Panoramic Radiographs Research Articles

The agreement of panoramic radiography with cone-beam computed tomography in classifying impacted lower third molars: a systematic review

The agreement of panoramic radiography with cone-beam computed tomography in classifying impacted lower third molars: a systematic review

An investigation of the validity of the Bedek models and Cameriere-European formula used in dental age prediction in Turkish children.

Dental age estimation is one of the most reliable methods for determining age, commonly used for forensic, criminal, and anthropological purposes. The purpose of this study was to assess and compare the precision of the Bedek models and the Cameriere-European Formula (CF) in determinating dental age in a sample of Turkish individuals. Retrospective panoramic radiographs of 1018 subjects (497 boys and 521 girls) aged 5-14 years were evaluated using the Bedek models and the CF. The differences between calculated dental age (DA) and chronological age (CA) for each sex and age group were analyzed using the paired sample t-test and the Wilcoxon signed-rank test. The accuracy of the methods used to estimate dental age was determined by calculating the mean absolute error (MAE) based on the proximity of the dental age to the chronological age. The CF method overestimated CA by 0.1 year in the entire sample. Conversely, the Bedek models tended to overestimate CA, with the three-, four-, and seven-tooth models exhibiting the most significant difference. There was a statistically significant difference between boys and girls in the DA-CA calculation using the CF method in the entire sample. Among the Bedek models, the three- and four-tooth models had the lowest MAE in the entire sample, while the single-tooth model had the highest MAE. The CF method showed higher accuracy in predicting the age of children living in eastern Turkey compared to the Bedek Models.

ResNet-Transformer deep learning model-aided detection of dens evaginatus.

Dens evaginatus is a dental morphological developmental anomaly. Failing to detect it may lead to tubercles fracture and pulpal/periapical disease. Consequently, early detection and intervention of dens evaginatus are significant to preserve vital pulp. This study aimed to develop a deep learning model to assist dentists in early diagnosing dens evaginatus, thereby supporting early intervention and mitigating the risk of severe consequences. In this study, a deep learning model was developed utilizing panoramic radiograph images sourced from 1410 patients aged 3-16 years, with high-quality annotations to enable the automatic detection of dens evaginatus. Model performance and model's efficacy in aiding dentists were evaluated. The findings indicated that the current deep learning model demonstrated commendable sensitivity (0.8600) and specificity (0.9200), outperforming dentists in detecting dens evaginatus with an F1-score of 0.8866 compared to their average F1-score of 0.8780, indicating that the model could detect dens evaginatus with greater precision. Furthermore, with its support, young dentists heightened their focus on dens evaginatus in tooth germs and achieved improved diagnostic accuracy. Based on these results, the integration of deep learning for dens evaginatus detection holds significance and can augment dentists' proficiency in identifying such anomaly.

Dental dysplasia in childhood cancer survivors: a case series of permanent tooth abnormalities.

This study was performed to investigate the association of dental dysplasia with childhood cancer. We examined the occurrence of agenesis, microdontia, and enamel changes of permanent teeth in pediatric cancer survivors. Seventy-six patients with pediatric cancer and hematologic diseases were referred to our department for the first time from October 2005 to December 2019. Of these, 17 patients who presented for a checkup in our department fulfilled the study criteria and were analyzed. Clinical examinations and panoramic radiographs were performed to investigate dental dysplasia of permanent teeth. A total of 34 developmental abnormalities (18 missing teeth, 16 cases of microdontia) occurred in 5 patients. The patients' medical records were also analyzed to assess the relationships of specific types of dysplasia with the treatment duration and cumulative drug dose administered. All five pediatric cancer survivors had dental dysplasia; all five had tooth agenesis, and four had microdontia. All five patients were < 4 years of age and had undergone high-dose chemotherapy. In this study, childhood cancer survivors who received high-dose chemotherapy before age 4 years experienced the dental dysplasia, including tooth agenesis, microdontia. These findings highlight the need for careful dental monitoring and early intervention in pediatric patients undergoing high-dose chemotherapy. This research was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee of the Medical University of Kobe (protocol code No. B230066, 1 September 2023).

How does the direction of region of interest selection affect the fractal dimension?

Introduction Fractal analysis (FA) is a computational method used to quantify the complex trabecular structure of bone. While FA has been widely applied in dentistry, there are challenges in standardizing the technique due to factors such as image resolution, region of interest (ROI) selection, and image processing. This study aimed to investigate the impact of the direction of ROI selection (DROIS) on fractal dimension (FD) calculations. Panoramic radiographs of 226 individuals aged 20-35 years were analyzed. ROIs were selected on the mandibular condyle, angular region, and mental region, and oriented at 0°, 22.5°, 45°, and 67.5° angles. FD was calculated using the box-counting method in ImageJ. The Friedman test and Wilcoxon signed-rank test were used for statistical analysis. The FD values differed significantly between the angled ROI groups in all three regions (Friedman test, p < 0.0001).Pairwise comparisons showed significant differences in FD between most ROI orientations, except between 22.5° and 67.5° in the angular region. DROIS is an important factor that should be considered in FA studies to ensure reliable and reproducible FD values. Appropriate methodological choices can help account for the influence of DROIS on FD calculations..

A two-stage deep-learning model for determination of the contact of mandibular third molars with the mandibular canal on panoramic radiographs.

This study aimed to assess the accuracy of a two-stage deep learning (DL) model for (1)detecting mandibular third molars (MTMs) and the mandibular canal (MC), and (2)classifying the anatomical relationship between these structures (contact/no contact) on panoramic radiographs. MTMs and MCs were labeled on panoramic radiographs by a calibrated examiner using bounding boxes. Each bounding box contained MTM and MC on one side. The relationship of MTMs with the MC was assessed on CBCT scans by two independent examiners without the knowledge of the condition of MTM and MC on the corresponding panoramic image, and dichotomized as contact/no contact. Data were split into training, validation, and testing sets with a ratio of 80:10:10. Faster R-CNN was used for detecting MTMs and MCs and ResNeXt for classifying their relationship. AP50 and AP75 were used as outcomes for detecting MTMs and MCs, and accuracy, precision, recall, F1-score, and the area-under-the-receiver-operating-characteristics curve (AUROC) were used to assess classification performance. The training and validation of the models were conducted using the Python programming language with the PyTorch framework. Three hundred eighty-seven panoramic radiographs were evaluated. MTMs were present bilaterally on 232 and unilaterally on 155 radiographs. In total, 619 images were collected which included MTMs and MCs. AP50 and AP75 indicating accuracy for detecting MTMs and MCs were 0.99 and 0.90 respectively. Classification accuracy, recall, specificity, F1-score, precision, and AUROC values were 0.85, 0.85, 0.93, 0.84, 0.86, and 0.91, respectively. DL can detect MTMs and MCs and accurately assess their anatomical relationship on panoramic radiographs.

Impact of Different Orthodontic Appliances on Mandibular Condyle, Anterior Region of Mandibular, and Maxillary Sinus Using Panoramic Radiography.

BACKGROUND This retrospective study of 44 patients requiring removable functional dental appliances aimed to compare the use of a maxillary expansion device, twinblock/monoblock, face mask, and chincup on maxillary sinus dimensions, mandibular anterior region bone density (MABD), and mandibular condyle head bone formation (MCBF) using panoramic radiography. MATERIAL AND METHODS The patients were divided into 4 groups based on the treatment modalities used: group A, maxillary expansion device (n=11); group B, twinblock/monoblock (n=11); group C, face mask (n=11); and group D, chincup (n=11). MABD and MCBF were assessed using the fractal analysis method, while maxillary sinus dimensions were compared using the ImageJ program. Different groups of patients were tested by the Mann-Whitney U test, with Bonferroni correction. RESULTS Group A showed an increase in MABD, MCBF, and average maxillary sinus indentation base length (MSIL) and a decrease in average planar base length (MSPL) and maxillary sinus area (MSA) (P<0.05). Group B showed an increase in MABD, MSIL, MSPL, MSA, and MCBF (P<0.05). Group C showed an increase in MABD, MSIL, MSPL, and MCBF, with a decrease in MSA (P>0.05). Group D showed an increase in MCBF, MABD, MSIL, and MSA, with a decrease in MSPL at the end of treatment, compared with pretreatment (P<0.05). CONCLUSIONS Orthodontic treatment with different removable functional dental appliances has varying effects on the mandible and maxilla. Possible changes in the maxilla and mandible should be considered before treatments with removable functional dental appliances.

Prevalence, Occurrence, and Characteristics of Supernumerary Teeth Among the Saudi Arabian Population Using Panoramic Radiographs

Background: Supernumerary teeth numerical anomalies and the early diagnosis of supernumerary teeth is very important to avoid potential complications. The study aim was to determine the prevalence, occurrence, and characteristics of supernumerary teeth among the Arabian population. Methods: A retrospective radiographic study was performed using panoramic radiographs of patients attending a teaching hospital from January 2018 to December 2020. Only healthy patients with clear radiographs were included in the study, and patients with syndromes, cleft lip, and palate, and unclear radiographs were excluded from the study. The details include the patient’s age and gender, supernumerary tooth number, location, orientation, and position. Only a single examiner was involved in the data collection and analysis. Results: Overall, 38 (2%) patients were observed with 47 supernumerary teeth. Among them, 76% were males and 24% were females, with a mean age of 16.1 ± 9.7 years. Mesiodens (87%) are the common type of supernumerary tooth, and the majority of the supernumerary teeth were impacted (66%). The majority of the patients presented with a single supernumerary tooth, while 24% of the patients presented with two supernumerary teeth. Sixty percent of the supernumerary teeth were conical in morphology, followed by a tuberculate morphology. In the study population, most of the supernumerary teeth were normal in orientation. Conclusions: The prevalence of supernumerary teeth was 2%. Among them, the majority were observed at the anterior region of the maxillary arch with a conical shape of normal orientation. The gender-based comparison of location, orientation, morphology, eruption, and number of supernumerary teeth showed male predilection.

Knowledge and Attitudes toward Dental X-Rays among Patients at Muhimbili School of Dentistry in Tanzania

Background Dental X-rays are vital diagnostic tools in dentistry, providing critical information for treatment planning. However, patient awareness and understanding of the risks and benefits associated with dental radiography can significantly influence their attitudes toward these procedures. Method This cross-sectional study was conducted in 2008 at the Radiology Department of Muhimbili Dental School. A total of seventy patients attending the dental clinic who underwent periapical and orthopantomogram (OPG) radiography were surveyed. Data were collected using structured questionnaires designed to assess knowledge and attitudes regarding dental X-rays. Results The results revealed that 70% of participants understood the radiation risks associated with X-rays, while 75% were aware of the indications for their use. However, only 60% were familiar with the benefits of OPG. In terms of attitudes, 80% of patients agreed that X-rays are essential for diagnosis, and 85% believed their dentist should explain the necessity of X-rays. Conclusion The findings indicate that while patients at Muhimbili Dental School exhibit a moderate level of knowledge and positive attitudes toward dental X-rays, there is a need for enhanced patient education regarding the risks and benefits. Implementing educational initiatives could improve understanding and alleviate concerns related to dental radiography.

Fractal dimension, lacunarity, and bone area fraction analysis of peri-implant trabecular bone after prosthodontic loading.

To assess the structural alterations in peri-implant bone occurring 5years after prosthodontic loading in panoramic radiography (PR). PR images of 44 mandibular and 33 maxillary implants along with 42 healthy control teeth taken before and 5years after prosthodontic loading were included. Two regions-of-interest (ROI) were selected from mesial and distal surrounding bone of each implant and tooth. Then, the selected ROIs were divided to obtain three sub-ROIs (coronal, middle, and apical) on each side. A total of eight ROIs and sub-ROIs from each implant and control tooth were used for the calculations of fractal dimension (FD), lacunarity, and bone area fraction (BA/TA). The paired-sample t test was used to compare measurements before and 5years after loading (p = 0.05). Overall evaluation of 77 implants showed that FD decreased at the middle and apical peri-implant bone levels 5years after loading (p < 0.05). In mandibular implants, BA/TA decreased after loading (p < 0.05). While FD decreased at the coronal level (p = 0.022), lacunarity increased at the middle level of mandibular implants (p < 0.05). In maxillary implants, FD decreased at the middle and BA/TA decreased at the coronal level (p < 0.05). On the other hand, BA/TA increased at the apical level of maxillary implants (p = 0.016) after loading. None of the parameters revealed any difference in the control group (p > 0.05). FD and BA/TA can be used to analyze structural changes in peri-implant bone after prosthodontic loading. Additionally, FD, lacunarity and BA/TA may provide useful information about changes occurring at different levels of peri-implant bone.

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

Artificial intelligence (AI), a field within computer science, uses algorithms to replicate human intelligence tasks such as pattern recognition, decision-making and problem-solving through complex datasets. In endodontics, AI is transforming diagnosis and treatment by applying deep learning algorithms, notably convolutional neural networks, which mimic human brain function to analyse two-dimensional (2D) and three-dimensional (3D) data. This article provides an overview of AI applications in endodontics, evaluating its use in 2D and 3D imaging and examining its role as a beneficial tool or potential challenge. Through a narrative review, the article explores AI's use in 2D and 3D imaging modalities, discusses their limitations and examines future directions in the field. AI significantly enhances endodontic practice by improving diagnostic accuracy, workflow efficiency, and treatment planning. In 2D imaging, AI excels at detecting periapical lesions on both periapical and panoramic radiographs, surpassing expert radiologists in accuracy, sensitivity and specificity. AI also accurately detects and classifies radiolucent lesions, such as radicular cysts and periapical granulomas, matching the precision of histopathology analysis. In 3D imaging, AI automates the segmentation of fine structures such as pulp chambers and root canals on cone-beam computed tomography scans, thereby supporting personalized treatment planning. However, a significant limitation highlighted in some studies is the reliance on in vitro or ex vivo datasets for training AI models. These datasets do not replicate the complexities of clinical environments, potentially compromising the reliability of AI applications in endodontics. Despite advancements, challenges remain in dataset variability, algorithm generalization, and ethical considerations such as data security and privacy. Addressing these is essential for integrating AI effectively into clinical practice and unlocking its transformative potential in endodontic care. Integrating radiomics with AI shows promise for enhancing diagnostic accuracy and predictive analytics, potentially enabling automated decision support systems to enhance treatment outcomes and patient care. Although AI enhances endodontic capabilities through advanced imaging analyses, addressing current limitations and fostering collaboration between AI developers and dental professionals are essential. These efforts will unlock AI's potential to achieve more predictable and personalized treatment outcomes in endodontics, ultimately benefiting both clinicians and patients alike.

TRATAMENTO CIRURGICO DE CERATOCISTOS ODONTOGÊNICOS: RELATO DE CASO CLÍNICO

Introduction: The keratocyst or keratocyst is an odontogenic tumor characterized as a lesion that affects the maxillary and mandibular regions. It is considered a benign lesion, reaching a prevalence of 3% to 11% among all odontogenic cysts, resulting in major bone loss. It can be asymptomatic and is usually diagnosed through routine radiographs or histopathological findings. Objective: To present a clinical case concerning the diagnosis and surgical treatment of an odontogenic keratocyst in the anterior mandibular region. Clinical case: Male, normosystemic, melanodermic patient. An increase in extraoral volume was observed on the right side of the face in the anterior region of the mandible. Panoramic radiography revealed well-defined, unilocular radiolucent images in the areas of the right lower first premolar, left lower third molar and bilateral upper third molars. On intraoral physical examination, bone bulging was observed in the region of the tooth in question. A computerized tomography scan was then requested, in which extensive expansive bone lesions were detected. Marsupialization and local decompression of the two upper lesions and the lower right lesion were performed, detecting the presence of liquid content. Firstly, an incisional biopsy was carried out and a decompression drain was installed in the three lesions. The histopathological report was of an odontogenic keratocyst. The decompression device remained in place for 30 days in the lower right lesion and approximately 15 days in the upper lesions, showing a significant reduction in the lesions. The lower right lesion was then enucleated. The 6-month post-operative image showed bone formation with no signs of recurrence. Conclusion: The proposed treatment proved to be effective in treating odontogenic keratocysts, but follow-up imaging is extremely important in view of the possibility of recurrence.

Integrating Super-Resolution with Deep Learning for Enhanced Periodontal Bone Loss Segmentation in Panoramic Radiographs

Periodontal disease is a widespread global health concern that necessitates an accurate diagnosis for effective treatment. Traditional diagnostic methods based on panoramic radiographs are often limited by subjective evaluation and low-resolution imaging, leading to suboptimal precision. This study presents an approach that integrates Super-Resolution Generative Adversarial Networks (SRGANs) with deep learning-based segmentation models to enhance the segmentation of periodontal bone loss (PBL) areas on panoramic radiographs. By transforming low-resolution images into high-resolution versions, the proposed method reveals critical anatomical details that are essential for precise diagnostics. The effectiveness of this approach was validated using datasets from the Chungbuk National University Hospital and the Kaggle data portal, demonstrating significant improvements in both image resolution and segmentation accuracy. The SRGAN model, evaluated using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics, achieved a PSNR of 30.10 dB and an SSIM of 0.878, indicating high fidelity in image reconstruction. When applied to semantic segmentation using a U-Net architecture, the enhanced images resulted in a dice similarity coefficient (DSC) of 0.91 and an intersection over union (IoU) of 84.9%, compared with 0.72 DSC and 65.4% IoU for native low-resolution images. These results underscore the potential of SRGAN-enhanced imaging to improve PBL area segmentation and suggest broader applications in medical imaging, where enhanced image clarity is crucial for diagnostic accuracy. This study also highlights the importance of further research to expand the dataset diversity and incorporate clinical validation to fully realize the benefits of super-resolution techniques in medical diagnostics.

CBCT Guidance for Removing Foreign Object from the Jawbone: A Case Study

AbstractForeign objects lodged in the mandibular bone often result from trauma or dental procedures. Accurate localization of these foreign objects is crucial for guided surgical removal, especially when patients present with persistent pain. Conventional imaging modalities like periapical and panoramic radiographs may not provide sufficient detail for precise localization. Cone beam computed tomography (CBCT) has emerged as a valuable tool for guided surgical interventions owing to its superior imaging capabilities. We present two cases of foreign objects lodged in the mandible, where patients complained of prolonged pain, soreness, and numbness in the right mandible extending to the head and behind the ear. Both patients had undergone previous right mandibular tooth extractions and received treatment from a neurologist without resolution of symptoms. Both cases showed the control improvement in complaints, and no paresthesia or postoperative complications were found. CBCT-guided surgical removal was performed in both cases, revealing a metal specimen measuring 6 × 3 × 1 mm3 in the first patient and a remaining root measuring 5 × 3 × 2 mm3 in the second patient. Diagnosing foreign objects in the mandible poses challenges due to their varied size, composition, and proximity to vital structures. CBCT offers superior imaging resolution, enabling precise localization and assessment of anatomical relationships, such as the distance to the inferior alveolar nerve and surrounding boundaries. CBCT emerges as the preferred imaging modality for diagnosing and guiding the surgical removal of foreign objects in the mandible. Its advantages include accurate localization, low radiation exposure, and cost-effectiveness. Compared with CT scans, CBCT also offers faster scanning times, making it a valuable tool in clinical practice for managing such cases

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.

Unintentionally Retained Elevator Blade Left for 8 Years Causes Mild Chronic Pain.

Fracture of surgical instruments in dental practice is not usually reported in the literature. Management involves searching for and retrieving the fragment to avoid issues such as infection, swallowing, or aspiration. Although foreign bodies may not cause symptoms for years, some can cause chronic pain. The authors describe herein the unusual case of an unintentionally retained broken elevator blade left for 8 years before being found during an orthodontic evaluation. Panoramic radiography revealed a foreign body in the alveolar bone of the right mandibular third molar that was causing mild chronic pain. The patient was unaware whether routine radiographic examination had revealed its presence. Cone-beam computer tomography was performed to identify the position of the blade fragment, and surgery was performed to retrieve it. The mild chronic pain disappeared after removal. It is advisable to use high-quality surgical instruments and to perform routine checkups before and after surgery.

Screening ability of dental students to detect osteoporosis on dental panoramic radiographs

ObjectivesPostmenopausal women with osteoporosis are frequently underdiagnosed. In Japan, general dental practitioners have begun using the cortical shape of the mandible on dental panoramic radiographs (PRs) to identify and refer women at risk of osteoporosis to medical professionals. It remains unclear whether dental students, after relevant education, possess the ability to identify these at-risk individuals. This study evaluated the ability of dental students to screen for osteoporosis on PRs. MethodsA cohort of 113 fifth-year dental students participated in a lecture on osteoporosis screening using PRs. The students then categorized the mandibular inferior cortex on PRs from 30 postmenopausal women (11 with osteoporosis) as normal, mildly to moderately eroded, or severely eroded. Interobserver agreement between the students and an expert oral radiologist using two cortical groups (normal to moderately eroded and severely eroded) was assessed through kappa statistics. Screening ability for osteoporosis detection by identifying severely eroded cortices was calculated and compared with that of the Osteoporosis Self-assessment Tool for Asians (OSTA). ResultsTwenty-one (18.6%) students demonstrated moderate to substantial agreement with the expert. The mean sensitivity, specificity, and positive and negative predictive values for these students in identifying osteoporosis were 60.2%, 88.0%, 76.3%, and 80.0%, respectively. For the OSTA, sensitivity, specificity, and positive and negative predictive values were 27.3%, 68.4%, 33.3%, and 61.9%, respectively. ConclusionsEven with minimal education, approximately one-fourth of dental students may accurately identify postmenopausal women with osteoporosis on PRs, outperforming questionnaire-based screening tools.

Segmentation of periapical lesions with automatic deep learning on panoramic radiographs: an artificial intelligence study

Periapical periodontitis may manifest as a radiographic lesion radiographically. Periapical lesions are amongst the most common dental pathologies that present as periapical radiolucencies on panoramic radiographs. The objective of this research is to assess the diagnostic accuracy of an artificial intelligence (AI) model based on U²-Net architecture in the detection of periapical lesions on dental panoramic radiographs and to determine whether they can be useful in aiding clinicians with diagnosis of periapical lesions and improving their clinical workflow. 400 panoramic radiographs that included at least one periapical radiolucency were selected retrospectively. 780 periapical radiolucencies in these anonymized radiographs were manually labeled by two independent examiners. These radiographs were later used to train the AI model based on U²-Net architecture trained using a deep supervision algorithm. An AI model based on the U²-Net architecture was implemented. The model achieved a dice score of 0.8 on the validation set and precision, recall, and F1-score of 0.82, 0.77, and 0.8 respectively on the test set. This study has shown that an AI model based on U²-Net architecture can accurately diagnose periapical lesions on panoramic radiographs. The research provides evidence that AI-based models have promising applications as adjunct tools for dentists in diagnosing periapical radiolucencies and procedure planning. Further studies with larger data sets would be required to improve the diagnostic accuracy of AI-based detection models.

THE DETECTION OF DISTOMOLAR TEETH ON PANORAMIC RADIOGRAPHS USING DIFFERENT ARTIFICIAL INTELLIGENCE MODELS.

One notable anomaly, presence of distomolars, arises beyond the typical sequence of the human dental system. In this study, convolutional neural networks (CNNs) based machine learning methods were employed to classify distomolar tooth existence using panoramic radiography (PR). PRs dataset, composed of 117 subjects with distomolar teeth and 146 subjects without distomolar teeth, was constructed. These images were assessed using AlexNet, DarkNet, DenseNet, EfficientNet, GoogLeNet, ResNet, MobileNet, NasNet-Mobile, VGG, and XceptionNet frameworks for distomolar teeth existence. Considering the moderate number dataset samples, transfer learning was also utilized to improve the performance of these CNN based networks along with 5-fold cross-validation. The final classification was obtained through the fusion of the classifiers results. Performance of the experimental studies was assessed utilizing accuracy (Acc), sensitivity (sen), specificity (spe) and precision (pre) metrics. Best accuracy value of 96.2% was obtained for the fusion of DarkNet, DenseNet, and ResNet, three best individual performing architectures, in distomolar teeth classification problem. In summary, this study has demonstrated the significant potential of CNNs in accurately detecting distomolar teeth in dental radiographs, a critical task for clinical diagnosis and treatment planning. The fusion of CNN architectures, particularly ResNet, Darknet, and DenseNet, has shown exceptional performance, pointing towards the future of artificial intelligence (AI) driven dental diagnostics. Our findings showed that these systems can help clinicians during radiologic examinations.

Comparing tooth development timing between ethnic groups, excluding nutritional and environmental influences.

The timing of dental development in ethnic Finns and Somalis, who were born and living in Finland, was compared, with efforts to minimize environmental bias. The developmental status of seven lower left permanent teeth were staged according to Demirjian et al., using panoramic radiographs from 2,100 Finnish and 808 Somali females and males, aged 2 to 23 years. For each tooth, a continuation-ratio model was constructed to analyze the allocated stages as a function of sex and ethnic origin. Several statistically significant differences in mean age of certain tooth developmental stage transitions were revealed. While Somalis generally displayed stage transitions at younger age, none of the seven teeth consistently showed earlier stage transitions in Somalis compared to Finns. Within each tooth, the lowest (or highest) mean age of stage transition varied without any discernible pattern between the two ethnic groups. Overall, the observed differences in mean age of stage transition between the groups was minimal, suggesting a low impact on clinical and forensic age assessment practice. In conclusion, the studied ethnic Finn and Somali groups with equal nutritional and /or environmental conditions exhibit similar timing in the development of all lower left permanent teeth.