Improved Diagnostic Accuracy of Ameloblastoma and Odontogenic Keratocyst on Cone-Beam CT by Artificial Intelligence

Concordance of clinician, Chat-GPT4, and ORAD diagnoses against histopathology in Odontogenic Keratocysts and tumours: a 15-Year New Zealand retrospective study

BACKGROUND

This research aimed to investigate the concordance between clinical impressions and histopathologic diagnoses made by clinicians and artificial intelligence tools for odontogenic keratocyst (OKC) and Odontogenic tumours (OT) in a New Zealand population from 2008 to 2023.

METHODS

Histopathological records from the Oral Pathology Centre, University of Otago (2008-2023) were examined to identify OKCs and OT. Specimen referral details, histopathologic reports, and clinician differential diagnoses, as well as those provided by ORAD and Chat-GPT4, were documented. Data were analyzed using SPSS, and concordance between provisional and histopathologic diagnoses was ascertained.

RESULTS

Of the 34,225 biopsies, 302 and 321 samples were identified as OTs and OKCs. Concordance rates were 43.2% for clinicians, 45.6% for ORAD, and 41.4% for Chat-GPT4. Corresponding Kappa value against histological diagnosis were 0.23, 0.13 and 0.14. Surgeons achieved a higher concordance rate (47.7%) compared to non-surgeons (29.82%). Odds ratio of having concordant diagnosis using Chat-GPT4 and ORAD were between 1.4 and 2.8 (p < 0.05). ROC-AUC and PR-AUC were similar between the groups (Clinician 0.62/0.42, ORAD 0.58/0.28, Char-GPT4 0.63/0.37) for ameloblastoma and for OKC (Clinician 0.64/0.78, ORAD 0.66/0.77, Char-GPT4 0.60/0.71).

CONCLUSION

Clinicians with surgical training achieved higher concordance rate when it comes to OT and OKC. Chat-GPT4 and Bayesian approach (ORAD) have shown potential in enhancing diagnostic capabilities.

The efficacy of CBCT-based radiomics techniques in differentiating between conventional and unicystic ameloblastoma

OBJECTIVE

The aim of this study was to develop a cone beam computed tomography (CBCT) radiomics-based model that differentiates between conventional and unicystic ameloblastoma (AB).

METHODS

In this retrospective study, CBCT images were collected from 100 patients who had ABs that were diagnosed histopathologically as conventional or unicystic AB after surgical treatment. The patients were randomly divided into training (70) and validation (30) cohorts. Radiomics features were extracted from the images, and the optimal features were incorporated into 5 models: Logistic Regression, Support Vector Machine, Linear Discriminant Analysis, Random Forest, and XGBoost for prediction of tumor type. Model performance was evaluated using the area under the curve (AUC) from receiver operating characteristic analysis, sensitivity, specificity, accuracy, calibration curves, and decision curve analysis (DCA).

RESULTS

The 20 optimal radiomics features were incorporated into the Logistic Regression (LR) model, which exhibited the best overall performance with AUC = 0.936 (95% confidence interval [CI] = 0.877-0.996) for the training cohort and AUC = 0.929 (95% CI = 0.832-1.000) for the validation cohort. The nomogram combined the clinical features and the radiomics signature and resulted in the best predictive performance.

CONCLUSIONS

The LR model demonstrated the ability of radiomics and the nomogram to distinguish between the 2 types of AB and may have the potential to replace biopsies under noninvasive conditions.

Automatic classification and segmentation of multiclass jaw lesions in cone-beam CT using deep learning

OBJECTIVES

To develop and validate a modified deep learning (DL) model based on nnU-Net for classifying and segmenting five-class jaw lesions using cone-beam CT (CBCT).

METHODS

A total of 368 CBCT scans (37 168 slices) were used to train a multi-class segmentation model. The data underwent manual annotation by two oral and maxillofacial surgeons (OMSs) to serve as ground truth. Sensitivity, specificity, precision, F1-score, and accuracy were used to evaluate the classification ability of the model and doctors, with or without artificial intelligence assistance. The dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and segmentation time were used to evaluate the segmentation effect of the model.

RESULTS

The model achieved the dual task of classifying and segmenting jaw lesions in CBCT. For classification, the sensitivity, specificity, precision, and accuracy of the model were 0.871, 0.974, 0.874, and 0.891, respectively, surpassing oral and maxillofacial radiologists (OMFRs) and OMSs, approaching the specialist. With the model's assistance, the classification performance of OMFRs and OMSs improved, particularly for odontogenic keratocyst (OKC) and ameloblastoma (AM), with F1-score improvements ranging from 6.2% to 12.7%. For segmentation, the DSC was 87.2% and the ASSD was 1.359 mm. The model's average segmentation time was 40 ± 9.9 s, contrasting with 25 ± 7.2 min for OMSs.

CONCLUSIONS

The proposed DL model accurately and efficiently classified and segmented five classes of jaw lesions using CBCT. In addition, it could assist doctors in improving classification accuracy and segmentation efficiency, particularly in distinguishing confusing lesions (eg, AM and OKC).

New perspectives in the differential diagnosis of jaw lesions: Machine learning and inflammatory biomarkers

This study aimed to assess the diagnostic performance of a machine learning approach that utilized radiomic features extracted from Cone Beam Computer Tomography (CBCT) images and inflammatory biomarkers for distinguishing between Dentigerous Cysts (DCs), Odontogenic Keratocysts (OKCs), and Unicystic Ameloblastomas (UAs). This retrospective study involves 103 patients who underwent jaw lesion surgery in the Maxillofacial Surgery Unit of Federico II University Of Naples between January 2018 and January 2023. Nonparametric Wilcoxon-Mann-Whitney and Kruskal Wallis tests were used for continuous variables. Linear and non-logistic regression models (LRM and NLRM) were employed, along with machine learning techniques such as decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM), to predict the outcomes. When individual inflammatory biomarkers were considered alone, their ability to differentiate between OKCs, UAs, and DCs was below 50 % accuracy. However, a linear regression model combining four inflammatory biomarkers achieved an accuracy of 95 % and an AUC of 0.96. The accuracy of single radiomics predictors was lower than that of inflammatory biomarkers, with an AUC of 0.83. The Fine Tree model, utilizing NLR, SII, and one radiomic feature, achieved an accuracy of 94.3 % (AUC = 0.95) on the training and testing sets, and a validation set accuracy of 100 %. The Fine Tree model demonstrated the capability to discriminate between OKCs, UAs, and DCs. However, the LRM utilizing four inflammatory biomarkers proved to be the most effective algorithm for distinguishing between OKCs, UAs, and DCs.

The Use of Artificial Intelligence in Endodontics

Endodontics is the dental specialty foremost concerned with diseases of the pulp and periradicular tissues. Clinicians often face patients with varying symptoms, must critically assess radiographic images in 2 and 3 dimensions, derive complex diagnoses and decision making, and deliver sophisticated treatment. Paired with low intra- and interobserver agreement for radiographic interpretation and variations in treatment outcome resulting from nonstandardized clinical techniques, there exists an unmet need for support in the form of artificial intelligence (AI), providing automated biomedical image analysis, decision support, and assistance during treatment. In the past decade, there has been a steady increase in AI studies in endodontics but limited clinical application. This review focuses on critically assessing the recent advancements in endodontic AI research for clinical applications, including the detection and diagnosis of endodontic pathologies such as periapical lesions, fractures and resorptions, as well as clinical treatment outcome predictions. It discusses the benefits of AI-assisted diagnosis, treatment planning and execution, and future directions including augmented reality and robotics. It critically reviews the limitations and challenges imposed by the nature of endodontic data sets, AI transparency and generalization, and potential ethical dilemmas. In the near future, AI will significantly affect the everyday endodontic workflow, education, and continuous learning.

Odontogenic Keratocyst in Maxillary Sinus with Ectopic Third Molar: A Case Report

BACKGROUND Odontogenic keratocyst (OKC) is a common odontogenic cyst, and it occurs more frequently in the mandible, with the posterior region of the dental arch, the angle, or the ramus being the most commonly affected sites. Odontogenic keratocyst occurring within the maxillary sinus is extremely rare, accounting for only about 1% of cases. CASE REPORT A 20-year-old female patient without any clinical symptoms underwent an oral examination, during which a dense dental shadow was identified within the maxillary sinus, surrounded by a low-density shadow. Physical examination revealed absence of the left maxillary third molar, with intact mucosa. The patient reported no history of tooth extraction. X-ray and cone-beam computed tomography revealed a high-density image within the left maxillary sinus, resembling a tooth and surrounded by a soft-tissue shadow, which exhibited a greater density in comparison to conventional odontogenic cysts. The initial diagnosis was odontogenic keratocyst in the maxillary sinus with an ectopic maxillary third molar. Surgical enucleation of the cyst and extraction of the impacted tooth were carried out utilizing the Caldwell-Luc approach. Histopathological analysis confirmed the presence of OKC. No significant recurrence was noted during the 6 months of follow-up. CONCLUSIONS Odontogenic keratocysts in the maxillary sinus with ectopic third molar are rare and may not have any symptoms in the early stage. Surgery can be performed using the Caroler-Luke approach to achieve ideal treatment results. In view of the high recurrence rate of OKC, close follow-up should be conducted after surgery.

Accuracy of machine learning in the diagnosis of odontogenic cysts and tumors: a systematic review and meta-analysis

BACKGROUND

The recent impact of artificial intelligence in diagnostic services has been enormous. Machine learning tools offer an innovative alternative to diagnose cysts and tumors radiographically that pose certain challenges due to the near similar presentation, anatomical variations, and superimposition. It is crucial that the performance of these models is evaluated for their clinical applicability in diagnosing cysts and tumors.

METHODS

A comprehensive literature search was carried out on eminent databases for published studies between January 2015 and December 2022. Studies utilizing machine learning models in the diagnosis of odontogenic cysts or tumors using Orthopantomograms (OPG) or Cone Beam Computed Tomographic images (CBCT) were included. QUADAS-2 tool was used for the assessment of the risk of bias and applicability concerns. Meta-analysis was performed for studies reporting sufficient performance metrics, separately for OPG and CBCT.

RESULTS

16 studies were included for qualitative synthesis including a total of 10,872 odontogenic cysts and tumors. The sensitivity and specificity of machine learning in diagnosing cysts and tumors through OPG were 0.83 (95% CI 0.81-0.85) and 0.82 (95% CI 0.81-0.83) respectively. Studies utilizing CBCT noted a sensitivity of 0.88 (95% CI 0.87-0.88) and specificity of 0.88 (95% CI 0.87-0.89). Highest classification accuracy was 100%, noted for Support Vector Machine classifier.

CONCLUSION

The results from the present review favoured machine learning models to be used as a clinical adjunct in the radiographic diagnosis of odontogenic cysts and tumors, provided they undergo robust training with a huge dataset. However, the arduous process, investment, and certain ethical concerns associated with the total dependence on technology must be taken into account. Standardized reporting of outcomes for diagnostic studies utilizing machine learning methods is recommended to ensure homogeneity in assessment criteria, facilitate comparison between different studies, and promote transparency in research findings.

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.

Application of machine learning in the preoperative radiomic diagnosis of ameloblastoma and odontogenic keratocyst based on cone-beam CT

OBJECTIVES

Preoperative diagnosis of oral ameloblastoma (AME) and odontogenic keratocyst (OKC) has been a challenge in dentistry. This study uses radiomics approaches and machine learning (ML) algorithms to characterize cone-beam CT (CBCT) image features for the preoperative differential diagnosis of AME and OKC and compares ML algorithms to expert radiologists to validate performance.

METHODS

We retrospectively collected the data of 326 patients with AME and OKC, where all diagnoses were confirmed by histopathologic tests. A total of 348 features were selected to train six ML models for differential diagnosis by a 5-fold cross-validation. We then compared the performance of ML-based diagnoses to those of radiologists.

RESULTS

Among the six ML models, XGBoost was effective in distinguishing AME and OKC in CBCT images, with its classification performance outperforming the other models. The mean precision, recall, accuracy, F1-score, and area under the curve (AUC) were 0.900, 0.807, 0.843, 0.841, and 0.872, respectively. Compared to the diagnostics by radiologists, ML-based radiomic diagnostics performed better.

CONCLUSIONS

Radiomic-based ML algorithms allow CBCT images of AME and OKC to be distinguished accurately, facilitating the preoperative differential diagnosis of AME and OKC.

ADVANCES IN KNOWLEDGE

ML and radiomic approaches with high-resolution CBCT images provide new insights into the differential diagnosis of AME and OKC.

Deep learning in the diagnosis for cystic lesions of the jaws: a review of recent progress

Cystic lesions of the gnathic bones present challenges in differential diagnosis. In recent years, artificial intelligence (AI) represented by deep learning (DL) has rapidly developed and emerged in the field of dental and maxillofacial radiology (DMFR). Dental radiography provides a rich resource for the study of diagnostic analysis methods for cystic lesions of the jaws and has attracted many researchers. The aim of the current study was to investigate the diagnostic performance of DL for cystic lesions of the jaws. Online searches were done on Google Scholar, PubMed, and IEEE Xplore databases, up to September 2023, with subsequent manual screening for confirmation. The initial search yielded 1862 titles, and 44 studies were ultimately included. All studies used DL methods or tools for the identification of a variable number of maxillofacial cysts. The performance of algorithms with different models varies. Although most of the reviewed studies demonstrated that DL methods have better discriminative performance than clinicians, further development is still needed before routine clinical implementation due to several challenges and limitations such as lack of model interpretability, multicentre data validation, etc. Considering the current limitations and challenges, future studies for the differential diagnosis of cystic lesions of the jaws should follow actual clinical diagnostic scenarios to coordinate study design and enhance the impact of AI in the diagnosis of oral and maxillofacial diseases.

Odontogenic cystic lesion segmentation on cone-beam CT using an auto-adapting multi-scaled UNet

Objectives

Precise segmentation of Odontogenic Cystic Lesions (OCLs) from dental Cone-Beam Computed Tomography (CBCT) is critical for effective dental diagnosis. Although supervised learning methods have shown practical diagnostic results in segmenting various diseases, their ability to segment OCLs covering different sub-class varieties has not been extensively investigated.

Methods

In this study, we propose a new supervised learning method termed OCL-Net that combines a Multi-Scaled U-Net model, along with an Auto-Adapting mechanism trained with a combined supervised loss. Anonymous CBCT images were collected retrospectively from one hospital. To assess the ability of our model to improve the diagnostic efficiency of maxillofacial surgeons, we conducted a diagnostic assessment where 7 clinicians were included to perform the diagnostic process with and without the assistance of auto-segmentation masks.

Results

We collected 300 anonymous CBCT images which were manually annotated for segmentation masks. Extensive experiments demonstrate the effectiveness of our OCL-Net for CBCT OCLs segmentation, achieving an overall Dice score of 88.84%, an IoU score of 81.23%, and an AUC score of 92.37%. Through our diagnostic assessment, we found that when clinicians were assisted with segmentation labels from OCL-Net, their average diagnostic accuracy increased from 53.21% to 55.71%, while the average time spent significantly decreased from 101s to 47s (P<0.05).

Conclusion

The findings demonstrate the potential of our approach as a robust auto-segmentation system on OCLs in CBCT images, while the segmented masks can be used to further improve OCLs dental diagnostic efficiency.

Classification of Ameloblastoma, Periapical Cyst, and Chronic Suppurative Osteomyelitis with Semi-Supervised Learning: The WaveletFusion-ViT Model Approach

Ameloblastoma (AM), periapical cyst (PC), and chronic suppurative osteomyelitis (CSO) are prevalent maxillofacial diseases with similar imaging characteristics but different treatments, thus making preoperative differential diagnosis crucial. Existing deep learning methods for diagnosis often require manual delineation in tagging the regions of interest (ROIs), which triggers some challenges in practical application. We propose a new model of Wavelet Extraction and Fusion Module with Vision Transformer (WaveletFusion-ViT) for automatic diagnosis using CBCT panoramic images. In this study, 539 samples containing healthy (n = 154), AM (n = 181), PC (n = 102), and CSO (n = 102) were acquired by CBCT for classification, with an additional 2000 healthy samples for pre-training the domain-adaptive network (DAN). The WaveletFusion-ViT model was initialized with pre-trained weights obtained from the DAN and further trained using semi-supervised learning (SSL) methods. After five-fold cross-validation, the model achieved average sensitivity, specificity, accuracy, and AUC scores of 79.60%, 94.48%, 91.47%, and 0.942, respectively. Remarkably, our method achieved 91.47% accuracy using less than 20% labeled samples, surpassing the fully supervised approach's accuracy of 89.05%. Despite these promising results, this study's limitations include a low number of CSO cases and a relatively lower accuracy for this condition, which should be addressed in future research. This research is regarded as an innovative approach as it deviates from the fully supervised learning paradigm typically employed in previous studies. The WaveletFusion-ViT model effectively combines SSL methods to effectively diagnose three types of CBCT panoramic images using only a small portion of labeled data.

Digital pathology-based artificial intelligence models for differential diagnosis and prognosis of sporadic odontogenic keratocysts

Odontogenic keratocyst (OKC) is a common jaw cyst with a high recurrence rate. OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin syndrome. Moreover, OKC needs to be differentiated from orthokeratinized odontogenic cyst and other jaw cysts. Because of the different prognosis, differential diagnosis of several cysts can contribute to clinical management. We collected 519 cases, comprising a total of 2 157 hematoxylin and eosin-stained images, to develop digital pathology-based artificial intelligence (AI) models for the diagnosis and prognosis of OKC. The Inception_v3 neural network was utilized to train and test models developed from patch-level images. Finally, whole slide image-level AI models were developed by integrating deep learning-generated pathology features with several machine learning algorithms. The AI models showed great performance in the diagnosis (AUC = 0.935, 95% CI: 0.898-0.973) and prognosis (AUC = 0.840, 95%CI: 0.751-0.930) of OKC. The advantages of multiple slides model for integrating of histopathological information are demonstrated through a comparison with the single slide model. Furthermore, the study investigates the correlation between AI features generated by deep learning and pathological findings, highlighting the interpretative potential of AI models in the pathology. Here, we have developed the robust diagnostic and prognostic models for OKC. The AI model that is based on digital pathology shows promise potential for applications in odontogenic diseases of the jaw.

Automatic segmentation of ameloblastoma on ct images using deep learning with limited data

BACKGROUND

Ameloblastoma, a common benign tumor found in the jaw bone, necessitates accurate localization and segmentation for effective diagnosis and treatment. However, the traditional manual segmentation method is plagued with inefficiencies and drawbacks. Hence, the implementation of an AI-based automatic segmentation approach is crucial to enhance clinical diagnosis and treatment procedures.

METHODS

We collected CT images from 79 patients diagnosed with ameloblastoma and employed a deep learning neural network model for training and testing purposes. Specifically, we utilized the Mask R-CNN neural network structure and implemented image preprocessing and enhancement techniques. During the testing phase, cross-validation methods were employed for evaluation, and the experimental results were verified using an external validation set. Finally, we obtained an additional dataset comprising 200 CT images of ameloblastoma from a different dental center to evaluate the model's generalization performance.

RESULTS

During extensive testing and evaluation, our model successfully demonstrated the capability to automatically segment ameloblastoma. The DICE index achieved an impressive value of 0.874. Moreover, when the IoU threshold ranged from 0.5 to 0.95, the model's AP was 0.741. For a specific IoU threshold of 0.5, the model achieved an AP of 0.914, and for another IoU threshold of 0.75, the AP was 0.826. Our validation using external data confirms the model's strong generalization performance.

CONCLUSION

In this study, we successfully applied a neural network model based on deep learning that effectively performs automatic segmentation of ameloblastoma. The proposed method offers notable advantages in terms of efficiency, accuracy, and speed, rendering it a promising tool for clinical diagnosis and treatment.

Evaluating a Periapical Lesion Detection CNN on a Clinically Representative CBCT Dataset-A Validation Study

The aim of this validation study was to comprehensively evaluate the performance and generalization capability of a deep learning-based periapical lesion detection algorithm on a clinically representative cone-beam computed tomography (CBCT) dataset and test for non-inferiority. The evaluation involved 195 CBCT images of adult upper and lower jaws, where sensitivity and specificity metrics were calculated for all teeth, stratified by jaw, and stratified by tooth type. Furthermore, each lesion was assigned a periapical index score based on its size to enable a score-based evaluation. Non-inferiority tests were conducted with proportions of 90% for sensitivity and 82% for specificity. The algorithm achieved an overall sensitivity of 86.7% and a specificity of 84.3%. The non-inferiority test indicated the rejection of the null hypothesis for specificity but not for sensitivity. However, when excluding lesions with a periapical index score of one (i.e., very small lesions), the sensitivity improved to 90.4%. Despite the challenges posed by the dataset, the algorithm demonstrated promising results. Nevertheless, further improvements are needed to enhance the algorithm's robustness, particularly in detecting very small lesions and the handling of artifacts and outliers commonly encountered in real-world clinical scenarios.

Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss-A scoping review

Radiographic evaluation of bone changes is one of the main tools in the diagnosis of many oral and maxillofacial diseases. However, this approach to assessment has limitations in accuracy, inconsistency and comparatively low diagnostic efficiency. Recently, artificial intelligence (AI)-based algorithms like deep learning networks have been introduced as a solution to overcome these challenges. Based on recent studies, AI can improve the detection accuracy of an expert clinician for periapical pathology, periodontal diseases and their prognostication, as well as peri-implant bone loss. Also, AI has been successfully used to detect and diagnose oral and maxillofacial lesions with a high predictive value. This study aims to review the current evidence on artificial intelligence applications in the detection and analysis of bone loss in the oral and maxillofacial regions.

Machine learning-based radiomics for predicting BRAF-V600E mutations in ameloblastoma

Background

Ameloblastoma is a locally invasive and aggressive epithelial odontogenic neoplasm. The BRAF-V600E gene mutation is a prevalent genetic alteration found in this tumor and is considered to have a crucial role in its pathogenesis. The objective of this study is to develop and validate a radiomics-based machine learning method for the identification of BRAF-V600E gene mutations in ameloblastoma patients.

Methods

In this retrospective study, data from 103 patients diagnosed with ameloblastoma who underwent BRAF-V600E mutation testing were collected. Of these patients, 72 were included in the training cohort, while 31 were included in the validation cohort. To address class imbalance, synthetic minority over-sampling technique (SMOTE) is applied in our study. Radiomics features were extracted from preprocessed CT images, and the most relevant features, including both radiomics and clinical data, were selected for analysis. Machine learning methods were utilized to construct models. The performance of these models in distinguishing between patients with and without BRAF-V600E gene mutations was evaluated using the receiver operating characteristic (ROC) curve.

Results

When the analysis was based on radiomics signature, Random Forest performed better than the others, with the area under the ROC curve (AUC) of 0.87 (95%CI, 0.68-1.00). The performance of XGBoost model is slightly lower than that of Random Forest, and its AUC is 0.83 (95% CI, 0.60-1.00). The nomogram evident that among younger women, the affected region primarily lies within the mandible, and patients with larger tumor diameters exhibit a heightened risk. Additionally, patients with higher radiomics signature scores are more susceptible to the BRAF-V600E gene mutations.

Conclusions

Our study presents a comprehensive radiomics-based machine learning model using five different methods to accurately detect BRAF-V600E gene mutations in patients diagnosed with ameloblastoma. The Random Forest model's high predictive performance, with AUC of 0.87, demonstrates its potential for facilitating a convenient and cost-effective way of identifying patients with the mutation without the need for invasive tumor sampling for molecular testing. This non-invasive approach has the potential to guide preoperative or postoperative drug treatment for affected individuals, thereby improving outcomes.

A deep learning approach for radiological detection and classification of radicular cysts and periapical granulomas

OBJECTIVES

Dentists and oral surgeons often face difficulties distinguishing between radicular cysts and periapical granulomas on panoramic imaging. Radicular cysts require surgical removal while root canal treatment is the first-line treatment for periapical granulomas. Therefore, an automated tool to aid clinical decision making is needed.

METHODS

A deep learning framework was developed using panoramic images of 80 radicular cysts and 72 periapical granulomas located in the mandible. Additionally, 197 normal images and 58 images with other radiolucent lesions were selected to improve model robustness. The images were cropped into global (affected half of the mandible) and local images (only the lesion) and then the dataset was split into 90% training and 10% testing sets. Data augmentation was performed on the training dataset. A two-route convolutional neural network using the global and local images was constructed for lesion classification. These outputs were concatenated into the object detection network for lesion localization.

RESULTS

The classification network achieved a sensitivity of 1.00 (95% C.I. 0.63 - 1.00), specificity of 0.95 (0.86 - 0.99), and AUC (area under the receiver-operating characteristic curve) of 0.97 for radicular cysts and a sensitivity of 0.77 (0.46 - 0.95), specificity of 1.00 (0.93 - 1.00), and AUC of 0.88 for periapical granulomas. Average precision for the localization network was 0.83 for radicular cysts and 0.74 for periapical granulomas.

CONCLUSIONS

The proposed model demonstrated reliable diagnostic performance for the detection and differentiation of radicular cysts and periapical granulomas. Using deep learning, diagnostic efficacy can be enhanced leading to a more efficient referral strategy and subsequent treatment efficacy.

CLINICAL SIGNIFICANCE

A two-route deep learning approach using global and local images can reliably differentiate between radicular cysts and periapical granulomas on panoramic imaging. Concatenating its output to a localizing network creates a clinically usable workflow for classifying and localizing these lesions, enhancing treatment and referral practices.

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

This study aimed at performing a systematic review of the literature on the application of artificial intelligence (AI) in dental and maxillofacial cone beam computed tomography (CBCT) and providing comprehensive descriptions of current technical innovations to assist future researchers and dental professionals. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA) Statement was followed. The study's protocol was prospectively registered. Following databases were searched, based on MeSH and Emtree terms: PubMed/MEDLINE, Embase and Web of Science. The search strategy enrolled 1473 articles. 59 publications were included, which assessed the use of AI on CBCT images in dentistry. According to the PROBAST guidelines for study design, seven papers reported only external validation and 11 reported both model building and validation on an external dataset. 40 studies focused exclusively on model development. The AI models employed mainly used deep learning models (42 studies), while other 17 papers used conventional approaches, such as statistical-shape and active shape models, and traditional machine learning methods, such as thresholding-based methods, support vector machines, k-nearest neighbors, decision trees, and random forests. Supervised or semi-supervised learning was utilized in the majority (96.62%) of studies, and unsupervised learning was used in two (3.38%). 52 publications included studies had a high risk of bias (ROB), two papers had a low ROB, and four papers had an unclear rating. Applications based on AI have the potential to improve oral healthcare quality, promote personalized, predictive, preventative, and participatory dentistry, and expedite dental procedures.

Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases

The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.