Comparison of cone-beam computed tomography and panoramic radiography in the evaluation of maxillary sinus pathology related to maxillary posterior teeth: Do apical lesions increase the risk of maxillary sinus pathology?

Expert consensus on odontogenic maxillary sinusitis multi-disciplinary treatment

ABSTARCT

Odontogenic maxillary sinusitis (OMS) is a subtype of maxillary sinusitis (MS). It is actually inflammation of the maxillary sinus that secondary to adjacent infectious maxillary dental lesion. Due to the lack of unique clinical features, OMS is difficult to distinguish from other types of rhinosinusitis. Besides, the characteristic infectious pathogeny of OMS makes it is resistant to conventional therapies of rhinosinusitis. Its current diagnosis and treatment are thus facing great difficulties. The multi-disciplinary cooperation between otolaryngologists and dentists is absolutely urgent to settle these questions and to acquire standardized diagnostic and treatment regimen for OMS. However, this disease has actually received little attention and has been underrepresented by relatively low publication volume and quality. Based on systematically reviewed literature and practical experiences of expert members, our consensus focuses on characteristics, symptoms, classification and diagnosis of OMS, and further put forward multi-disciplinary treatment decisions for OMS, as well as the common treatment complications and relative managements. This consensus aims to increase attention to OMS, and optimize the clinical diagnosis and decision-making of OMS, which finally provides evidence-based options for OMS clinical management.

Deep learning for tooth identification and enumeration in panoramic radiographs

Background

Dentists begin the diagnosis by identifying and enumerating teeth. Panoramic radiographs are widely used for tooth identification due to their large field of view and low exposure dose. The automatic numbering of teeth in panoramic radiographs can assist clinicians in avoiding errors. Deep learning has emerged as a promising tool for automating tasks. Our goal is to evaluate the accuracy of a two-step deep learning method for tooth identification and enumeration in panoramic radiographs.

Materials and Methods

In this retrospective observational study, 1007 panoramic radiographs were labeled by three experienced dentists. It involved drawing bounding boxes in two distinct ways: one for teeth and one for quadrants. All images were preprocessed using the contrast-limited adaptive histogram equalization method. First, panoramic images were allocated to a quadrant detection model, and the outputs of this model were provided to the tooth numbering models. A faster region-based convolutional neural network model was used in each step.

Results

Average precision (AP) was calculated in different intersection-over-union thresholds. The AP50 of quadrant detection and tooth enumeration was 100% and 95%, respectively.

Conclusion

We have obtained promising results with a high level of AP using our two-step deep learning framework for automatic tooth enumeration on panoramic radiographs. Further research should be conducted on diverse datasets and real-life situations.

Performance of panoramic radiography compared with computed tomography in the evaluation of pathological changes in the maxillary sinuses: a systematic review and meta-analysis

OBJECTIVE

A systematic review was performed to evaluate the performance of panoramic radiography (PR) vs CT or cone beam CT (CBCT) in the diagnosis of pathological maxillary sinuses.

METHODS

This review was registered in the PROSPERO database under the number CRD42020211766. Observational studies that compared PR with CT/CBCT were used to evaluate pathological changes in the maxillary sinuses. A complete search of seven primary databases and gray literature was carried out. The risk of bias was assessed according to the Newcastle-Ottawa tool, and the GRADE tool was used to assess the quality of evidence. A binary meta-analysis was performed to assess the effectiveness of evaluating pathological alterations in the maxillary sinuses in PR and CT/CBCT.

RESULTS

Seven studies were included in our study, out of which four were included in a quantitative analysis. All studies were classified as low risk of bias. Five studies compared PR with CBCT and two studies compared PR to CT. The most common pathological alteration in maxillary sinuses reported was mucosal thickening. CT/CBCT was seen to be the most effective method for assessing pathological changes in the maxillary sinus when compared to PR (RR = 0.19, 95% confidence interval [CI] = 0.05 to 0.70, p = 0.01).

CONCLUSION

CT/CBCT are the most appropriate imaging methods to evaluate pathological changes in the maxillary sinuses, while PR is still limited in the evaluation of these changes being considered only for initial diagnosis.

Increased risks of maxillary sinus mucosal thickening in Chinese patients with periapical lesions

Objectives

This study aimed to evaluate the effects of factors related to periapical lesions (PALs) on sinus membrane thickening (SMT) in the Chinese population using cone-beam computed tomography (CBCT).

Methods

In this retrospective study, CBCT images (n = 512) of maxillary sinuses of 446 patients were evaluated by two examiners for SMT and PALs, PAL size, and the distance between the maxillary sinus floor (MSF), and the PAL's edge/root apex. The data were analyzed using analysis of variance, the Kruskal-Wallis test, χ²-test, and logistic regression.

Results

A binary logistic regression model showed that the prevalence and severity of SMT > 2 mm were significantly associated with older age (>60 years) (odds ratio [OR]: 4.03, 95% confidence interval [CI]): 2.24-7.72, P < 0.001], male sex (OR: 2.08, 95% CI: 1.21-3.56, P < 0.006), and PALs (OR: 6.89, 95% CI: 3.93-12.08, P < 0.001). The type of contact and penetration between the MSF and PALs or root apex showed a more significant relation with SMT > 2 mm than did distance after adjusting for confounding factors, including age and sex (PALs: OR = 10.17 and 14.57, P < 0.001; root apex: OR = 3.49 and 5.86, P < 0.001).

Conclusions

The prevalence and severity of SMT were significantly associated with older age, male sex, PALs, PAL size, and the distance between the MSF and PALs/root apex. Therefore, communication between dental surgeons and an otolaryngology specialist is important for the timely diagnosis and treatment of SMT of dental origin.

Prevalence and pattern of proximity of maxillary posterior teeth to maxillary sinus with mucosal thickening: A cone beam computed tomography based retrospective study

Context

Odontogenic sinusitis is a prevalent but frequently unrecognized condition, and periapical pathologies of maxillary posterior teeth are accused as one of the main causative factors.

Aim

This study aimed to evaluate the relationship between the periapical status of maxillary posterior teeth and its proximity to the maxillary sinus floor in the occurrence of incidental sinus pathologies using cone-beam computed tomography (CBCT).

Methodology

CBCT scans of 118 patients of age range 18-77 years were evaluated retrospectively to determine the relationship of maxillary posterior teeth to sinus floor in which vertical relationship was assessed using modified Kwak's classification and periapical status using CBCT periapical index. Statistical analysis was performed using SPSS statistics software.

Results

Of all 227 sinuses examined, 56.8% showed pathological changes, with mucosal thickening being the most common. More than 50% (50.2%) of sinuses were associated with periapical lesions of at least one maxillary posterior tooth based on evidence of pathological mucosal thickening. The presence of pathologic mucosal thickening was also significantly related (P < 0.05) to the presence of periapical pathologies. There was a significant association between tooth position and pathological sinus mucosal thickening, especially with second molars, first molars, and second premolars, respectively (P < 0.05). Second molar involvement was the most significant (P < 0.05).

Conclusion

The present study established a positive association between periapical disease status of maxillary posteriors and maxillary sinus mucosal thickening. Maxillary second premolar and first and second molars pathologies can significantly affect the maxillary sinus compared to other maxillary posterior tooth. CBCT proved to be an efficient imaging modality in detecting these changes.

Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study

BACKGROUND

Artificial intelligence (AI) has been introduced to interpret the panoramic radiographs (PRs). The aim of this study was to develop an AI framework to diagnose multiple dental diseases on PRs, and to initially evaluate its performance.

METHODS

The AI framework was developed based on 2 deep convolutional neural networks (CNNs), BDU-Net and nnU-Net. 1996 PRs were used for training. Diagnostic evaluation was performed on a separate evaluation dataset including 282 PRs. Sensitivity, specificity, Youden's index, the area under the curve (AUC), and diagnostic time were calculated. Dentists with 3 different levels of seniority (H: high, M: medium, L: low) diagnosed the same evaluation dataset independently. Mann-Whitney U test and Delong test were conducted for statistical analysis (ɑ=0.05).

RESULTS

Sensitivity, specificity, and Youden's index of the framework for diagnosing 5 diseases were 0.964, 0.996, 0.960 (impacted teeth), 0.953, 0.998, 0.951 (full crowns), 0.871, 0.999, 0.870 (residual roots), 0.885, 0.994, 0.879 (missing teeth), and 0.554, 0.990, 0.544 (caries), respectively. AUC of the framework for the diseases were 0.980 (95%CI: 0.976-0.983, impacted teeth), 0.975 (95%CI: 0.972-0.978, full crowns), and 0.935 (95%CI: 0.929-0.940, residual roots), 0.939 (95%CI: 0.934-0.944, missing teeth), and 0.772 (95%CI: 0.764-0.781, caries), respectively. AUC of the AI framework was comparable to that of all dentists in diagnosing residual roots (p > 0.05), and its AUC values were similar to (p > 0.05) or better than (p < 0.05) that of M-level dentists for diagnosing 5 diseases. But AUC of the framework was statistically lower than some of H-level dentists for diagnosing impacted teeth, missing teeth, and caries (p < 0.05). The mean diagnostic time of the framework was significantly shorter than that of all dentists (p < 0.001).

CONCLUSIONS

The AI framework based on BDU-Net and nnU-Net demonstrated high specificity on diagnosing impacted teeth, full crowns, missing teeth, residual roots, and caries with high efficiency. The clinical feasibility of AI framework was preliminary verified since its performance was similar to or even better than the dentists with 3-10 years of experience. However, the AI framework for caries diagnosis should be improved.

Maxillary Sinus Pathology and its Relationship with Pathology and Dental Treatments. Systematic Review

Introduction and objective: The aim of this review is to evaluate to what extent sinus pathology originates from dental pathology or treatment, and to assess the occurrence frequency of sinus pathology in its different forms using cone beam computed tomography (CBCT). Method: The literature review was conducted using PubMed, Scopus and the Cochrane Library. Forty-two articles were included (25 case series, ten cross- sectional studies, three case-control studies, two cohort studies, one prospective study, and one retrospective study). Results: Forty-two articles involving a total of 13,191 patients and 17,374 CBCTs were included in this review. The most frequent pathological findings were, by a considerable degree, inflammatory diseases, which represented 75.16% of the total findings, followed by infection (12.13%), tumours (6.88%), and high pneumatisation (2.07%). Within dental pathology, there is a direct Pearson correlation with polyps (1) and opacification (0.999), and an almost direct correlation with retention cysts (0.981) and sinus-associated dental elements (0.972). Conclusions: Our results further support the argument that dental modifications and treatments are an important cause of sinus pathology. For this reason, dental aetiologies must be taken into account by both dentists, maxillofacial surgeons and ENT when considering the most appropriate treatment for patients with maxillary sinusitis.

Lightweight deep learning methods for panoramic dental X-ray image segmentation

Dental X-ray image segmentation is helpful for assisting clinicians to examine tooth conditions and identify dental diseases. Fast and lightweight segmentation algorithms without using cloud computing may be required to be implemented in X-ray imaging systems. This paper aims to investigate lightweight deep learning methods for dental X-ray image segmentation for the purpose of deployment on edge devices, such as dental X-ray imaging systems. A novel lightweight neural network scheme using knowledge distillation is proposed in this paper. The proposed lightweight method and a number of existing lightweight deep learning methods were trained on a panoramic dental X-ray image data set. These lightweight methods were evaluated and compared by using several accuracy metrics. The proposed lightweight method only requires 0.33 million parameters ($$\sim 7.5$$ ∼ 7.5 megabytes) for the trained model, while it achieved the best performance in terms of IoU (0.804) and Dice (0.89) comparing to other lightweight methods. This work shows that the proposed method for dental X-ray image segmentation requires small memory storage, while it achieved comparative performance. The method could be deployed on edge devices and could potentially assist clinicians to alleviate their daily workflow and improve the quality of their analysis.

Deep learning for automated detection and numbering of permanent teeth on panoramic images

OBJECTIVE

This study aimed to evaluate an automated detection system to detect and classify permanent teeth on orthopantomogram (OPG) images using convolutional neural networks (CNNs).

METHODS

In total, 591 digital OPGs were collected from patients older than 18 years. Three qualified dentists performed individual teeth labelling on images to generate the ground truth annotations. A three-step procedure, relying upon CNNs, was proposed for automated detection and classification of teeth. Firstly, U-Net, a type of CNN, performed preliminary segmentation of tooth regions or detecting regions of interest (ROIs) on panoramic images. Secondly, the Faster R-CNN, an advanced object detection architecture, identified each tooth within the ROI determined by the U-Net. Thirdly, VGG-16 architecture classified each tooth into 32 categories, and a tooth number was assigned. A total of 17,135 teeth cropped from 591 radiographs were used to train and validate the tooth detection and tooth numbering modules. 90% of OPG images were used for training, and the remaining 10% were used for validation. 10-folds cross-validation was performed for measuring the performance. The intersection over union (IoU), F1 score, precision, and recall (i.e. sensitivity) were used as metrics to evaluate the performance of resultant CNNs.

RESULTS

The ROI detection module had an IoU of 0.70. The tooth detection module achieved a recall of 0.99 and a precision of 0.99. The tooth numbering module had a recall, precision and F1 score of 0.98.

CONCLUSION

The resultant automated method achieved high performance for automated tooth detection and numbering from OPG images. Deep learning can be helpful in the automatic filing of dental charts in general dentistry and forensic medicine.

Single-visit nonsurgical endodontic treatment of maxillary sinusitis: A case series

The etiopathology of maxillary sinusitis of dental origin (MSDO) is well established, and chronic apical periodontitis is the second most common cause of all dental-induced sinusitis incidents. The literature presents no common treatment protocols for MSDO and very few studies address the impact of root canal treatment (RCT) in its management. The literature presents cases of maxillary sinusitis resolution after performing a multivisit nonsurgical endodontic treatment, yet none described complete healing of MSDO as a result of single-visit nonsurgical RCT. This paper reports a case series of maxillary sinusitis of endodontic origin (MSEO) associated with upper maxillary molars that were successfully treated with single-visit nonsurgical antiseptic RCT. In all cases, the clinical symptoms subsided within a week after endodontic treatment. Control cone-beam computed tomography (CBCT) scan showed healing of periapical bone and total resolution of maxillary sinusitis symptoms. MSDO treatment protocol should start with nonsurgical antiseptic RCT. Single-visit nonsurgical endodontic treatment can be effective in MSEO management. CBCT is a method of choice in MSEO diagnostics. Endodontists are well trained and well equipped to treat MSDO, and the cooperation between ear, nose, and throat specialists, maxillofacial surgeons, and endodontists is crucial for both: good diagnostics and treatment.

Intrusion of maxillary molar roots into the maxillary sinus in a sample of the Egyptian population using cone beam computed tomography

Background

The proximity of the maxillary sinus floor to the maxillary molar roots increases the probability of oroantral communication on conducting any surgical or endodontic procedure in the involved area. The aim of this study is to evaluate the relationship between each maxillary molar root and maxillary sinus floor using cone beam computed tomography. Predicting the probability of protrusion of each root into the sinus will consequently predict the probability of occurrence of the oroantral fistula in a sample of the Egyptian population.

Results

The total number of roots located outside the sinus was 121 (35.3%), while those contacting the sinus floor were 80 (23.3%) and those intruded the sinus were 141 (41.2%). The percentage of root intrusion into the sinus in males (56.9%) was significantly (p = 0.01) higher than females (42.9%). The probability of root intrusion in the left molars (54.2%) was non-significantly (p = 0.067) higher than that of the right side (44.3%). As for the type of tooth, the second molar showed the highest probability of root intrusion into the sinus (55.3%) followed by the third molars (52.6%) then the first molars (40.9). According to the type of root, the mesiobuccal root showed the highest probability of intrusion into the sinus (50.9%) followed by the palatal root (49.1%) then the distobuccal root (47.4%). However, the difference in both type of tooth and type of root was statistically non-significant (p = 0.051 and 0.869 respectively). As for the individual root with the highest probability of intrusion, the mesio-buccal root of the right third molar is the most frequent root to intrude the sinus (71.4%) and the mesio-buccal root of the right first molar is the least frequent (22.7%).

Conclusions

In a sample of the Egyptian population, males exhibit higher probability of root protrusion into the sinus than females. The side and type of tooth are of higher impact on the probability of its intrusion into the sinus compared to the type of root. Left second molars are at a higher risk of oroantral communications on surgical or endodontic procedures compared to other molars due to its highest probability of intrusion into the sinus.

Comparison of panoramic radiography and cone-beam computed tomography for assessing radiographic signs indicating root protrusion into the maxillary sinus

Purpose

This study investigated correlations between findings on panoramic radiographs and cone-beam computed tomography (CBCT) to assess the relationship between the maxillary sinus floor and the roots of maxillary posterior teeth. In addition, radiographic signs indicating actual root protrusion into the maxillary sinus were evaluated on panoramic radiographs.

Materials and Methods

Paired panoramic radiographs and CBCT images from 305 subjects were analyzed. This analysis classified 2,440 maxillary premolars and molars according to their relationship with the maxillary sinus floor on panoramic radiographs and CBCT images. In addition, interruption of the sinus floor was examined on panoramic radiographs.

Results

Root protrusion into the maxillary sinus occurred most frequently in the mesiobuccal roots of the second molars. The classification according to panoramic radiographs and CBCT images was the same in more than 90% of cases when there was no contact between the root apex and the sinus floor. When the panoramic radiograph showed root protrusion into the sinus, the CBCT images showed the same classification in 67.5% of second molars, 48.8% of first molars, and 53.3% of second premolars. There was a statistically significant relationship between interruption of the sinus floor on panoramic radiographs and root protrusion into the sinus on CBCT images.

Conclusion

The presence of root protrusion into the sinus on panoramic radiographs demonstrated a moderate ability to predict root protrusion into the maxillary sinus. Interruption of the maxillary sinus floor could be considered an indicator of actual root protrusion into the maxillary sinus.

Imaging technologies for the detection of sinus pathologies of odontogenic origin. A review

Sinus pathologies of odontogenic origin (SPO) are common in the clinical consultation; however, the dentist has some complications to detect them because their discovery is usually incidental and through imaging studies that, in most cases, are of low quality. The objective of this review is to describe the pertinent imaging resources that allow the detection of the most frequent SPO and, at the same time, carry out an updated review of the scientific literature in order to recognize the imaging of both the maxillary sinus and the dental organs. The scientific literature focused on this topic, published between 2014 and 2020, was consulted. The review showed two important results: the first is that Cone Beam Tomography (CBCT) represents the imaging modality with the best performance for the detection of SPO by what can be considered the gold standard for this purpose. The second is that the most frequent SPO is sinus mucositis, which is related to odontogenic conditions such as periapical lesions and periodontal affectations. Although Cone Beam Computed Tomography (CBCT) is the most appropriate tool to detect SPO compared to images obtained by 2D devices, there are also other alternatives such as magnetic resonance imaging and ultrasonography, which seem to have a promising future.

Application of a fully deep convolutional neural network to the automation of tooth segmentation on panoramic radiographs

OBJECTIVES

To evaluate a fully deep learning mask region-based convolutional neural network (R-CNN) method for automated tooth segmentation using individual annotation of panoramic radiographs.

STUDY DESIGN

In total, 846 images with tooth annotations from 30 panoramic radiographs were used for training, and 20 panoramic images as the validation and test sets. An oral radiologist manually performed individual tooth annotation on the panoramic radiographs to generate the ground truth of each tooth structure. We used the augmentation technique to reduce overfitting and obtained 1024 training samples from 846 original data points. A fully deep learning method using the mask R-CNN model was implemented through a fine-tuning process to detect and localize the tooth structures. For performance evaluation, the F1 score, mean intersection over union (IoU), and visual analysis were utilized.

RESULTS

The proposed method produced an F1 score of 0.875 (precision: 0.858, recall: 0.893) and a mean IoU of 0.877. A visual evaluation of the segmentation method showed a close resemblance to the ground truth.

CONCLUSIONS

The method achieved high performance for automation of tooth segmentation on dental panoramic images. The proposed method might be applied in the first step of diagnosis automation and in forensic identification, which involves similar segmentation tasks.