15% were within 3-6. The average time required for automated segmentation was 1.7 min. Performance metrics indicated an excellent overlap between automatic and refined segmentation with a dice similarity coefficient (DSC) of 99.3%. High inter-observer consistency of refinements was observed, with a 95% Hausdorff distance (HD) of 0.045 mm. CONCLUSION: The integrated CNN models proved to be fast, accurate, and consistent along with a strong interobserver consistency in creating the MVP. CLINICAL RELEVANCE: The automated segmentation of these structures simultaneously could act as a valuable tool in clinical orthodontics, implant rehabilitation, and any oral or maxillofacial surgical procedures, where visualization of MVP and its relationship with surrounding structures is a necessity for reaching an accurate diagnosis and patient-specific treatment planning.

Three-dimensional quantification of skeletal midfacial complex symmetry.

PURPOSE: Quantification of skeletal symmetry in a healthy population could have a strong impact on the reconstructive surgical procedures where mirroring of the contralateral healthy side acts as a clinical reference for the restoration of unilateral defects. Hence, the aim of this study was to three-dimensionally assess the symmetry of skeletal midfacial complex in skeletal class I patients. METHODS: A sample of 100 cone beam computed tomography (CBCT) scans (50 males, 50 females; age range: 19-40 years) were recruited. Automated segmentation of the skeletal midfacial complex was performed to create a three-dimensional (3D) virtual model using a convolutional neural network (CNN)-based segmentation tool. Thereafter, the segmented model was mirrored and registered to quantify skeletal symmetry using a color-coded conformance mapping based on a surface part comparison analysis. RESULTS: Overall, the mean and root-mean-square (RMS) differences between complete true and mirrored models were 0.14 ± 0.12 and 0.87 ± 0.21 mm, respectively. Female patients had a significantly more symmetrical midfacial complex (mean difference: 0.11 ± 0.1 mm, RMS: 0.81 ± 0.17 mm) compared to male patients (mean difference: 0.16 ± 0.13 mm, RMS: 0.94 ± 0.23 mm). No significant difference existed between left and right sides irrespective of the patient's gender. CONCLUSION: The comparison between true and mirrored complete and left/right split midfacial complex showed symmetry within a clinically acceptable range of 1 mm, which justifies the applicability of using the mirroring technique. The presented data could act as a reference guide for surgeons during planning of reconstructive surgical procedures and outcome assessment at follow-up.

Deep convolutional neural network-based automated segmentation of the maxillofacial complex from cone-beam computed tomography:A validation study.

OBJECTIVES: The present study investigated the accuracy, consistency, and time-efficiency of a novel deep convolutional neural network (CNN) based model for the automated maxillofacial bone segmentation from cone beam computed tomography (CBCT) images. METHOD: A dataset of 144 scans was acquired from two CBCT devices and randomly divided into three subsets: training set (n = 110), validation set (n = 10) and testing set (n = 24). A three-dimensional (3D) U-Net (CNN) model was developed, and the achieved automated segmentation was compared with a manual approach. RESULTS: The average time required for automated segmentation was 39.1 s with a 204-fold decrease in time consumption compared to manual segmentation (132.7 min). The model was highly accurate for identification of the bony structures of the anatomical region of interest with a dice similarity coefficient (DSC) of 92.6%. Additionally, the fully deterministic nature of the CNN model was able to provide 100% consistency without any variability. The inter-observer consistency for expert-based minor correction of the automated segmentation observed an excellent DSC of 99.7%. CONCLUSION: The proposed CNN model provided a time-efficient, accurate, and consistent CBCT-based automated segmentation of the maxillofacial complex. CLINICAL SIGNIFICANCE: Automated segmentation of the maxillofacial complex could act as an alternative to the conventional segmentation techniques for improving the efficiency of the digital workflows. This approach could deliver accurate and ready-to-print3D models, essential to patient-specific digital treatment planning for orthodontics, maxillofacial surgery, and implant dentistry.

Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images.

An accurate three-dimensional (3D) segmentation of the maxillary sinus is crucial for multiple diagnostic and treatment applications. Yet, it is challenging and time-consuming when manually performed on a cone-beam computed tomography (CBCT) dataset. Recently, convolutional neural networks (CNNs) have proven to provide excellent performance in the field of 3D image analysis. Hence, this study developed and validated a novel automated CNN-based methodology for the segmentation of maxillary sinus using CBCT images. A dataset of 264 sinuses were acquired from 2 CBCT devices and randomly divided into 3 subsets: training, validation, and testing. A 3D U-Net architecture CNN model was developed and compared to semi-automatic segmentation in terms of time, accuracy, and consistency. The average time was significantly reduced (p-value < 2.2e-16) by automatic segmentation (0.4 min) compared to semi-automatic segmentation (60.8 min). The model accurately identified the segmented region with a dice similarity co-efficient (DSC) of 98.4%. The inter-observer reliability for minor refinement of automatic segmentation showed an excellent DSC of 99.6%. The proposed CNN model provided a time-efficient, precise, and consistent automatic segmentation which could allow an accurate generation of 3D models for diagnosis and virtual treatment planning.

Impact of orthognathic surgery on root resorption: A systematic review.

OBJECTIVE: This systematic review was performed to assess the potential influence of orthognathic surgery on root resorption (RR). MATERIAL AND METHODS: An electronic search was conducted using PubMed, Web of Science, Cochrane Central and Embase for articles published up to April 2022. Following inclusion and exclusion criteria, a total of six articles were selected that reported on RR following orthognathic surgery. Risk of bias assessment was performed according to the ROBINS-1 and ROB-2 tools. RESULTS: The design of five studies was retrospective and one randomized clinical trial was included, with a follow-up period ranging between six months and ten years. The assessment methodologies mostly relied on two-dimensional imaging modalities where only one study used cone-beam computed tomography (CBCT) for objective quantification via linear measurements. The percentage of teeth affected by RR varied between approximately 1 and 36%, where surgically assisted rapid maxillary expansion (SARME) and Le Fort I osteotomy showed the highest percentage of RR followed by bilateral sagittal split osteotomy. CONCLUSIONS: The present data tend to indicate that specific orthognathic procedures such as SARME and Le Fort I osteotomy may induce or reinforce RR. Yet, considering lack of evidence related to objective quantification of RR following orthodontic and/or orthognathic treatment, further CBCT-based prospective studies are required for an improved understanding of RR following different surgical procedures.

Three-dimensional facial hard tissue symmetry in a healthy Caucasian population group: a systematic review.

OBJECTIVE: The aim of this study was to quantify the symmetry of the facial hard tissue structures using three-dimensional radiographic imaging modalities in a normal Caucasian population group. MATERIALS AND METHODS: Electronic literature search was conducted in the following databases: PubMed, Embase, Web of Science, and Cochrane Library up to February 2021. The studies assessing symmetry of facial bones using computed tomography (CT) and cone beam CT were included. RESULTS: The initial search revealed 8811 studies. Full-text analysis was performed on 33 studies. Only 10 studies were found eligible based on the inclusion criteria. The qualitative analysis revealed that a significant variability existed in relation to the methodologies applied for symmetry quantification. CONCLUSION: The current review suggested that the overall relative symmetry of the normal Caucasian population group varied depending on the skeletal structure being assessed; however, majority of the observations showed a symmetry within the range of 1 mm without any significant difference between left and right sides. CLINICAL RELEVANCE: The quantification of facial hard tissue structure symmetry is vital for the diagnosis and treatment planning of orthodontic and/or maxillofacial surgical procedures. Prospero registration number CRD42020169908.