Dental Images Research Articles

Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.

Artificial intelligence (AI) is an emerging field in dentistry. AI is gradually being integrated into dentistry to improve clinical dental practice. The aims of this scoping review were to investigate the application of AI in image analysis for decision-making in clinical dentistry and identify trends and research gaps in the current literature. This review followed the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). An electronic literature search was performed through PubMed and Scopus. After removing duplicates, a preliminary screening based on titles and abstracts was performed. A full-text review and analysis were performed according to predefined inclusion criteria, and data were extracted from eligible articles. Of the 1334 articles returned, 276 met the inclusion criteria (consisting of 601,122 images in total) and were included in the qualitative synthesis. Most of the included studies utilized convolutional neural networks (CNNs) on dental radiographs such as orthopantomograms (OPGs) and intraoral radiographs (bitewings and periapicals). AI was applied across all fields of dentistry - particularly oral medicine, oral surgery, and orthodontics - for direct clinical inference and segmentation. AI-based image analysis was use in several components of the clinical decision-making process, including diagnosis, detection or classification, prediction, and management. A variety of machine learning and deep learning techniques are being used for dental image analysis to assist clinicians in making accurate diagnoses and choosing appropriate interventions in a timely manner.

Mandibular cortical morphology in patients with osteoporosis using computer‐aided detection with dental panoramic imaging: A pilot study of the relation to age and bone‐modifying agents

AbstractAimMandibular cortical morphology with panoramic radiography is considered to be a useful technique for identifying at risk of osteoporosis. The aim of the study was to investigate the mandibular cortical morphology in patients with osteoporosis using computer‐aided detection (CAD) with dental panoramic imaging, especially the relation to age and bone‐modifying agents.MethodsOne hundred and thirteen patients with osteoporosis were underwent panoramic radiography. The mandibular cortical morphology, such as mandibular cortical index (MCI) and degree of deformation of mandibular inferior cortex, was analyzed automatically with a CAD with dental panoramic imaging “PanoSCOPE.” Those were analyzed in relation to age and bone‐modifying agents.ResultsRegarding Class 3 (severely eroded) of the MCI classifications, the frequency in 61–70, 71–80, and 81–90 years were 22.2%, 38.6%, and 26.3%, respectively, and the MCI in bisphosphonate and denosumab were 29.8% and 42.1%, respectively. The degree of deformation in 61–70, 71–80, and 81–90 years were 45.9 ± 22.6, 58.8 ± 20.0, and 52.6 ± 21.5, respectively, and those in bisphosphonate and denosumab were 54.1 ± 20.6 and 57.5 ± 25.0, respectively. The MCI and degree of deformation in patients with osteoporosis were not significant differences for age and bone‐modifying agents.ConclusionThis study suggests that the CAD using PanoSCOPE can perform a quantitative analysis of mandibular cortical morphology in patients with osteoporosis.

Preparing for downstream tasks in AI for dental radiology: a baseline performance comparison of deep learning models.

To compare the performance of the convolutional neural network (CNN) with the vision transformer (ViT) and the gated multilayer perceptron (gMLP) in the classification of radiographic images of dental structures. Retrospectively collected 2-dimensional images derived from cone beam computed tomographic volumes were used to train CNN, ViT and gMLP architectures as classifiers for 4 different cases. Cases selected for training the architectures were the classification of the radiographic appearance of maxillary sinuses, maxillary and mandibular incisors, presence or absence of the mental foramen and the positional relationship of the mandibular third molar to the inferior alveolar nerve canal. The performance metrics (sensitivity, specificity, precision, accuracy and f1-score) and area under curve (AUC) - receiver operating characteristic and precision-recall curves were calculated. The ViT with an accuracy of 0.74-0.98, performed on par with the CNN model (accuracy 0.71-0.99) in all tasks. The gMLP displayed marginally lower performance (accuracy 0.65-0.98) as compared to the CNN and ViT. For certain tasks, the ViT outperformed the CNN. The AUCs ranged from 0.77-1.00 (CNN), 0.80-1.00 (ViT) and 0.73-1.00 (gMLP) for all of the 4 cases. The difference in performance of the ViT, gMLP and the CNN (the current state-of-the-art) was significant in certain tasks. This difference in model performance for various tasks proves that capabilities of different architectures may be leveraged. The vision transformer, followed by the gated multilayer perceptron are deep learning models that exhibit comparable performance with the convolutional neural network in the classification of dental radiographic images.

Using dental X-ray imaging to measure the diffusion coefficient for concrete samples

Using dental X-ray imaging to measure the diffusion coefficient for concrete samples

Classification and detection of dental images using meta-learning.

Meta-learning of dental X-rays is a machine learning technique that can be used to train models to perform new tasks quickly and with minimal input. Instead of just memorizing a task, this is accomplished through teaching a model how to learn. Algorithms for meta-learning are typically trained on a collection of training problems, each of which has a limited number of labelled instances. Multiple X-ray classification tasks, including the detection of pneumonia, coronavirus disease 2019, and other disorders, have demonstrated the effectiveness of meta-learning. Meta-learning has the benefit of allowing models to be trained on dental X-ray datasets that are too few for more conventional machine learning methods. Due to the high cost and lengthy collection process associated with dental imaging datasets, this is significant for dental X-ray classification jobs. The ability to train models that are more resistant to fresh input is another benefit of meta-learning.

Integration of cone beam computed tomography for interdisciplinary diagnosis and treatment planning

Cone-beam computed tomography (CBCT) has revolutionized dental imaging by providing precise three-dimensional visualization of hard tissue structures with minimal radiation exposure compared to conventional CT. This review explores the integration of CBCT in interdisciplinary diagnosis and treatment planning across various fields of dentistry. CBCT offers versatile applications including assessment of impacted teeth, airway analysis, temporomandibular joint evaluation, detection of jaw pathologies, and precise diagnosis of dento-maxillofacial fractures. Its ability to generate static and rotational 3D images aids in surgical planning and treatment outcome prediction. Furthermore, CBCT plays a crucial role in orthodontics for localization of impacted teeth and evaluation of craniofacial anomalies such as cleft lip and palate. In periodontics and implantology, CBCT facilitates detailed assessment of periodontal structures, bone defects, and precise implant placement. Additionally, CBCT assists in maxillofacial prosthetics by enabling virtual surgical planning and custom prosthetic fabrication. This paper underscores CBCT as an indispensable tool in modern dental practice, enhancing diagnostic accuracy, treatment precision, and patient outcomes across diverse clinical scenarios.

A Review of Applications of Photon-Counting Computed Tomography in Head and Neck Imaging.

Photon-counting CT (PCCT), approved for clinical practice for over two years now, both improves on features of conventional energy-integrating detector (EID) CT and introduces new capabilities such as multienergy acquisition. PCCT is already transforming all domains of radiology, including head and neck imaging, and will become increasingly utilized in the approaching years. In this review, we first concisely explain the key physical principles distinguishing PCCT from EID-CT. We then discuss how the underlying physics leads to the novel features associated with PCCT, focusing on improved artifact reduction, spatial resolution, contrast-to-noise ratio, as well as multienergy acquisition and reduced contrast and radiation doses. Next, we review head and neck PCCT applications and comparison to EID-CT in dental imaging, sinus imaging, temporal bone, tumor imaging, and vascular imaging. Within the temporal bone applications, we explore normal anatomy, pathologic anatomy, and the appearance of protheses and implants. Representative imaging is provided to highlight differences between PCCT and EID-CT. Finally, we highlight areas of ongoing research in PCCT.

Effect of Salvadora persica chewing sticks on maxillary central incisors crown-root angulation in Western-Asian population & correlation with Collum angle on 3D CBCT images

The main aim of the study was to identify the Collum angle classification for maxillary central incisors among individuals use vs not-use chewing sticks which could have beneficial role for immediate implant placement in respective individuals. Additionally, to identify the root-crown positioning and angulation in the Western Asian Saudi Arabian population for chewing sticks user’s vs non-users in order to develop guideline for the prosthetic procedures using Cone beam computed tompography (CBCT). A total number of 380 CBCT scans were included in the study for both of the groups based on the inclusion criterion of the patients reporting to the Dental OPD of Riyadh Elm University. The data was analyzed using 3D planner dental imaging software. Initially the teeth were classified based on root position in relation to facial plate of alveolar bone using Jung et al. classification. Additionally, the Collum angle and the root angulation of the maxillary central incisors was evaluated. The data was analyzed using one-way Anova with post-hoc Tuckey-Kramer test for pair-wise comparison. As per the Jung et al. classification, majority of the roots were observed with buccal placement. Based on the Collum angle classification majority of the maxillary central incisors were observed with facial placement (n=170), specially subtype I (n=78). But the root angulation was observed to be independent of Collum angle. Thus, it can be concluded, unlike root angulation, Collum angle has a correlation with root positioning. The angle for the individuals using chewing sticks of Salvadora Persica was similar to the individuals not using the miswak. Collum angle is correlated in all classified position of root. But the root angulation is independent of classification varies between miswak and non-miswak users.

Source-detector trajectory optimization for FOV extension in dental CBCT imaging

In dental imaging, Cone Beam Computed Tomography (CBCT) is a widely used imaging modality for diagnosis and treatment planning. Small dental scanning units are the most popular due to their cost-effectiveness. However, these small systems have the limitation of a small field of view (FOV) as the source and detector move at a limited angle in a circular path. This often limits the FOV size. In this study, we addressed this issue by modifying the source-detector trajectory of the small dental device. The main goal of this study was to extend the FOV algorithmically by acquiring projection data with optimal projection angulation and isocenter location rather than upgrading any physical parts of the device. A novel algorithm to implement a Volume of Interest (VOI) guided trajectory is developed in this study based on the small dental imaging device's geometry. In addition, this algorithm is fused with a previously developed off-axis scanning method which uses an elliptical trajectory, to compensate for the existing constraints and to further extend the FOV. A comparison with standard circular trajectory is performed. The FOV of such a standard trajectory is a circle of 11 cm diameter in the axial plane. The proposed novel trajectory extends the FOV significantly and a maximum FOV of 19.5 cm is achieved with the Structural Similarity Index Measure (SSIM) score ranging between (≈98-99%) in different VOIs. The study results indicate that the proposed source-detector trajectory can extend dental imaging FOV and increase imaging performance, which ultimately results in more precise diagnosis and enhanced patient outcomes.

Risk of Maxillary Canine Impaction in Pediatric Patients with Maxillary Constriction: Retrospective Study on Panoramic Dental Images

The maxillary canine is the second most commonly impacted tooth after the maxillary third molar. Identifying risk factors for impaction is crucial for timely intervention; panoramic radiographs are particularly valuable for screening and identification purposes. This study aims to explore the association between transverse maxillary deficiency with bilateral crossbite, and the risk of maxillary canine impaction. A cross-sectional retrospective study was conducted on records of pediatric patients (7–13 years old) undergoing orthodontic evaluations. Panoramic radiographs were analyzed to assess the risk of canine impaction using the sectorial method, distance from the occlusal plane, and the alpha angle. Results from 48 canines of patients with transverse maxillary deficiency and bilateral crossbite were compared to canines of sex- and age-matched patients without these malocclusions. Statistical analysis was performed by the Shapiro–Wilk test, Levene’s test, or a t-test. There were no significant differences in high-risk canine impaction between groups when considering sector classification or distance from the occlusal plane. However, the control group showed a significantly higher risk according to the alpha angle (≥25°). No overall significant difference in risk was observed, suggesting that transverse maxillary deficiency with bilateral crossbite may not increase the risk of maxillary canine impaction.

A global overview of the use of cone beam computed tomography in dentistry: a bibliometric review focusing on paediatric patients

Background Cone Beam Computed Tomography (CBCT) has improved diagnosis and treatment planning in paediatric dentistry, but no bibliometric studies have examined the research landscape. This study provides an overview of CBCT in paediatric dentistry. Methods A bibliometric review was conducted using articles from the Web of Science database up to February 2024. Conference papers and editorials were excluded. Data extracted included citation counts, publication dates, journals, impact factors, study designs, topics, geographical and institutional affiliations, authors, and keywords. Collaborative networks were visualised using VOSviewer, and Spearman’s correlation assessed the relationship between citation counts and other variables. Results The review analysed 517 articles, with the most cited receiving 557 citations. Publication dates ranged from 2005 to 2024, with a peak in 2023. Observational studies were the most common, particularly on maxillary expansion. The American Journal of Orthodontics and Dentofacial Orthopedics was the most cited journal, and the USA was a major contributor. Jacobs R authored the most articles (n=19), and the University of Alberta led in institutional output. Spearman’s correlation showed a weak positive correlation between citation count and journal impact factor (rho=0.272, p<0.001) and a strong negative correlation with publication year (rho=-0.762, p<0.001). Conclusions This bibliometric review provides an overview of the use of CBCT in paediatric dentistry, particularly in maxillary expansion. The findings suggest that more specific imaging protocols may improve safety and clinical outcomes, and that further investigation of long-term outcomes may provide valuable insights.

Spatial Prior-Guided Bi-Directional Cross-Attention Transformers for Tooth Instance Segmentation.

Tooth instance segmentation of dental panoramic X-ray images is of significant clinical importance. Teeth exhibit symmetry within the upper and lower jawbones and are arranged in a specific order. However, previous studies frequently overlook this crucial spatial prior information, resulting in the misidentifications of tooth categories, especially for adjacent or similarly shaped teeth. In this paper, we propose SPGTNet, a spatial prior-guided transformer method, designed to both the extracted tooth positional features from CNNs and the long-range contextual information from vision transformers, specifically for dental panoramic X-ray image segmentation. Initially, a center-based spatial prior perception module is employed to identify the centroid of each tooth, thereby enhancing the spatial prior information for the CNN sequence features. Subsequently, a bi-directional cross-attention module is designed to facilitate the interaction between the spatial prior information of the CNN sequence features and the long-range contextual features of the vision transformer sequence features. Finally, an instance identification head is employed to generate the tooth segmentation results. Extensive experiments on three public benchmark datasets demonstrate the effectiveness and superiority of our proposed method compared to other state-of-the-art approaches. The proposed method accurately identifies and analyzes tooth structures, thereby providing crucial information for dental diagnosis, treatment planning, and research.

Eliminating metal artifacts in dental computed tomography using an elaborate sinogram normalization interpolation method with CNR-based metal segmentation

Metal artifact reduction (MAR) in dental cone-beam computed tomography (CBCT) is critical for improving its clinical usefulness and remains a challenging problem. This study presents an elaborate sinogram normalization interpolation (SNI) method with contrast-to-noise ratio (CNR)-based metal segmentation to eliminate metal artifacts in dental CBCT. The proposed MAR method involves three main steps: (1) CNR-based segmentation of a metal trace in the sinogram domain; (2) generation of a residual artifact-reduced prior sinogram; and (3) sinogram completion using the prior sinogram, followed by filtered-backprojection (FBP)-based CBCT reconstruction and metal insertion processes. To verify the efficacy of the proposed method, simulations and experiments were performed using numerical and physical dental phantoms with several metal inserts. The quality of the resulting CBCT images was quantitatively evaluated using the structural similarity (SSIM) metric and compared to those obtained with other interpolation-based MAR methods. A tabletop setup for the micro-CT system was used in the experiment, which comprised an X-ray tube operated under tube conditions of 70 kV p and 5 mA and a flat-panel detector with a pixel size of 198 μm. Our results indicate that the CNR-based metal segmentation method precisely identified traces of metallic objects on the sinogram, and thereby, the proposed SNI-based MAR method considerably reduced metal artifacts in dental CBCT images without introducing any contrast anomalies. The SSIM values of the CBCT images obtained with the proposed MAR method were 0.99 and 0.95 for the simulation and experiment, respectively, which are approximately 11% and 9% greater than those with a simple threshold-based linear interpolation method, respectively, improving the image quality. The proposed MAR method can be applied to reduce metal artifacts in real-world dental CBCT systems.

Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review.

Dental care has been transformed by neural networks, introducing advanced methods for improving patient outcomes. By leveraging technological innovation, dental informatics aims to enhance treatment and diagnostic processes. Early diagnosis of dental problems is crucial, as it can substantially reduce dental disease incidence by ensuring timely and appropriate treatment. The use of artificial intelligence (AI) within dental informatics is a pivotal tool that has applications across all dental specialties. This systematic literature review aims to comprehensively summarize existing research on AI implementation in dentistry. It explores various techniques used for detecting oral features such as teeth, fillings, caries, prostheses, crowns, implants, and endodontic treatments. AI plays a vital role in the diagnosis of dental diseases by enabling precise and quick identification of issues that may be difficult to detect through traditional methods. Its ability to analyze large volumes of data enhances diagnostic accuracy and efficiency, leading to better patient outcomes. An extensive search was conducted across a number of databases, including Science Direct, PubMed (MEDLINE), arXiv.org, MDPI, Nature, Web of Science, Google Scholar, Scopus, and Wiley Online Library. The studies included in this review employed a wide range of neural networks, showcasing their versatility in detecting the dental categories mentioned above. Additionally, the use of diverse datasets underscores the adaptability of these AI models to different clinical scenarios. This study highlights the compatibility, robustness, and heterogeneity among the reviewed studies. This indicates that AI technologies can be effectively integrated into current dental practices. The review also discusses potential challenges and future directions for AI in dentistry. It emphasizes the need for further research to optimize these technologies for broader clinical applications. By providing a detailed overview of AI's role in dentistry, this review aims to inform practitioners and researchers about the current capabilities and future potential of AI-driven dental care, ultimately contributing to improved patient outcomes and more efficient dental practices.

Radiological Evaluation of Marginal Bone Loss around Dental Implants with Two Different Collar Designs

Objective: To compare mean peri-implant Marginal Bone Loss in patients receiving dental implants with micro-threaded collars with those receiving dental implants with machined collars. Study Design: Quasi-experimental study. Place and Duration of Study: Prosthodontics Department, Armed Forces Institute of Dentistry, Rawalpindi Pakistan, from Feb to Aug 2022. Methodology: Marginal bone loss surrounding dental implants was evaluated by measuring peri-implant bone levels at the time of placement (T0), and at 3 months (T1) and 6 months (T2) after placement. Bone level around implant was measured from the top of dental implant to the first point of contact between the marginal bone and implant body with the help of digital peri apical radiograph (Care stream RVG 6500) with built in software measuring tool (Dental Imaging Software 6.14.0). Results: A total of 80 implants were placed in which 40 were with rough micro-threaded collar (Group-A) and 40 were with smooth machined collar (Group-B). Out of 80 patients, 51(63.75%) were males and 29(36.25%) were females. Mean and SD of age in years was 40.52±11.75 years. Highest bone loss was at T2 in machined collar implant i.e. 0.72±0.04 mm, and lowest bone loss at T2 in micro-threaded collar implant i.e. 0.54±0.04 mm. A statistically significant difference was found between MBL in machined and micro-threaded implants at T2 (p-0.001). Conclusion: Marginal bone loss around rough-surfaced micro-threaded collar implants was significantly lower than machined collar implants.

DA-Net: A classification-guided network for dental anomaly detection from dental and maxillofacial images

Dental abnormalities (DA) are frequent signs of disorders of the mouth that cause discomfort, infection, and loss of teeth. Early and reasonably priced treatment may be possible if defective teeth in the oral cavity are automatically detected. Several research works have endeavored to create a potent deep learning model capable of identifying DA from pictures. However, because of the following problems, aberrant teeth from the oral cavity are difficult to detect: 1) Normal teeth and crowded dentition frequently overlap; 2) The lesion area on the tooth surface is tiny. This paper proposes a professional dental anomaly detection network (DA-Net) to address such issues. First, a multi-scale dense connection module (MSDC) is designed to distinguish crowded teeth from normal teeth by learning multi-scale spatial information of dentition. Then, a pixel differential convolution (PDC) module is designed to perform pathological tooth recognition by extracting small lesion features. Finally, a multi-stage convolutional attention module (MSCA) is developed to integrate spatial information and channel information to obtain abnormal teeth in small areas. Experiments on benchmarks show that DA-Net performs well in dental anomaly detection and can further assist doctors in making treatment plans. Specifically, the DA-Net method performs best on multiple detection evaluation metrics: IoU, PRE, REC, and mAP. In terms of REC and mAP indicators, the proposed DA-Net method is 1.1% and 1.3% higher than the second-ranked YOLOv7 method.

Challenges in the diagnosis and management of soft tissue tumors of the oral cavity.

Soft tissue lesions are among the most prevalent forms of tumors or tumor-like alterations within the oral cavity. They exhibit a wide spectrum of characteristics, ranging from benign, noninvasive lesions to malignant tumors, which collectively present a diagnostic challenge. A 67-year-old patient presented with an incidental finding of induration on the right cheek during dental hygiene. The intraoral examination revealed a soft, non-tender swelling measuring 20 mm in diameter. The sensitivity and percussion tests for the teeth in the fourth quadrant yielded unremarkable results. Panoramic radiography (OPG) revealed tip-ping of the premolars and an unclear apical finding in region 44. Cone-beam computed tomography (CBCT) revealed a sharply defined, lobulated hypodense lesion in the right buccal area. To clarify the diagnosis and optimize perioperative management, a 3 Tesla dental magnetic resonance imaging (MRI) scan was performed. This identified a 23×6×23 mm lipoma along the right mandibular ramus, causing detachment of the buccinator and de-pressor anguli oris muscles. A cinematic rendering reconstruction derived from the MRI data facilitated enhanced visualization for preoperative planning. The surgical excision was performed under local anesthesia, resulting in the successful removal of the neoplasm while preserving the mental nerve and submandibular gland. Histo-pathology confirmed the presence of a lobulated mature cell lipoma. The patient exhibited no complications during the removal of the sutures, with complete wound healing and no recurrence observed. This article highlights the efficacy of advanced MRI diagnostics and post-processing techniques in the management of ambiguous soft tissue neoplasms.

Use of intraoral mobile photography for screening of oral health in children

Abstract Background The aim of the study is to evaluate the diagnostic accuracy of intraoral mobile photography to assess caries intensity (DMFT/deft), simplified oral hygiene index (S-OHI) and modified gingival index (MGI). Methods Children aged 7-12 were included in the study. The clinical doctor evaluated the oral health indicators by visual examination: DMFT, deft, S-OHI, MGI were calculated. Dental students independently captured photos with a mobile phone (iPhone-11) in 8 predetermined projections. Dental images were kept in coded folders for research anonymity. Another professional calculated index based on the photos. Variables were used with binary values (DMFT/deft = 0/≥1; MGI=0/≥1; S-OHI≤1.2/>1.2). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of dental photography were evaluated and Cohen’s Kappa was calculated to measure the level of agreement between two independent observers. Results 2864 photographs were evaluated for 358 participants. The sensitivity and specificity of intraoral mobile photography for DMFT index is 95.8 (95% CI: 93.4-98.3) and 89.2 (95% CI: 83-95.5); For deft index: 100.0 (95% CI: 100-100) and 88.6 (95% CI: 79.3-98); For MGI: 89.7 (95% CI: 85.8-93.5) and 91.4 (95% CI: 86.3-96.5); For S-OHI: 77.6 (95% CI: 67.6-87.6) and 93.8 (95% CI: 91-96.6); The PPV value for DMFT, deft, MGI and S-OHI is 96.2; 97.7; 95.6 and 74.3 and the NPV value is 88.3; 100.0; 80.9 and 94.8 respectively. It was achieved substantial and perfect agreement between two independent raters: For DMFT, deft, MGI, S-OHI indices Cohen’s Kappa =0.85; 0.93; 0.78; 0.70 correspondingly. Conclusions Intraoral mobile photography, if applied within appropriate guidelines is reliable for assessing caries intensity, simplified hygiene index and modified gingival index as well as visual screening and may become a priority for dental public health. The involvement of dental students in studies of similar design is appropriate. Key messages • Intraoral mobile photography can be used for oral health screening in children. • The involvement of dental program students in screening studies conducted with intraoral mobile photography is appropriate.

Multi-label dental disorder diagnosis based on MobileNetV2 and swin transformer using bagging ensemble classifier

Dental disorders are common worldwide, causing pain or infections and limiting mouth opening, so dental conditions impact productivity, work capability, and quality of life. Manual detection and classification of oral diseases is time-consuming and requires dentists’ evaluation and examination. The dental disease detection and classification system based on machine learning and deep learning will aid in early dental disease diagnosis. Hence, this paper proposes a new diagnosis system for dental diseases using X-ray imaging. The framework includes a robust pre-processing phase that uses image normalization and adaptive histogram equalization to improve image quality and reduce variation. A dual-stream approach is used for feature extraction, utilizing the advantages of Swin Transformer for capturing long-range dependencies and global context and MobileNetV2 for effective local feature extraction. A thorough representation of dental anomalies is produced by fusing the extracted features. To obtain reliable and broadly applicable classification results, a bagging ensemble classifier is utilized in the end. We evaluate our model on a benchmark dental radiography dataset. The experimental results and comparisons show the superiority of the proposed system with 95.7% for precision, 95.4% for sensitivity, 95.7% for specificity, 95.5% for Dice similarity coefficient, and 95.6% for accuracy. The results demonstrate the effectiveness of our hybrid model integrating MoileNetv2 and Swin Transformer architectures, outperforming state-of-the-art techniques in classifying dental diseases using dental panoramic X-ray imaging. This framework presents a promising method for robustly and accurately diagnosing dental diseases automatically, which may help dentists plan treatments and identify dental diseases early on.

Multivariate Cluster Point Process to Quantify and Explore Multi-Entity Configurations: Application to Biofilm Image Data.

Clusters of similar or dissimilar objects are encountered in many fields. Frequently used approaches treat each cluster's central object as latent. Yet, often objects of one or more types cluster around objects of another type. Such arrangements are common in biomedical images of cells, in which nearby cell types likely interact. Quantifying spatial relationships may elucidate biological mechanisms. Parent-offspring statistical frameworks can be usefully applied even when central objects ("parents") differ from peripheral ones ("offspring"). We propose the novel multivariate cluster point process (MCPP) to quantify multi-object (e.g., multi-cellular) arrangements. Unlike commonly used approaches, the MCPP exploits locations of the central parent object in clusters. It accounts for possibly multilayered, multivariate clustering. The model formulation requires specification of which object types function as cluster centers and which reside peripherally. If such information is unknown, the relative roles of object types may be explored by comparing fit of different models via the deviance information criterion (DIC). In simulated data, we compared a series of models' DIC; the MCPP correctly identified simulated relationships. It also produced more accurate and precise parameter estimates than the classical univariate Neyman-Scott process model. We also used the MCPP to quantify proposed configurations and explore new ones in human dental plaque biofilm image data. MCPP models quantified simultaneous clustering of Streptococcus and Porphyromonas around Corynebacterium and of Pasteurellaceae around Streptococcus and successfully captured hypothesized structures for all taxa. Further exploration suggested the presence of clustering between Fusobacterium and Leptotrichia, a previously unreported relationship.