Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review

Unveiling the power of artificial intelligence for image-based diagnosis and treatment in endodontics: An ally or adversary?

BACKGROUND

Artificial intelligence (AI), a field within computer science, uses algorithms to replicate human intelligence tasks such as pattern recognition, decision-making and problem-solving through complex datasets. In endodontics, AI is transforming diagnosis and treatment by applying deep learning algorithms, notably convolutional neural networks, which mimic human brain function to analyse two-dimensional (2D) and three-dimensional (3D) data.

OBJECTIVES

This article provides an overview of AI applications in endodontics, evaluating its use in 2D and 3D imaging and examining its role as a beneficial tool or potential challenge.

METHODS

Through a narrative review, the article explores AI's use in 2D and 3D imaging modalities, discusses their limitations and examines future directions in the field.

RESULTS

AI significantly enhances endodontic practice by improving diagnostic accuracy, workflow efficiency, and treatment planning. In 2D imaging, AI excels at detecting periapical lesions on both periapical and panoramic radiographs, surpassing expert radiologists in accuracy, sensitivity and specificity. AI also accurately detects and classifies radiolucent lesions, such as radicular cysts and periapical granulomas, matching the precision of histopathology analysis. In 3D imaging, AI automates the segmentation of fine structures such as pulp chambers and root canals on cone-beam computed tomography scans, thereby supporting personalized treatment planning. However, a significant limitation highlighted in some studies is the reliance on in vitro or ex vivo datasets for training AI models. These datasets do not replicate the complexities of clinical environments, potentially compromising the reliability of AI applications in endodontics.

DISCUSSION

Despite advancements, challenges remain in dataset variability, algorithm generalization, and ethical considerations such as data security and privacy. Addressing these is essential for integrating AI effectively into clinical practice and unlocking its transformative potential in endodontic care. Integrating radiomics with AI shows promise for enhancing diagnostic accuracy and predictive analytics, potentially enabling automated decision support systems to enhance treatment outcomes and patient care.

CONCLUSIONS

Although AI enhances endodontic capabilities through advanced imaging analyses, addressing current limitations and fostering collaboration between AI developers and dental professionals are essential. These efforts will unlock AI's potential to achieve more predictable and personalized treatment outcomes in endodontics, ultimately benefiting both clinicians and patients alike.

Assessment of hard tissue changes after horizontal guided bone regeneration with the aid of deep learning CBCT segmentation

OBJECTIVES

To investigate the performance of a deep learning (DL) model for segmenting cone-beam computed tomography (CBCT) scans taken before and after mandibular horizontal guided bone regeneration (GBR) to evaluate hard tissue changes.

MATERIALS AND METHODS

The proposed SegResNet-based DL model was trained on 70 CBCT scans. It was tested on 10 pairs of pre- and post-operative CBCT scans of patients who underwent mandibular horizontal GBR. DL segmentations were compared to semi-automated (SA) segmentations of the same scans. Augmented hard tissue segmentation performance was evaluated by spatially aligning pre- and post-operative CBCT scans and subtracting preoperative segmentations obtained by DL and SA segmentations from the respective postoperative segmentations. The performance of DL compared to SA segmentation was evaluated based on the Dice similarity coefficient (DSC), intersection over the union (IoU), Hausdorff distance (HD95), and volume comparison.

RESULTS

The mean DSC and IoU between DL and SA segmentations were 0.96 ± 0.01 and 0.92 ± 0.02 in both pre- and post-operative CBCT scans. While HD95 values between DL and SA segmentations were 0.62 mm ± 0.16 mm and 0.77 mm ± 0.31 mm for pre- and post-operative CBCTs respectively. The DSC, IoU and HD95 averaged 0.85 ± 0.08; 0.78 ± 0.07 and 0.91 ± 0.92 mm for augmented hard tissue models respectively. Volumes mandible- and augmented hard tissue segmentations did not differ significantly between the DL and SA methods.

CONCLUSIONS

The SegResNet-based DL model accurately segmented CBCT scans acquired before and after mandibular horizontal GBR. However, the training database must be further increased to increase the model's robustness.

CLINICAL RELEVANCE

Automated DL segmentation could aid treatment planning for GBR and subsequent implant placement procedures and in evaluating hard tissue changes.

Performance of the ChatGPT-3.5, ChatGPT-4, and Google Gemini large language models in responding to dental implantology inquiries

STATEMENT OF PROBLEM

Artificial intelligence (AI) chatbots have been proposed as promising resources for oral health information. However, the quality and readability of existing online health-related information is often inconsistent and challenging.

PURPOSE

This study aimed to compare the reliability and usefulness of dental implantology-related information provided by the ChatGPT-3.5, ChatGPT-4, and Google Gemini large language models (LLMs).

MATERIAL AND METHODS

A total of 75 questions were developed covering various dental implant domains. These questions were then presented to 3 different LLMs: ChatGPT-3.5, ChatGPT-4, and Google Gemini. The responses generated were recorded and independently assessed by 2 specialists who were blinded to the source of the responses. The evaluation focused on the accuracy of the generated answers using a modified 5-point Likert scale to measure the reliability and usefulness of the information provided. Additionally, the ability of the AI-chatbots to offer definitive responses to closed questions, provide reference citation, and advise scheduling consultations with a dental specialist was also analyzed. The Friedman, Mann Whitney U and Spearman Correlation tests were used for data analysis (α=.05).

RESULTS

Google Gemini exhibited higher reliability and usefulness scores compared with ChatGPT-3.5 and ChatGPT-4 (P<.001). Google Gemini also demonstrated superior proficiency in identifying closed questions (25 questions, 41%) and recommended specialist consultations for 74 questions (98.7%), significantly outperforming ChatGPT-4 (30 questions, 40.0%) and ChatGPT-3.5 (28 questions, 37.3%) (P<.001). A positive correlation was found between reliability and usefulness scores, with Google Gemini showing the strongest correlation (ρ=.702).

CONCLUSIONS

The 3 AI Chatbots showed acceptable levels of reliability and usefulness in addressing dental implant-related queries. Google Gemini distinguished itself by providing responses consistent with specialist consultations.

Bibliometric Analysis of the Role of Artificial Intelligence in Detecting Maxillofacial Fractures

Facial bone fractures are a common occurrence in trauma cases, particularly in India where road traffic accidents contribute significantly. Over the past few years, artificial intelligence (AI) has become a potent instrument to help medical professionals diagnose and treat facial fractures. This study aims to perform a bibliometric analysis, that is, a quantitative and qualitative analysis, of publications focusing on the role of AI in detecting facial bone fractures. Bibliometric analysis can be a very strong measure of research productivity and analysis of trends within a given area of research. Data were drawn from the Dimensions AI database; 58 relevant scientific articles were analyzed in this study. This bibliometric analysis aims to assess the volume of research in this area, identifying key trends, authors, institutions, and countries contributing to the literature. The Dimensions AI database was used to gather and analyze relevant data, shedding light on the research impact through indicators such as the h-index, citation counts, and publication trends. This review will depict the landscape of research work, highlighting the rising influence of the use of AI for accurate diagnostics in facial bone fractures and further detailing gaps and potential avenues of future research directed toward solutions such as standardizing datasets and clinical integration.

Comparative Analysis of 3D Cephalometry Provided with Artificial Intelligence and Manual Tracing

OBJECTIVES

To compare 3D cephalometric analysis performed using AI with that conducted manually by a specialist orthodontist.

METHODS

The CBCT scans (a field of view of 15 × 15 cm) used in the study were obtained from 30 consecutive patients, aged 18 to 50. The 3D cephalometric analysis was conducted using two methods. The first method involved manual tracing performed with the Invivo 6 software (Anatomage Inc., Santa Clara, CA, USA). The second method involved using AI for cephalometric measurements as part of an orthodontic report generated by the Diagnocat system (Diagnocat Ltd., San Francisco, CA, USA).

RESULTS

A statistically significant difference within one standard deviation of the parameter was found in the following measurements: SNA, SNB, and the left interincisal angle. Statistically significant differences within two standard deviations were noted in the following measurements: the right and left gonial angles, the left upper incisor, and the right lower incisor. No statistically significant differences were observed beyond two standard deviations.

CONCLUSIONS

AI in the form of Diagnocat proved to be effective in assessing the mandibular growth direction, defining the skeletal class, and estimating the overbite, overjet, and Wits parameter.

[Strategies for prevention and treatment of vascular and nerve injuries in mandibular anterior implant surgery]

Important anatomical structures such as mandibular incisive canal, tongue foramen, and mouth floor vessels may be damaged during implant surgery in the mandibular anterior region, which may lead to mouth floor hematoma, asphyxia, pain, paresthesia and other symptoms. In severe cases, this can be life-threatening. The insufficient alveolar bone space and the anatomical variation of blood vessels and nerves in the mandibular anterior region increase the risk of blood vessel and nerve injury during implant surgery. In case of vascular injury, airway control and hemostasis should be performed, and in case of nerve injury, implant removal and early medical treatment should be performed. To avoid vascular and nerve injury during implant surgery in the mandibular anterior region, it is necessary to be familiar with the anatomical structure, take cone-beam computed tomography, design properly before surgery, and use digital technology during surgery to achieve accurate implant placement. This article summarizes the anatomical structure of the mandibular anterior region, discusses the prevention strategies of vascular and nerve injuries in this region, and discusses the treatment methods after the occurrence of vascular and nerve injuries, to provide clinical reference.

Automating Dental Condition Detection on Panoramic Radiographs: Challenges, Pitfalls, and Opportunities

Background/Objectives: The integration of AI into dentistry holds promise for improving diagnostic workflows, particularly in the detection of dental pathologies and pre-radiotherapy screening for head and neck cancer patients. This study aimed to develop and validate an AI model for detecting various dental conditions, with a focus on identifying teeth at risk prior to radiotherapy. Methods: A YOLOv8 model was trained on a dataset of 1628 annotated panoramic radiographs and externally validated on 180 radiographs from multiple centers. The model was designed to detect a variety of dental conditions, including periapical lesions, impacted teeth, root fragments, prosthetic restorations, and orthodontic devices. Results: The model showed strong performance in detecting implants, endodontic treatments, and surgical devices, with precision and recall values exceeding 0.8 for several conditions. However, performance declined during external validation, highlighting the need for improvements in generalizability. Conclusions: YOLOv8 demonstrated robust detection capabilities for several dental conditions, especially in training data. However, further refinement is needed to enhance generalizability in external datasets and improve performance for conditions like periapical lesions and bone loss.

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.

An Examination of Temporomandibular Joint Disc Displacement through Magnetic Resonance Imaging by Integrating Artificial Intelligence: Preliminary Findings

Background and Objectives: This research was aimed at constructing a complete automated temporomandibular joint disc position identification system that could assist with magnetic resonance imaging disc displacement diagnosis on oblique sagittal and oblique coronal images. Materials and Methods: The study included fifty subjects with magnetic resonance imaging scans of the temporomandibular joint. Oblique sagittal and coronal sections of the magnetic resonance imaging scans were analyzed. Investigations were performed on the right and left coronal images with a closed mouth, as well as right and left sagittal images with closed and open mouths. Three hundred sagittal and coronal images were employed to train the artificial intelligence algorithm. Results: The accuracy ratio of the completely computerized articular disc identification method was 81%. Conclusions: An automated and accurate evaluation of temporomandibular joint disc position was developed by using both oblique sagittal and oblique coronal magnetic resonance imaging images.

Determination of the pubertal growth spurt by artificial intelligence analysis of cervical vertebrae maturation in lateral cephalometric radiographs

OBJECTIVE

This study aimed to assess the performance of a convolutional neural network (CNN) model in detecting the pubertal growth spurt by analyzing cervical vertebrae maturation (CVM) in lateral cephalometric radiographs (LCRs).

STUDY DESIGN

In total, 600 LCRs of patients from 6 to 17 years old were selected. Three radiologists independently and blindly classified the maturation stages of the LCRs and defined the difficulty of each classification. Subsequently, the stage and level of difficulty were determined by consensus. LCRs were distributed between training, validation, and test datasets across 4 CNN-based models. The models' responses were compared with the radiologists' reference standard, and the architecture with the highest success rate was selected for evaluation. Models were developed using full and cropped LCRs with original and simplified maturation classifications.

RESULTS

In the simplified classification, the Inception-v3 CNN yielded an accuracy of 74% and 75%, with recall and precision values of 61% and 62%, for full and cropped LCRs, respectively. It achieved 61% and 62% total success rates with full and cropped LCRs, respectively, reaching 72.7% for easy-to-classify cropped cases.

CONCLUSION

Overall, the CNN model demonstrated potential for determining the maturation status regarding the pubertal growth spurt through images of the cervical vertebrae. It may be useful as an initial assessment tool or as an aid for optimizing the assessment and treatment decisions of the clinician.

Robot-assisted dental implant surgery procedure: A literature review

Robot-assisted dental surgery has gained significant attention in the field of dental implant therapy as an alternative to conventional free-hand surgery. It addresses challenges faced by human operators, such as limited visibility, operator fatigue, and lack of experience, which can lead to errors. Dental implant robots offer improved precision, efficiency, and stability, enhancing implant accuracy and reducing surgical risks. Accurate placement of dental implants is crucial to avoid complications during and after surgery. Robotic guidance in dental implant surgery provides several benefits. Firstly, the robotic arm offers haptic feedback, allowing physical guidance when placing the implant in the desired position. Secondly, a patient tracker integrated into the robotic system monitors patient movement and provides real-time feedback on a screen. This feature ensures that the surgeon is aware of any changes and can adjust accordingly. Lastly, the robotic system operates under human-robot collaboration, with the surgeon maintaining control and oversight throughout the procedure. Therefore, the objective of the current study is to review the dental implant robots, as well as accuracy and efficiency (e.g. operation and preparation time) of robot-assisted dental implant surgery procedures.

Evaluation of deep learning for detecting intraosseous jaw lesions in cone beam computed tomography volumes

OBJECTIVE

The study aim was to develop and assess the performance of a deep learning (DL) algorithm in the detection of radiolucent intraosseous jaw lesions in cone beam computed tomography (CBCT) volumes.

STUDY DESIGN

A total of 290 CBCT volumes from more than 12 different scanners were acquired. Fields of view ranged from 6 × 6 × 6 cm to 18 × 18 × 16 cm. CBCT volumes contained either zero or at least one biopsy-confirmed intraosseous lesion. 80 volumes with no intraosseous lesions were included as controls and were not annotated. 210 volumes with intraosseous lesions were manually annotated using ITK-Snap 3.8.0. 150 volumes (10 control, 140 positive) were presented to the DL software for training. Validation was performed using 60 volumes (30 control, 30 positive). Testing was performed using the remaining 80 volumes (40 control, 40 positive).

RESULTS

The DL algorithm obtained an adjusted sensitivity by case, specificity by case, positive predictive value by case, and negative predictive value by case of 0.975, 0.825, 0.848, and 0.971, respectively.

CONCLUSIONS

A DL algorithm showed moderate success at lesion detection in their correct locations, as well as recognition of lesion shape and extent. This study demonstrated the potential of DL methods for intraosseous lesion detection in CBCT volumes.

Radiographic diagnosis of periodontal diseases - Current evidence versus innovations

Accurate diagnosis of periodontal and peri-implant diseases relies significantly on radiographic examination, especially for assessing alveolar bone levels, bone defect morphology, and bone quality. This narrative review aimed to comprehensively outline the current state-of-the-art in radiographic diagnosis of alveolar bone diseases, covering both two-dimensional (2D) and three-dimensional (3D) modalities. Additionally, this review explores recent technological advances in periodontal imaging diagnosis, focusing on their potential integration into clinical practice. Clinical probing and intraoral radiography, while crucial, encounter limitations in effectively assessing complex periodontal bone defects. Recognizing these challenges, 3D imaging modalities, such as cone beam computed tomography (CBCT), have been explored for a more comprehensive understanding of periodontal structures. The significance of the radiographic assessment approach is evidenced by its ability to offer an objective and standardized means of evaluating hard tissues, reducing variability associated with manual clinical measurements and contributing to a more precise diagnosis of periodontal health. However, clinicians should be aware of challenges related to CBCT imaging assessment, including beam-hardening artifacts generated by the high-density materials present in the field of view, which might affect image quality. Integration of digital technologies, such as artificial intelligence-based tools in intraoral radiography software, the enhances the diagnostic process. The overarching recommendation is a judicious combination of CBCT and digital intraoral radiography for enhanced periodontal bone assessment. Therefore, it is crucial for clinicians to weigh the benefits against the risks associated with higher radiation exposure on a case-by-case basis, prioritizing patient safety and treatment outcomes.

Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images

OBJECTIVES

The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model.

METHODS

In 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into 3 parts: 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, area under curve (AUC), Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) values.

RESULTS

F1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96, respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively.

CONCLUSIONS

Models based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.

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

OBJECTIVES

To conduct a scoping review focusing on artificial intelligence (AI) applications in presurgical dental implant planning. Additionally, to assess the automation degree of clinically available pre-surgical implant planning software.

DATA AND SOURCES

A systematic electronic literature search was performed in five databases (PubMed, Embase, Web of Science, Cochrane Library, and Scopus), along with exploring gray literature web-based resources until November 2023. English-language studies on AI-driven tools for digital implant planning were included based on an independent evaluation by two reviewers. An assessment of automation steps in dental implant planning software available on the market up to November 2023 was also performed.

STUDY SELECTION AND RESULTS

From an initial 1,732 studies, 47 met eligibility criteria. Within this subset, 39 studies focused on AI networks for anatomical landmark-based segmentation, creating virtual patients. Eight studies were dedicated to AI networks for virtual implant placement. Additionally, a total of 12 commonly available implant planning software applications were identified and assessed for their level of automation in pre-surgical digital implant workflows. Notably, only six of these featured at least one fully automated step in the planning software, with none possessing a fully automated implant planning protocol.

CONCLUSIONS

AI plays a crucial role in achieving accurate, time-efficient, and consistent segmentation of anatomical landmarks, serving the process of virtual patient creation. Additionally, currently available systems for virtual implant placement demonstrate different degrees of automation. It is important to highlight that, as of now, full automation of this process has not been documented nor scientifically validated.

CLINICAL SIGNIFICANCE

Scientific and clinical validation of AI applications for presurgical dental implant planning is currently scarce. The present review allows the clinician to identify AI-based automation in presurgical dental implant planning and assess the potential underlying scientific validation.

STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning

Accurate tooth segmentation and numbering are the cornerstones of efficient automatic dental diagnosis and treatment. In this paper, a multitask learning architecture has been proposed for accurate tooth segmentation and numbering in panoramic X-ray images. A graph convolution network was applied for the automatic annotation of the target region, a modified convolutional neural network-based detection subnetwork (DSN) was used for tooth recognition and boundary regression, and an effective region segmentation subnetwork (RSSN) was used for region segmentation. The features extracted using RSSN and DSN were fused to optimize the quality of boundary regression, which provided impressive results for multiple evaluation metrics. Specifically, the proposed framework achieved a top F1 score of 0.9849, a top Dice metric score of 0.9629, and an mAP (IOU = 0.5) score of 0.9810. This framework holds great promise for enhancing the clinical efficiency of dentists in tooth segmentation and numbering tasks.

Are Artificial Intelligence-Assisted Three-Dimensional Histological Reconstructions Reliable for the Assessment of Trabecular Microarchitecture?

Objectives: This study aimed to create a three-dimensional histological reconstruction through the AI-assisted classification of tissues and the alignment of serial sections. The secondary aim was to evaluate if the novel technique for histological reconstruction accurately replicated the trabecular microarchitecture of bone. This was performed by conducting micromorphometric measurements on the reconstruction and comparing the results obtained with those of microCT reconstructions. Methods: A bone biopsy sample was harvested upon re-entry following sinus floor augmentation. Following microCT scanning and histological processing, a modified version of the U-Net architecture was trained to categorize tissues on the sections. Detector-free local feature matching with transformers was used to create the histological reconstruction. The micromorphometric parameters were calculated using Bruker's CTAn software (version 1.18.8.0, Bruker, Kontich, Belgium) for both histological and microCT datasets. Results: Correlation coefficients calculated between the micromorphometric parameters measured on the microCT and histological reconstruction suggest a strong linear relationship between the two with p-values of 0.777, 0.717, 0.705, 0.666, and 0.687 for BV/TV, BS/TV, Tb.Pf Tb.Th, and Tb.Sp, respectively. Bland-Altman and mountain plots suggest good agreement between BV/TV measurements on the two reconstruction methods. Conclusions: This novel method for three-dimensional histological reconstruction provides researchers with a tool that enables the assessment of accurate trabecular microarchitecture and histological information simultaneously.

An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population

BACKGROUND

Panoramic radiographs, in which anatomic landmarks can be observed, are used to detect cases closely related to pediatric dentistry. The purpose of the study is to investigate the success and reliability of the detection of maxillary and mandibular anatomic structures observed on panoramic radiographs in children using artificial intelligence.

METHODS

A total of 981 mixed images of pediatric patients for 9 different pediatric anatomic landmarks including maxillary sinus, orbita, mandibular canal, mental foramen, foramen mandible, incisura mandible, articular eminence, condylar and coronoid processes were labelled, the training was carried out using 2D convolutional neural networks (CNN) architectures, by giving 500 training epochs and Pytorch-implemented YOLO-v5 models were produced. The success rate of the AI model prediction was tested on a 10% test data set.

RESULTS

A total of 14,804 labels including maxillary sinus (1922), orbita (1944), mandibular canal (1879), mental foramen (884), foramen mandible (1885), incisura mandible (1922), articular eminence (1645), condylar (1733) and coronoid (990) processes were made. The most successful F1 Scores were obtained from orbita (1), incisura mandible (0.99), maxillary sinus (0.98), and mandibular canal (0.97). The best sensitivity values were obtained from orbita, maxillary sinus, mandibular canal, incisura mandible, and condylar process. The worst sensitivity values were obtained from mental foramen (0.92) and articular eminence (0.92).

CONCLUSIONS

The regular and standardized labelling, the relatively larger areas, and the success of the YOLO-v5 algorithm contributed to obtaining these successful results. Automatic segmentation of these structures will save time for physicians in clinical diagnosis and will increase the visibility of pathologies related to structures and the awareness of physicians.

Diagnostic performance of artificial intelligence using cone-beam computed tomography imaging of the oral and maxillofacial region: A scoping review and meta-analysis

Purpose

The aim of this study was to conduct a scoping review and meta-analysis to provide overall estimates of the recall and precision of artificial intelligence for detection and segmentation using oral and maxillofacial cone-beam computed tomography (CBCT) scans.

Materials and Methods

A literature search was done in Embase, PubMed, and Scopus through October 31, 2022 to identify studies that reported the recall and precision values of artificial intelligence systems using oral and maxillofacial CBCT images for the automatic detection or segmentation of anatomical landmarks or pathological lesions. Recall (sensitivity) indicates the percentage of certain structures that are correctly detected. Precision (positive predictive value) indicates the percentage of accurately identified structures out of all detected structures. The performance values were extracted and pooled, and the estimates were presented with 95% confidence intervals (CIs).

Results

In total, 12 eligible studies were finally included. The overall pooled recall for artificial intelligence was 0.91 (95% CI: 0.87-0.94). In a subgroup analysis, the pooled recall was 0.88 (95% CI: 0.77-0.94) for detection and 0.92 (95% CI: 0.87-0.96) for segmentation. The overall pooled precision for artificial intelligence was 0.93 (95% CI: 0.88-0.95). A subgroup analysis showed that the pooled precision value was 0.90 (95% CI: 0.77-0.96) for detection and 0.94 (95% CI: 0.89-0.97) for segmentation.

Conclusion

Excellent performance was found for artificial intelligence using oral and maxillofacial CBCT images.