RESUMEN
This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution.
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Determinación de la Edad por los Dientes , Aprendizaje Automático , Radiografía Panorámica , Humanos , Radiografía Panorámica/métodos , Brasil , Niño , Adulto , Adolescente , Preescolar , Anciano , Persona de Mediana Edad , Adulto Joven , Determinación de la Edad por los Dientes/métodos , Femenino , Anciano de 80 o más Años , MasculinoRESUMEN
This study reviews the two most important and frequently used systems of tomography used in dentistry today. These are the dental panoramic radiograph (DPR) and cone-beam computed tomography (CBCT). The importance of the DPR has been accentuated by the recent COVID-19 pandemic, as it does not produce an aerosol. Its clinical importance is derived from its panoramic display of the jaws and associated structures and should be examined for incidental findings that may portend a potentially serious outcome. An important recent spin-off of the DPR is the extra-oral bitewing, which can replace its traditional, uncomfortable and aerosol-generating intra-oral counterpart. Although much has been written about them, this paper reviews their essential attributes and limitations in clinical dentistry. Although attempts have been made to reproduce some of the attributes of CT in CBCT such as Hounsfield Units (HU) and improve the contrast resolution of the soft tissues, these remain elusive. Nevertheless, CBCT's dataset should be appropriately reconstructed to fully display the clinical feature prompting its prescription. In certain cases, more than one mode of reconstruction is required.
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COVID-19 , Tomografía Computarizada de Haz Cónico , Radiografía Panorámica , Humanos , Tomografía Computarizada de Haz Cónico/métodos , Radiografía Panorámica/métodos , COVID-19/diagnóstico por imagen , SARS-CoV-2 , Radiografía Dental/métodosRESUMEN
BACKGROUND: We aimed to determine the feasibility of utilizing deep learning-based predictions of the indications for cracked tooth extraction using panoramic radiography. METHODS: Panoramic radiographs of 418 teeth (group 1: 209 normal teeth; group 2: 209 cracked teeth) were evaluated for the training and testing of a deep learning model. We evaluated the performance of the cracked diagnosis model for individual teeth using InceptionV3, ResNet50, and EfficientNetB0. The cracked tooth diagnosis model underwent fivefold cross-validation with 418 data instances divided into training, validation, and test sets at a ratio of 3:1:1. RESULTS: To evaluate the feasibility, the sensitivity, specificity, accuracy, and F1 score of the deep learning models were calculated, with values of 90.43-94.26%, 52.63-60.77%, 72.01-75.84%, and 76.36-79.00%, respectively. CONCLUSION: We found that the indications for cracked tooth extraction can be predicted to a certain extent through a deep learning model using panoramic radiography.
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Aprendizaje Profundo , Radiografía Panorámica , Extracción Dental , Radiografía Panorámica/métodos , Humanos , Síndrome de Diente Fisurado/diagnóstico por imagen , Estudios de Factibilidad , Sensibilidad y EspecificidadRESUMEN
BACKGROUND AND AIM: Dental caries is largely preventable, yet an important global health issue. Numerous systematic reviews have summarized the efficacy of artificial intelligence (AI) models for the diagnosis and detection of dental caries. Therefore, this umbrella review aimed to synthesize the results of systematic reviews on the application and effectiveness of AI models in diagnosing and detecting dental caries. METHODS: MEDLINE/PubMed, IEEE Explore, Embase, and Cochrane Database of Systematic Reviews were searched to retrieve studies. Two authors independently screened the articles based on eligibility criteria and then, appraised the included articles. The findings are summarized in tabulation form and discussed using the narrative method. RESULT: A total of 1249 entries were identified out of which 7 were finally included. The most often employed AI algorithms were the multilayer perceptron, support vector machine (SVM), and neural networks. The algorithms were built to perform the segmentation, classification, caries detection, diagnosis, and caries prediction from several sources, including periapical radiographs, panoramic radiographs, smartphone images, bitewing radiographs, near-infrared light transillumination images, and so forth. Convoluted neural networks (CNN) demonstrated high sensitivity, specificity, and area under the curve in the caries detection, segmentation, and classification tests. Notably, AI in conjunction with periapical and panoramic radiography images yielded better accuracy in detecting and diagnosing dental caries. CONCLUSION: AI models, especially convolutional neural network (CNN)-based models, have an enormous amount of potential for accurate, objective dental caries diagnosis and detection. However, ethical considerations and cautious adoption remain critical to its successful integration into routine practice.
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Inteligencia Artificial , Caries Dental , Humanos , Caries Dental/diagnóstico por imagen , Redes Neurales de la Computación , Radiografía Panorámica/métodos , Máquina de Vectores de Soporte , Revisiones Sistemáticas como AsuntoRESUMEN
The assessment of majority age is important for determining legal responsibility. The definition of the 3rd molar maturity index (Im3) have proven to be a simple and effective method of majority age establishment, the accuracy of which has been tested in different populations. There is a clear lack of studies in this scientific area in Russia. OBJECTIVE: To test diagnostic accuracy of majority age assessment method by 3rd molar (Cameriere index) in the Ufa sample. MATERIAL AND METHODS: The number of orthopantomograms equal 120 from males and females aged from 14 to 23 years without apparent pathological changes of the pulp of teeth 38 and 48 was examined. The measures were conducted using the standard R. Cameriere method. The method of logistic regression, determination of predictive values of sensitivity and specificity were used. RESULTS: The Im3 cut-off point, that was equal 0.08, had a sensitivity of 89% and specificity of 95% in the male sample, accuracy of age group determination was 93%. In the female sample the method sensitivity was 93%, specificity - 97%, accuracy - 97%. CONCLUSION: The study results confirmed the absence of population variability of the Cameriere index value, that makes it possible to use the majority age determination method by 3rd molar for practical application in the examination of a living person at the territory of the Russian Federation.
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Determinación de la Edad por los Dientes , Tercer Molar , Radiografía Panorámica , Humanos , Determinación de la Edad por los Dientes/métodos , Masculino , Femenino , Adolescente , Federación de Rusia , Tercer Molar/crecimiento & desarrollo , Tercer Molar/diagnóstico por imagen , Radiografía Panorámica/métodos , Adulto Joven , Adulto , Sensibilidad y Especificidad , Odontología Forense/métodosRESUMEN
OBJECTIVES: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs. METHODS: A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed. RESULTS: The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation. CONCLUSIONS: YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition.
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Aprendizaje Profundo , Dentición Mixta , Odontología Pediátrica , Radiografía Panorámica , Diente , Radiografía Panorámica/métodos , Aprendizaje Profundo/normas , Diente/diagnóstico por imagen , Humanos , Preescolar , Niño , Adolescente , Masculino , Femenino , Odontología Pediátrica/métodosRESUMEN
Background: The stage of tooth formation is one of the most reliable indicators for predicting a patient's developmental age by radiographs. This study compared the accuracy of three distinct dental age estimation methods (Demirjian, Nolla, and Willems) in children aged 3-17 in the northern Iranian population. Methods: This cross-sectional study examined panoramic radiographs of 434 children aged 3-17 from Mazandaran Province, Iran, who had teeth 31-37 present on the left mandible. This study employed the Demirjian, Nolla, and Willems methods to estimate the dental age of the sample and compare it with the chronological age. The data were analyzed using SPSS v16. A paired t-test was used to compare chronological and dental ages. The Pearson correlation was used to correlate the chronological and dental ages. The errors of different methods were compared using the Wilcoxon test. P values < 0.05 were considered significant for all tests except Wilcoxon. For Wilcoxon, a P value < 0.017 was considered significant. Results: The three methods presented differing mean estimated ages. The Demirjian method delivered the highest mean, and all three methods differed significantly when compared in pairs. The results showed that the Demirjian method overestimated chronological age by 0.25 years (P < 0.001) in girls and 0.09 years (P = 0.28) in boys. The Willems method underestimated chronological age by 0.05 years (P = 0.47) in girls and 0.12 years (P = 0.13) in boys. The Nolla method underestimated chronological age by 0.41 years (P < 0.001) in girls and 0.40 years (P < 0.001) in boys. The accuracy of each method varied with the patient's age. Conclusion: According to the findings, the Willems method outperformed the Demirjian method, and the Demirjian method exceeded the Nolla method for estimating dental age in Iranian children aged 3-17. Overall, the Demirjian method overestimated the age of the study population, whereas the other two underestimated it.
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Determinación de la Edad por los Dientes , Radiografía Panorámica , Diente , Humanos , Niño , Femenino , Adolescente , Masculino , Irán , Determinación de la Edad por los Dientes/métodos , Radiografía Panorámica/métodos , Preescolar , Estudios Transversales , Diente/diagnóstico por imagen , Diente/crecimiento & desarrollo , Mandíbula/diagnóstico por imagen , Mandíbula/crecimiento & desarrolloRESUMEN
PURPOSE: This study aims to develop a deep learning framework for the automatic detection of the position relationship between the mandibular third molar (M3) and the mandibular canal (MC) on panoramic radiographs (PRs), to assist doctors in assessing and planning appropriate surgical interventions. METHODS: Datasets D1 and D2 were obtained by collecting 253 PRs from a hospitals and 197 PRs from online platforms. The RPIFormer model proposed in this study was trained and validated on D1 to create a segmentation model. The CycleGAN model was trained and validated on both D1 and D2 to develop an image enhancement model. Ultimately, the segmentation and enhancement models were integrated with an object detection model to create a fully automated framework for M3 and MC detection in PRs. Experimental evaluation included calculating Dice coefficient, IoU, Recall, and Precision during the process. RESULTS: The RPIFormer model proposed in this study achieved an average Dice coefficient of 92.56 % for segmenting M3 and MC, representing a 3.06 % improvement over the previous best study. The deep learning framework developed in this research enables automatic detection of M3 and MC in PRs without manual cropping, demonstrating superior detection accuracy and generalization capability. CONCLUSION: The framework developed in this study can be applied to PRs captured in different hospitals without the need for model fine-tuning. This feature is significant for aiding doctors in accurately assessing the spatial relationship between M3 and MC, thereby determining the optimal treatment plan to ensure patients' oral health and surgical safety.
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Aprendizaje Profundo , Mandíbula , Tercer Molar , Radiografía Panorámica , Humanos , Tercer Molar/diagnóstico por imagen , Radiografía Panorámica/métodos , Mandíbula/diagnóstico por imagen , Femenino , Masculino , AdultoRESUMEN
Most artificial intelligence (AI) studies have attempted to identify dental implant systems (DISs) while excluding low-quality and distorted dental radiographs, limiting their actual clinical use. This study aimed to evaluate the effectiveness of an AI model, trained on a large and multi-center dataset, in identifying different types of DIS in low-quality and distorted dental radiographs. Based on the fine-tuned pre-trained ResNet-50 algorithm, 156,965 panoramic and periapical radiological images were used as training and validation datasets, and 530 low-quality and distorted images of four types (including those not perpendicular to the axis of the fixture, radiation overexposure, cut off the apex of the fixture, and containing foreign bodies) were used as test datasets. Moreover, the accuracy performance of low-quality and distorted DIS classification was compared using AI and five periodontists. Based on a test dataset, the performance evaluation of the AI model achieved accuracy, precision, recall, and F1 score metrics of 95.05%, 95.91%, 92.49%, and 94.17%, respectively. However, five periodontists performed the classification of nine types of DISs based on four different types of low-quality and distorted radiographs, achieving a mean overall accuracy of 37.2 ± 29.0%. Within the limitations of this study, AI demonstrated superior accuracy in identifying DIS from low-quality or distorted radiographs, outperforming dental professionals in classification tasks. However, for actual clinical application of AI, extensive standardization research on low-quality and distorted radiographic images is essential.
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Inteligencia Artificial , Implantes Dentales , Radiografía Dental , Humanos , Radiografía Dental/métodos , Algoritmos , Radiografía Panorámica/métodosRESUMEN
OBJECTIVES: To investigate the application value of combining the Demirjian's method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents. METHODS: Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian's method. Various machine learning algorithms, including support vector regression (SVR), gradient boosting regression (GBR), linear regression (LR), random forest regression (RFR), and decision tree regression (DTR) were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated. RESULTS: SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error (MAE) of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old. CONCLUSIONS: The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered.
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Determinación de la Edad por los Dientes , Algoritmos , Pueblo Asiatico , Aprendizaje Automático , Radiografía Panorámica , Adolescente , Niño , Preescolar , Femenino , Humanos , Masculino , Adulto Joven , Determinación de la Edad por los Dientes/métodos , China/etnología , Árboles de Decisión , Pueblos del Este de Asia , Etnicidad , Mandíbula , Radiografía Panorámica/métodos , Máquina de Vectores de Soporte , Diente/diagnóstico por imagen , Diente/crecimiento & desarrolloRESUMEN
BACKGROUND: Panoramic radiography (PR) is available to determine the contact relationship between maxillary molar teeth (MMT) and the maxillary sinus floor (MSF). However, as PRs do not provide clear and detailed anatomical information, advanced imaging methods can be used. AIM: The aim of this study was to evaluate the diagnostic performance of deep learning (DL) applications that assess the relationship of the MSF to the first maxillary molar teeth (fMMT) and second maxillary molar teeth (sMMT) on PRs with data confirmed by cone beam computed tomography (CBCT). METHODS: A total of 2162 fMMT and sMMT were included in this retrospective study. The contact relationship of teeth with MSF was compared among DL methods. RESULTS: DL methods, such as GoogLeNet, VGG16, VGG19, DarkNet19, and DarkNet53, were used to evaluate the contact relationship between MMT and MSF, and 85.89% accuracy was achieved by majority voting. In addition, 88.72%, 81.19%, 89.39%, and 83.14% accuracy rates were obtained in right fMMT, right sMMT, left fMMT, and left sMMT, respectively. CONCLUSION: DL models showed high accuracy values in detecting the relationship of fMMT and sMMT with MSF.
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Tomografía Computarizada de Haz Cónico , Aprendizaje Profundo , Seno Maxilar , Diente Molar , Radiografía Panorámica , Humanos , Tomografía Computarizada de Haz Cónico/métodos , Radiografía Panorámica/métodos , Seno Maxilar/diagnóstico por imagen , Estudios Retrospectivos , Femenino , Diente Molar/diagnóstico por imagen , Masculino , Adulto , Maxilar/diagnóstico por imagen , Persona de Mediana Edad , Adulto JovenRESUMEN
BACKGROUND: Research using panoramic X-ray images using deep learning has been progressing in recent years. There is a need to propose methods that can classify and predict from image information. OBJECTIVE: In this study, Eichner classification was performed on image processing based on panoramic X-ray images. The Eichner classification was based on the remaining teeth, with the aim of making partial dentures. This classification was based on the condition that the occlusal position was supported by the remaining teeth in the upper and lower jaws. METHODS: Classification models were constructed using two convolutional neural network methods: the sequential and VGG19 models. The accuracy was compared with the accuracy of Eichner classification using the sequential and VGG19 models. RESULTS: Both accuracies were greater than 81%, and they had sufficient functions for the Eichner classification. CONCLUSION: We were able to build a highly accurate prediction model using deep learning scratch sequential model and VGG19. This predictive model will become part of the basic considerations for future AI research in dentistry.
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Aprendizaje Profundo , Redes Neurales de la Computación , Radiografía Panorámica , Radiografía Panorámica/métodos , Proyectos Piloto , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Masculino , Femenino , Persona de Mediana Edad , AdultoRESUMEN
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.
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Arterias Carótidas , Aprendizaje Profundo , Radiografía Panorámica , Calcificación Vascular , Humanos , Radiografía Panorámica/métodos , Arterias Carótidas/diagnóstico por imagen , Arterias Carótidas/patología , Calcificación Vascular/diagnóstico por imagen , Enfermedades de las Arterias Carótidas/diagnóstico por imagen , Femenino , Masculino , Anciano , Persona de Mediana Edad , Tomografía Computarizada por Rayos X/métodosRESUMEN
Forensic age assessment in the living can provide legal certainty when an individual's chronological age is unknown or when age-related information is questionable. An established method involves assessing the eruption of mandibular third molars through dental panoramic radiographs (PAN). In age assessment procedures, the respective findings are compared to reference data. The objective of this study was to generate new reference data in line with the required standards for mandibular third molar eruption within a German population. For this purpose, 605 PANs from 302 females and 303 males aged 15.04 to 25.99 years were examined. The PANs were acquired between 2013 and 2020, and the development of the mandibular third molars was rated independently by two experienced examiners using the Olze et al. staging scale from 2012. In case of disagreement in the assigned ratings, a consensus was reached through arbitration. While the mean, median and minimum ages were observed to increase with each stage of mandibular third molar eruption according to the Olze method, there was considerable overlap in the distribution of age between the stages. The minimum age for stage D, which corresponds to complete tooth eruption, was 16.1 years for females and 17.1 years for males. Thus, the completion of mandibular third molar eruption was found in both sexes before reaching the age of 18. In all individuals who had at least one tooth with completed eruption and who were younger than 17.4 years of age (n = 10), mineralization of the teeth in question was not complete. Based on our findings, the feature of assessing mandibular third molar eruption in PAN cannot be relied upon for determining age of majority.
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Determinación de la Edad por los Dientes , Tercer Molar , Radiografía Panorámica , Erupción Dental , Humanos , Radiografía Panorámica/métodos , Tercer Molar/diagnóstico por imagen , Masculino , Femenino , Determinación de la Edad por los Dientes/métodos , Adolescente , Erupción Dental/fisiología , Alemania , Adulto , Adulto Joven , Valores de ReferenciaRESUMEN
PURPOSE: To analyze the visibility of the maxillary sinus septa (MSS) in panoramic radiography (PR) versus cone beam computed tomography (CBCT) and to investigate whether the buccal cortical bone thickness (BT) or the septa dimensions influence their visibility. METHODS: Corresponding PR and CBCT images of 355 patients were selected and examined for MSS visibility. The septa dimensions (width, height, depth) and the BT were measured. Results were analysed statistically. RESULTS: Comparing the corresponding regions on CBCT and PR, 170 MSS were identified; however, only 106 of these were also visible using PR. The MSS visibility was significantly higher on CBCT versus PR images (P1: p = 0.039, P2: p = 0.015, M1: p = 0.041, M2: p = 0.017, M3: p = 0.000), except region C (p = 0.625). Regarding the measurements of MSS dimensions, only the height in region M1 (p = 0.013) and the width in region P2 (p = 0.034) were significantly more visible on CBCT. The BT in the area of the MSS was found to have a marginal influence on its visibility on the PR images only in regions M3 and M1 (M3: p = 0.043, M1: p = 0.047). In terms of MSS visibility based on the dimensions, significance was found for all three influencing variables only in region P2 (width; p = 0.041, height; p = 0.001, depth; p = 0.007). There were only isolated cases of further significance: M3 for width (p = 0.043), M2 for height (p = 0.024), and P1 for depth (p = 0.034), no further significance was noted. CONCLUSION: MSS visibility appears significantly higher on CBCT versus PR images. It is concluded that the septa dimensions and BT can influence MSS visibility on PR images just in certain regions.
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Tomografía Computarizada de Haz Cónico , Hueso Cortical , Seno Maxilar , Radiografía Panorámica , Humanos , Tomografía Computarizada de Haz Cónico/métodos , Radiografía Panorámica/métodos , Seno Maxilar/diagnóstico por imagen , Seno Maxilar/anatomía & histología , Estudios Retrospectivos , Masculino , Femenino , Persona de Mediana Edad , Adulto , Hueso Cortical/diagnóstico por imagen , Hueso Cortical/anatomía & histología , Anciano , Adulto Joven , Anciano de 80 o más AñosRESUMEN
Objectives: The aim of the review is to evaluate the existing precision of artificial intelligence (AI) in detecting Marginal Bone Loss (MBL) around prosthetic crowns using 2-Dimentional radiographs. It also summarises the recent advances and future challenges associated to their clinical application. Methodology: A literature survey of electronic databases was conducted in November 2023 to recognize the relevant articles. MeSH terms/keywords were used to search ("panoramic" OR "pantomogram" OR "orthopantomogram" OR "opg" OR "periapical") AND ("artificial intelligence" OR "deep" OR "machine" OR "automated" OR "learning") AND ("periodontal bone loss") AND ("prosthetic crown") in PubMed database, SCOPUS, COCHRANE library, EMBASE, CINAHL and Science Direct. RESULTS: The searches identified 49 relevant articles, of them 5 articles met the inclusion criteria were included. The outcomes measured were sensitivity, specificity and accuracy of AI models versus manual detection in panoramic and intraoral radiographs. Few studies reported no significant difference between AI and manual detection, whereas majority demonstrated the superior ability of AI in detecting MBL. CONCLUSIONS: AI models show promising accuracy in analysing complex datasets and generate accurate predictions in the MBL around fixed prosthesis. However, these models are still in the developmental phase. Therefore, it is crucial to assess the effectiveness and reliability of these models before recommending their use in clinical practice.
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Pérdida de Hueso Alveolar , Inteligencia Artificial , Humanos , Pérdida de Hueso Alveolar/diagnóstico por imagen , Pérdida de Hueso Alveolar/etiología , Coronas/efectos adversos , Radiografía Panorámica/métodos , Sensibilidad y EspecificidadRESUMEN
BACKGROUND/AIM: The styloid process (SP) becomes clinically relevant when it shows enlargement (>30 mm) in the sense of an elongated SP (ESP) and/or increasing calcification leading to Eagle Syndrome (ES). Panoramic radiograph (PR) or computed tomography (CT) are part of the routine diagnostics in ES. Currently, CT is considered the gold standard. The aim of this study was to investigate the accuracy in the diagnostics/measurements of SP/ESP throughout a comparative study between PR and CT. Furthermore, in addition to measuring established parameters, this study aimed to determine the currently unexamined width in the base and tip of the SP. PATIENTS AND METHODS: The present study examined the radiological findings of bilateral SP in 100 patients who received both PR and CT on the same day. Measurements of the length of the SP and width at the basis and tip were performed. Furthermore, calcification patterns, Langlais classification and the prevalence of ESP were analyzed. RESULTS: There was a highly significant correlation between PR and CT measuring SP for every parameter. Males showed significantly longer SP than females among the age group between 18-75 years. The results of the length measurements of the SP (male: right SP=32.98 mm; left SP=35.21 mm; female: right SP=30.31 mm; left SP=30.92 mm) significantly exceeded the values of comparable studies. CONCLUSION: Consequently, it can be concluded that PR provides accurate measurements when compared to CT for measuring and diagnosing SP/ESP/Eagle syndrome. This study was one of the first to examine the width of the SP in the base and tip, thus these measurements can serve as a baseline for further studies. Since the mean lengths of SP exceeded 30.0 mm in the present study, these findings raise the question of whether the cut-off of 30.0 mm is adequate for the diagnosis of ESP.
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Radiografía Panorámica , Hueso Temporal , Hueso Temporal/anomalías , Tomografía Computarizada por Rayos X , Humanos , Masculino , Femenino , Persona de Mediana Edad , Adulto , Hueso Temporal/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Anciano , Radiografía Panorámica/métodos , Adolescente , Adulto Joven , Osificación Heterotópica/diagnóstico por imagen , Osificación Heterotópica/diagnósticoRESUMEN
PURPOSE: To assess the impact of endoscope-assisted fractured roots or fragments extraction within the mandibular canal, along with quantitative sensory testing (QST) alterations in the inferior alveolar nerve (IAN). METHODS: Six patients with lower lip numbness following mandibular third molar extraction were selected. All patients had broken roots or fragments within the mandibular canal that were extracted under real-time endoscopic assistance. Follow-up assessments were conducted on postoperative days 1, 7, and 35, including a standardized QST of the lower lip skin. RESULTS: The average surgical duration was 32.5 min, with the IAN exposed in all cases. Two of the patient exhibited complete recovery of lower lip numbness, three experienced symptom improvement, and one patient remained unaffected 35 days after the surgery. Preoperative QST results showed that the mechanical detection and pain thresholds on the affected side were significantly higher than those on the healthy side, but improved significantly by postoperative day 7 in five patients, and returned to baseline in two patients on day 35. There were no significant differences in the remaining QST parameters. CONCLUSIONS: All endoscopic surgical procedures were successfully completed without any additional postoperative complications. There were no cases of deterioration of IAN injury, and lower lip numbness recovered in the majority of cases. Endoscopy allowed direct visualization and examination of the affected nerve, facilitating a comprehensive analysis of the IAN.
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Diente Impactado , Traumatismos del Nervio Trigémino , Humanos , Estudios Retrospectivos , Hipoestesia/complicaciones , Hipoestesia/cirugía , Canal Mandibular , Traumatismos del Nervio Trigémino/etiología , Mandíbula/cirugía , Nervio Mandibular , Extracción Dental/efectos adversos , Extracción Dental/métodos , Tercer Molar/cirugía , Diente Impactado/cirugía , Radiografía Panorámica/métodosRESUMEN
BACKGROUND: The most severe complication that can occur after mandibular third molar (MM3) surgery is inferior alveolar nerve (IAN) damage. It is crucial to have a comprehensive radiographic evaluation to reduce the possibility of nerve damage. The objective of this study is to assess the diagnostic accuracy of panoramic radiographs (PR) and posteroanterior (PA) radiographs in identifying the association between impacted MM3 roots and IAN. METHODS: This study included individuals who had PR, PA radiographs, and cone beam computed tomography (CBCT) and who had at least one impacted MM3. A total of 141 impacted MM3s were evaluated on CBCT images, and the findings were considered gold standard. The relationship between impacted MM3 roots and IAN was also evaluated on PR and PA radiographies. The data was analyzed using the McNemar and Chi-squared tests. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of PR and PA radiographies were determined. RESULTS: Considering CBCT the gold standard, the relationship between MM3 roots and IAN was found to be statistically significant between PR and CBCT (p = 0.00). However, there was no statistically significant relationship between PA radiography and CBCT (0.227). The study revealed that the most prevalent limitation of the PR in assessing the relationship between MM3 roots and IAN was the identification of false-positive relationship. CONCLUSIONS: PA radiography may be a good alternative in developing countries to find out if there is a contact between MM3 roots and IAN because it is easier to get to, cheaper, and uses less radiation.
Asunto(s)
Tercer Molar , Diente Impactado , Humanos , Tercer Molar/diagnóstico por imagen , Tercer Molar/cirugía , Proyectos Piloto , Extracción Dental/métodos , Tomografía Computarizada de Haz Cónico/métodos , Nervio Mandibular/diagnóstico por imagen , Radiografía Panorámica/métodos , Diente Impactado/diagnóstico por imagen , Diente Impactado/cirugía , Mandíbula/diagnóstico por imagenRESUMEN
To evaluate the feasibility of temporomandibular disorder (TMD) diagnosis with panoramic radiography, and provide standardized data for artificial intelligence-assisted diagnosis by measuring the differences in the condylar and mandibular ramus heights. A total of 500 panoramic radiographs (219 male and 281 female participants) of healthy individuals were examined. The panoramic machine compatible measurement software, SCANORA 5.2.6, was used to measure the bilateral condylar height and mandibular ramus height, and SPSS 27.0 was used to calculate the left- and right-side differences in condylar height and mandibular ramus height of healthy individuals. Magnetic resonance images of the temporomandibular joint region obtained from 46 outpatients in the Stomatology Department were selected along with their corresponding panoramic radiographs. The left- and right-sided differences were measured and compared with the magnetic resonance imaging results. The measurement data are expressed as meanâ ±â standard deviation (mm). t Tests were used to analyze data from healthy male and healthy female groups. The findings revealed that while there was no significant difference (Pâ >â .05) in the height of the condyle between men and women, there was a significant difference (Pâ <â .05) in the height of the mandibular ramus. In healthy population, the difference in height between the left and right condyle was 1.09â ±â 0.99 mm. The difference in height of mandibular ramus in men was 1.26â ±â 0.85 mm and that in women was 1.19â ±â 0.87 mm. For the diagnosis of TMD, the sensitivity of panoramic radiographs was 94.74% (36/38), specificity was 75.00% (6/8), and diagnostic accuracy was 91.30% (42/46). The height of the right and left lateral condyles was not identical in healthy individuals, resulting in a discernible height discrepancy. In addition, the height of the mandibular ramus varied. By considering the left-right lateral height differences identified in this study along with clinical examination, it is possible to employ this metric as a preliminary screening tool for patients with TMD. Further, the use of panoramic radiographs for initial TMD screening is both viable and significant.