RESUMEN

BACKGROUND: Artificial intelligence (AI) acts as the state-of-the-art in periodontitis diagnosis in dentistry. Current diagnostic challenges include errors due to a lack of experienced dentists, limited time for radiograph analysis, and mandatory reporting, impacting care quality, cost, and efficiency. OBJECTIVE: This review aims to evaluate the current and future trends in AI for diagnosing periodontitis. METHODS: A thorough literature review was conducted following PRISMA guidelines. We searched databases including PubMed, Scopus, Wiley Online Library, and ScienceDirect for studies published between January 2018 and December 2023. Keywords used in the search included "artificial intelligence," "panoramic radiograph," "periodontitis," "periodontal disease," and "diagnosis." RESULTS: The review included 12 studies from an initial 211 records. These studies used advanced models, particularly convolutional neural networks (CNNs), demonstrating accuracy rates for periodontal bone loss detection ranging from 0.76 to 0.98. Methodologies included deep learning hybrid methods, automated identification systems, and machine learning classifiers, enhancing diagnostic precision and efficiency. CONCLUSIONS: Integrating AI innovations in periodontitis diagnosis enhances diagnostic accuracy and efficiency, providing a robust alternative to conventional methods. These technologies offer quicker, less labor-intensive, and more precise alternatives to classical approaches. Future research should focus on improving AI model reliability and generalizability to ensure widespread clinical adoption.

RESUMEN

This study aimed to compare and evaluate the accuracy of the Demirjian (DE) and the London Atlas (LAE) dental age estimation methods in a Saudi population sample. This retrospective cross-sectional study used digital radiographs from electronic health records in three different dental institutes. In total, 357 male and 354 female (ages 5-15 years) digital orthopantomograms were selected for age estimation. The mean difference between the chronological age (CA) and age estimation method among males and females was 0.03 ± 0.34 and 0.00 ± 0.34, respectively, for LAE and 0.55 ± 0.84 and 0.76 ± 0.51, respectively, for DE. The mean difference between the LAE and DE methods among males and females was 0.52 ± 0.89 and -0.76 ± 0.57, respectively. No statistically significant difference between CA and LAE was found in either males (p = 0.079) or females (p = 0.872). A statistically significant difference was found between CA and DE in both genders (p < 0.001). A statistically significant difference was found between the LAE and DE groups (p < 0.001) in both genders. An overestimation of dental age was observed with DE compared with that in CA. LAE showed higher accuracy than CA, with no clinically significant difference. Although the difference between the LAE and DE methods was insignificant, the LAE method proved to be more accurate.

RESUMEN

The purpose of this study is to develop two-step deep learning models that can automatically detect implant regions on panoramic radiographs and identify several types of implants. A total of 1,574 panoramic radiographs containing 3675 implants were included. The implant manufacturers were Kyocera, Dentsply Sirona, Straumann, and Nobel Biocare. Model A was created to detect oral implants and identify the manufacturers using You Only Look Once (YOLO) v7. After preparing the image patches that cropped the implant regions detected by model A, model B was created to identify the implant types per manufacturer using EfficientNet. Model A achieved very high performance, with recall of 1.000, precision of 0.979, and F1 score of 0.989. It also had accuracy, recall, precision, and F1 score of 0.98 or higher for the classification of the manufacturers. Model B had high classification metrics above 0.92, exception for Nobel's class 2 (Parallel). In this study, two-step deep learning models were built to detect implant regions, identify four manufacturers, and identify implant types per manufacturer.

RESUMEN

Cleidocranial dysplasia (CCD) is a rare, congenital disorder characterized by a unique constellation of skeletal and dental abnormalities. The imaging findings, combined with clinical examination, help establish a definitive diagnosis. Understanding the broad spectrum of manifestations in CCD is essential for effective management and treatment. This case report aims to provide a comprehensive overview of a 25-year-old male patient with CCD, highlighting the genetic etiologies, clinical presentation, radiological findings, and a review of current literature to enhance understanding and awareness of this rare condition.

RESUMEN

BACKGROUND: This study aimed to develop a new formula to easily estimate the vertical dimension of occlusion (VDO) by using the distance between the mental foramen on a panoramic radiograph. SUBJECTS AND METHODS: A total of 508 dentulous subjects were selected from outpatient dental clinics at the College of Dental Medicine, Al-Azhar University. The vertical dimension of the occlusion was measured using a single calibrated calliper. For each subject, a digital panoramic radiograph was taken with fixed exposure parameters. The intermental foramina distance (IMFD) was measured. The data were collected and then analysed using the IBM SPSS version 20.0 software package. (Armonk, NY: IBM Corp.). Linear regression was used to determine the relationship between the intermental foramina distance (IMFD) and the vertical dimension at occlusion (VDO). RESULTS: Pearson's correlation analysis revealed that there was a strong correlation between the intermental foramina distance (IMFD) and the VDO. Thus, a novel formula was developed for determining the VDO using panoramic radiography. CONCLUSION: The novel formula developed herein facilitated the determination of the VDO among prosthetic rehabilitation for subjects who lost vertical dimension due to loss of posterior teeth or severe wear of natural posterior teeth. Further studies are needed to determine the clinical applicability of the derived formulae for edentulous subjects.

Asunto(s)

RESUMEN

Rare genetic diseases are difficult to diagnose and this translates in patient's diagnostic odyssey! This is particularly true for more than 900 rare diseases including orodental developmental anomalies such as missing teeth. However, if left untreated, their symptoms can become significant and disabling for the patient. Early detection and rapid management are therefore essential in this context. The i-Dent project aims to supply a pre-diagnostic tool to detect rare diseases with tooth agenesis of varying severity and pattern. To identify missing teeth, image segmentation models (Mask R-CNN, U-Net) have been trained for the automatic detection of teeth on patients' panoramic dental X-rays. Teeth segmentation enables the identification of teeth which are present or missing within the mouth. Furthermore, a dental age assessment is conducted to verify whether the absence of teeth is an anomaly or a characteristic of the patient's age. Due to the small size of our dataset, we developed a new dental age assessment technique based on the tooth eruption rate. Information about missing teeth is then used by a final algorithm based on the agenesis probabilities to propose a pre-diagnosis of a rare disease. The results obtained in detecting three types of genes (PAX9, WNT10A and EDA) by our system are very promising, providing a pre-diagnosis with an average accuracy of 72 %.

Asunto(s)

RESUMEN

The aim of this study was to develop an optimal, simple, and lightweight deep learning convolutional neural network (CNN) model to detect the presence of mesiodens on panoramic radiographs. A total of 628 panoramic radiographs with and without mesiodens were used as training, validation, and test data. The training, validation, and test dataset were consisted of 218, 51, and 40 images with mesiodens and 203, 55, and 61 without mesiodens, respectively. Unclear panoramic radiographs for which the diagnosis could not be accurately determined and other modalities were required for the final diagnosis were retrospectively identified and employed as the training dataset. Four CNN models provided within software supporting the creation of neural network models for deep learning were modified and developed. The diagnostic performance of the CNNs was evaluated according to accuracy, precision, recall and F1 scores, receiver operating characteristics (ROC) curves, and area under the ROC curve (AUC). In addition, we used SHapley Additive exPlanations (SHAP) to attempt to visualize the image features that were important in the classifications of the model that exhibited the best diagnostic performance. A binary_connect_mnist_LeNet model exhibited the best performance of the four deep learning models. Our results suggest that a simple lightweight model is able to detect mesiodens. It is worth referring to AI-based diagnosis before an additional radiological examination when diagnosis of mesiodens cannot be made on unclear images. However, further revaluation by the specialist would be also necessary for careful consideration because children are more radiosensitive than adults.

RESUMEN

BACKGROUND: Osteogenesis imperfecta (OI) is an inherited disorder characterized by bone fragility and skeletal alterations. The administration of bisphosphonates (BPs) to patients with OI reduces pain, thereby improving their quality of life. The main mechanism of action of BPs is the inhibition of osteoclast action. In the oral cavity of children with OI during growth and development, physiological processes that require the function of osteoclasts occur. The aim of this investigation was to study the dental development of premolars and the root resorption of primary molars in children with OI medicated with BPs according to age and sex. METHODS: An observational and analytical study was designed. The study sample consisted of 26 6- to 12-year-old children with a confirmed diagnosis of OI treated with BPs with available panoramic radiographs. The control group consisted of 395 children with available panoramic radiographs. Both groups were divided into subgroups according to sex and age. The third quadrant was studied, focusing on the first left temporary molar (7.4), the second left temporary molar (7.5), the first left permanent premolar (3.4) and the second left permanent premolar (3.5). The Demirjian method was used to study the dental development of 3.4 and 3.5, and the Haavikko method was used to study the root resorption of 7.4 and 7.5. The Mann‒Whitney U test was used for comparisons, and p < 0.05 indicated statistical significance. RESULTS: The mean chronological age of the 421 patients was 9.21 years (95% CI 9.05-9.37). The sample was reasonably balanced by sex, with 52.5% (221 patients) boys versus 47.5% (200 patients) girls. Delayed exfoliation and tooth development were described in children with OI (p = 0.05). According to sex, the root resorption of primary molars and tooth development were significantly lower in boys in both groups and in girls in the OI group, but the differences between the age groups were not significant. CONCLUSIONS: Children with OI treated with BPs exhibit delayed dental development of the premolars and delayed root resorption of the primary molars. Boys exhibited delays in both variables, but the differences by age subgroup were not significant. These clinical findings support the importance of clinically and radiographically monitoring the dental development and root resorption of primary teeth in children with OI treated with BPs to avoid alterations of the eruptive process.

Asunto(s)

RESUMEN

Background: The canine plays a vital role in dentofacial aesthetics and function. It supports the base of the alar and upper lip, which are crucial for smile aesthetics. When impacted, these functions are lost, leading to low self-esteem and overall poor health-related quality of life. The present study was conducted to find the prevalence of impacted and transmigrated canines in orthodontic patients and also to find the most prevalent type of canine impaction. Materials and Methods: This retrospective study was conducted in a hospital setting at Dental College. A total of 3050 OPGs (Orthopantomagram) of patients who visited dental hospitals for orthodontic treatment constituted the final sample. Demographic details regarding age, gender, and place of residence were collected from the patients. Evaluation of sample radiographs on the standard light box was performed to collect data regarding impacted and transmigrated canines. Statistical analysis was performed using descriptive statistics and Chi-square test. Results: Prevalence of impacted canine was found to be 2.46%. Impacted canine prevalence of 1.53% and 2.85% was reported in males and females, respectively. Only two female patients had transmigrated mandibular impacted canines. Comparison of arch showed a statistically significant (P value 0.02) higher prevalence in the maxillary arch, which was 1.54%, and in the mandibular arch, it was 0.92%. The present study reported significantly more unilateral impactions (P value 0.00) than bilateral impactions. Conclusion: The overall prevalence for impacted canine was 2.46%. Prevalence was higher in female patients. Early diagnosis of impacted canines is vital for planning orthodontic treatment in such patients.

RESUMEN

BACKGROUND: Preoperative assessment of the impacted mandibular third molar (LM3) in a panoramic radiograph is important in surgical planning. The aim of this study was to develop and evaluate a computer-aided visualisation-based deep learning (DL) system using a panoramic radiograph to predict the difficulty level of surgical removal of an impacted LM3. METHODS: The study included 1367 LM3 images from 784 patients who presented from 2021-2023 to the University Dental Hospital; images were collected retrospectively. The difficulty level of surgically removing impacted LM3s was assessed via our newly developed DL system, which seamlessly integrated 3 distinct DL models. ResNet101V2 handled binary classification for identifying impacted LM3s in panoramic radiographs, RetinaNet detected the precise location of the impacted LM3, and Vision Transformer performed multiclass image classification to evaluate the difficulty levels of removing the detected impacted LM3. RESULTS: The ResNet101V2 model achieved a classification accuracy of 0.8671. The RetinaNet model demonstrated exceptional detection performance, with a mean average precision of 0.9928. Additionally, the Vision Transformer model delivered an average accuracy of 0.7899 in predicting removal difficulty levels. CONCLUSIONS: The development of a 3-phase computer-aided visualisation-based DL system has yielded a very good performance in using panoramic radiographs to predict the difficulty level of surgically removing an impacted LM3.

RESUMEN

AIM: To investigate the prevalence of tooth agenesis and associated dental anomalies in Latvian adolescent dental patients and compare it to other European countries. DESIGN: Cross-sectional study of 2692 11-to-14-year-old patients (39.9% males and 60.1% females) attending Riga Stradins University Institute of Stomatology with panoramic radiographs taken between August 2020 and September 2021. Patients with any genetic syndromes were excluded. Data on tooth agenesis (excluding third molars) and other dental anomalies were recorded. RESULTS: The prevalence of tooth agenesis in Latvian adolescent dental patients was 9.3% with no statistically significant difference between genders (χ2 test, p = 0.472). The most commonly missing teeth were mandibular second premolars, followed by upper lateral incisors and upper second premolars. There was a statistically significant association with the presence of other dental anomalies in tooth agenesis patients (p < 0.001). CONCLUSIONS: This study found that the prevalence of non-syndromic tooth agenesis in Latvian adolescent dental patients was 9.3% with no statistically significant differences between the genders. Patients with tooth agenesis have a statistically significant possibility of the presence of other dental anomalies (p < 0.001).

Asunto(s)

RESUMEN

Background/Objectives: The purpose of this preliminary study was to evaluate the diagnostic performance of an AI-driven platform, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA), for assessing endodontic treatment outcomes using panoramic radiographs (PANs). Materials and Methods: The study included 55 PAN images of 55 patients (15 males and 40 females, aged 12-70) who underwent imaging at a private dental center. All images were acquired using a Hyperion X9 PRO digital cephalometer and were evaluated using Diagnocat, a cloud-based AI platform. The AI system assessed the following endodontic treatment features: filling probability, obturation adequacy, density, overfilling, voids in filling, and short filling. Two human observers independently evaluated the images, and their consensus served as the reference standard. The diagnostic accuracy metrics were calculated. Results: The AI system demonstrated high accuracy (90.72%) and a strong F1 score (95.12%) in detecting the probability of endodontic filling. However, the system showed variable performance in other categories, with lower accuracy metrics and unacceptable F1 scores for short filling and voids in filling assessments (8.33% and 14.29%, respectively). The accuracy for detecting adequate obturation and density was 55.81% and 62.79%, respectively. Conclusions: The AI-based system showed very high accuracy in identifying endodontically treated teeth but exhibited variable diagnostic accuracy for other qualitative features of endodontic treatment.

RESUMEN

Stroke is one of the major causes of death worldwide, and is closely associated with atherosclerosis of the carotid artery. Panoramic radiographs (PRs) are routinely used in dental practice, and can be used to visualize carotid artery calcification (CAC). The purpose of this study was to automatically and robustly classify and segment CACs with large variations in size, shape, and location, and those overlapping with anatomical structures based on deep learning analysis of PRs. We developed a cascaded deep learning network (CACSNet) consisting of classification and segmentation networks for CACs on PRs. This network was trained on ground truth data accurately determined with reference to CT images using the Tversky loss function with optimized weights by balancing between precision and recall. CACSNet with EfficientNet-B4 achieved an AUC of 0.996, accuracy of 0.985, sensitivity of 0.980, and specificity of 0.988 in classification for normal or abnormal PRs. Segmentation performances for CAC lesions were 0.595 for the Jaccard index, 0.722 for the Dice similarity coefficient, 0.749 for precision, and 0.756 for recall. Our network demonstrated superior classification performance to previous methods based on PRs, and had comparable segmentation performance to studies based on other imaging modalities. Therefore, CACSNet can be used for robust classification and segmentation of CAC lesions that are morphologically variable and overlap with surrounding structures over the entire posterior inferior region of the mandibular angle on PRs.

Asunto(s)

RESUMEN

INTRODUCTION: Evaluation of the eruption of mandibular third molars in orthopantomograms (OPGs) is a method of forensic age assessment. The objective of our study was to provide valid reference data for this trait within a population of black South Africans. The study was guided by the criteria for reference studies in age assessment. MATERIALS AND METHODS: A study population from Pretoria, South Africa comprising 670 OPGs obtained from 338 black females and 332 black males aged between 15.00 and 25.97 years was analysed. All OPGs were performed for medical indication during the period from 2011 to 2022 and were retrospectively evaluated. From the 670 OPGs, a total of 1021 mandibular third molars were evaluated. The assessment of mandibular third molars was conducted using the staging scale presented by Olze et al. in 2012. Two experienced dentists evaluated the OPGs independently of each other. If the two examiners diverged in their assessments, a consensus stage was assigned. RESULTS: As expected, the mean, median and minimal age increased with higher stages for both teeth and both sexes. The minimum age recorded for stage D, indicating complete tooth eruption, was 15.79 years in females and 16.62 years in males. CONCLUSION: As it is the case for previous reference studies in other countries, placing exclusive reliance on the evaluation of mandibular third molar eruption is inadequate for ascertaining the age of majority among Black South Africans. Future studies need to examine if our results are transferable to other countries in Sub-Saharan Africa.

Asunto(s)

RESUMEN

INTRODUCTION: Accurate bracket positioning remains challenging. To avoid angulation errors, some recommend examining the panoramic radiograph during bonding. However, it can cause distortions. Cone-beam computed tomography (CBCT) provides a more precise panoramic reconstruction but with higher radiation doses. The main objective of this study is to compare the accuracy of axial positioning between direct bonding without radiography, with conventional panoramic radiograph, and with panoramic reconstruction from CBCT. The secondary objectives are to evaluate positioning accuracy of each tooth and to assess the influence of practitioner level of experience. METHODS: Thirty practitioners, divided into two groups based on their experience performed direct bonding on a model thrice: without radiography, then with the conventional panoramic radiograph, then with the panoramic reconstruction from CBCT. Models were scanned, and angulation errors were measured using OrthoAnalyzer. Values were compared using the Friedman's test followed by the Bonferroni correction for multiple comparisons (P-value = 0.05). RESULTS: For the low level of experience group, angulation errors were significantly greater than the accepted limit without radiographic reference, and significantly lower with CBCT reconstruction. For the high level of experience group, angulation errors were significantly lower than the accepted limit for the three bonding methods. For every tooth, using the panoramic reconstruction from CBCT as a reference, was the most accurate method, regardless of the level of experience. More experienced practitioners made fewer errors for the three methods. CONCLUSIONS: Panoramic reconstruction from CBCT is the most accurate method to limit angulation errors during direct bonding. Conventional panoramic radiography remains a reliable tool if used with caution. Bonding without any radiographic reference should be avoided especially for less experienced practitioners.

RESUMEN

BACKGROUND: Diffuse idiopathic skeletal hyperostosis (DISH) is a potentially serious osteopathic disorder associated with coalescing ossifications of the anterior vertebrae and may be concomitant with a constellation of symptomatology and systemic comorbidities. There is limited dental literature describing this finding on panoramic radiographs and cone beam computed tomography (CBCT) scans. CASE PRESENTATIONS: Two case reports of DISH are provided. One patient manifested extensive ossifications along the cervical vertebrae and consequent episodes of dysphagia and hoarseness. The other affected patient's cervical ossification was found at an earlier stage of formation and without symptomatology. Panoramic radiography, cervical spine radiography, and CBCT examinations have been provided. CONCLUSION: Attending dental healthcare practitioners should carefully evaluate all areas of interest and surrounding fields of view when taking panoramic radiographs and CBCT scans for manifestations of DISH and other potential disorders of the cervical vertebrae. A suspected radiologic finding of DISH should prompt timely referral for comprehensive medical assessment to mitigate neurologic deficits and other comorbidities.

RESUMEN

Introduction Fractal analysis has proved to be a salient tool to quantitatively assess the qualitative changes in the bone trabeculae of patients with hyperparathyroidism, osteoporosis, and various temporomandibular disorders, including osteoarthritis (OA) and rheumatoid arthritis of temporomandibular joint (TMJ), in several previous studies. The purpose of this study was to use fractal analysis to assess alterations in the trabecular pattern of the mandibular condyle in patients with degenerative temporomandibular disorders. Materials and methods This study comprised 98 subjects with 49 subjects in the study group and 49 subjects in the control group, aged 18-50 years. Age and sex in the control group were matched to those in the case group. The subjects were assessed clinically with the Diagnostic Criteria for Temporomandibular Disorders. Digital panoramic X-ray equipment with set parameters of 70 kvp, 8 mA, and 16-second exposure duration was used to take panoramic radiographs. Fractal analysis was done and the calculated fractal dimension value was obtained using ImageJ version 1.48 software (National Institutes of Health, Bethesda, MD). The same observer used Muir and Goss's method to rate the total degenerative changes in the condylar surfaces, which were substantiated by the calculated fractal dimension value. The data were statistically analyzed. Results The results revealed a significant difference (p-value = 0.041) between the mean fractal value in the case group's (1.35) and the control group's (1.38) left sides whereas the differences in the mean fractal values between the case and control groups on the right side was not significant (p-value = 0.49). Conclusion It is recommended to use the fractal dimension value and the total degenerative severity score together to quantify degenerative changes in the TMJ OA rather than exclusively relying on fractal value.

RESUMEN

Background/Objectives: Periapical lesions (PLs) are frequently detected in dental radiology. Accurate diagnosis of these lesions is essential for proper treatment planning. Imaging techniques such as orthopantomogram (OPG) and cone-beam CT (CBCT) imaging are used to identify PLs. The aim of this study was to assess the diagnostic accuracy of artificial intelligence (AI) software Diagnocat for PL detection in OPG and CBCT images. Methods: The study included 49 patients, totaling 1223 teeth. Both OPG and CBCT images were analyzed by AI software and by three experienced clinicians. All the images were obtained in one patient cohort, and findings were compared to the consensus of human readers using CBCT. The AI's diagnostic accuracy was compared to a reference method, calculating sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Results: The AI's sensitivity for OPG images was 33.33% with an F1 score of 32.73%. For CBCT images, the AI's sensitivity was 77.78% with an F1 score of 84.00%. The AI's specificity was over 98% for both OPG and CBCT images. Conclusions: The AI demonstrated high sensitivity and high specificity in detecting PLs in CBCT images but lower sensitivity in OPG images.

RESUMEN

Aim: To report the radiographic preferences during dental implant therapy in Palestine. Materials and Methods: Fourteen multiple-choice questions were delivered in electronic and hardcopy formats questionnaires during the Sixth International Implantology Conference (Palestine). The questions investigated the radiographic techniques that are mostly used based on various clinical scenarios and treatment phases. Results: One hundred and thirty-seven responses were captured. The majority of the participants were general dentists with implant experience (79.6%). Less than a third of the participants (27.2%) were members of the Palestinian Association of Dental Implantology. The majority (85.9%) of them have their practice in a city zone. Panoramic radiograph (PAN) combined with cone beam computed tomography (CBCT) was the most preferred radiographic technique during the planning stage. Conclusion: PAN and CBCT was the preferred choice during the planning stages. A PAN was preferred postoperatively and if no complications were associated. In the case of symptomatic patients, CBCT was the radiograph of choice.