Reliability of the AI-Assisted Assessment of the Proximity of the Root Apices to Mandibular Canal.

Background: This study evaluates the diagnostic accuracy of an AI-assisted tool in assessing the proximity of the mandibular canal (MC) to the root apices (RAs) of mandibular teeth using computed tomography (CT). Methods: This study involved 57 patients aged 18-30 whose CT scans were analyzed by both AI and human experts. The primary aim was to measure the closest distance between the MC and RAs and to assess the AI tool's diagnostic performance. The results indicated significant variability in RA-MC distances, with third molars showing the smallest mean distances and first molars the greatest. Diagnostic accuracy metrics for the AI tool were assessed at three thresholds (0 mm, 0.5 mm, and 1 mm). Results: The AI demonstrated high specificity but generally low diagnostic accuracy, with the highest metrics at the 0.5 mm threshold with 40.91% sensitivity and 97.06% specificity. Conclusions: This study underscores the limited potential of tested AI programs in reducing iatrogenic damage to the inferior alveolar nerve (IAN) during dental procedures. Significant differences in RA-MC distances between evaluated teeth were found.

Accuracy of artificial intelligence-based segmentation of the mandibular canal in CBCT.

OBJECTIVES: To investigate the accuracy of artificial intelligence (AI)-based segmentation of the mandibular canal, compared to the conventional manual tracing, implementing implant planning software. MATERIALS AND METHODS: Localization of the mandibular canals was performed for 104 randomly selected patients. A localization was performed by three experienced clinicians in order to serve as control. Five tracings were performed: One from a clinician with a moderate experience with a manual tracing (I1), followed by the implementation of an automatic refinement (I2), one manual from a dental student (S1), and one from the experienced clinician, followed by an automatic refinement (E). Subsequently, two fully automatic AI-driven segmentations were performed (A1,A2). The accuracy between each method was measured using root mean square error calculation. RESULTS: The discrepancy among the models of the mandibular canals, between the experienced clinicians and each investigated method ranged from 0.21 to 7.65 mm with a mean of 3.5 mm RMS error. The analysis of each separate mandibular canal's section revealed that mean RMS error was higher in the posterior and anterior loop compared to the middle section. Regarding time efficiency, tracing by experienced users required more time compared to AI-driven segmentation. CONCLUSIONS: The experience of the clinician had a significant influence on the accuracy of mandibular canal's localization. An AI-driven segmentation of the mandibular canal constitutes a time-efficient and reliable procedure for pre-operative implant planning. Nevertheless, AI-based segmentation results should always be verified, as a subsequent manual refinement of the initial segmentation may be required to avoid clinical significant errors.

Evaluation of Mental Foramen and Mandibular Canal Course and Location in an Emirati Subpopulation.

INTRODUCTION AND AIMS: Violations of the mandibular canal (MC) and mental foramen (MF) and subsequent injuries to their neurovascular bundle have been reported after surgical and nonsurgical dental procedures. Besides using advanced technologies such as cone-beam computed tomography (CBCT), clinicians should be aware of the anatomy and location of MC and MF in different populations. This study aims to describe the morphologic characteristics of the MF, MC, and its intrabony location in relation to the apices of mandibular posterior teeth in an Emirati subpopulation using CBCT. METHODS: A total of 3700 CBCT scans were screened, and 154 scans that met the inclusion and exclusion criteria were randomly selected. The scans were assessed using 3-dimensional multiplanar imaging for the following structures: the location of MF and the MC course, its intrabony location, and its relationship to the apices of the mandibular posterior teeth. The data were analysed statistically using SPSS software. RESULTS: The MC ran lingually and inferiorly at the posterior region and became more buccal and superior towards the MF. The distal root of the mandibular second molar was found to be the closest root to the MC (2.06 ± 1.83 mm). Moreover, the most common location of the MF was distal to the contact area between the 2 premolars (0.83 ± 1.84 mm) with a significant negative correlation to age (with and increase in age, the MF moves distally). The distance between the root apices and the MC was statistically significantly affected by age (positive correlation) and gender (male patients had a greater distance). CONCLUSIONS: The common course of the MC is lingual and inferior posteriorly and becomes more buccal and superior towards the MF, which is located mostly between the mandibular first and second premolars. Furthermore, the distal root of the mandibular second molar is the closest to the MC and has a positive relationship with age.

Automatic detection of the third molar and mandibular canal on panoramic radiographs based on deep learning.

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.

Bifurcation of the mandibular canal according to sex, age, and facial skeletal pattern: a morphological and morphometric study of cone beam computed tomography.

PURPOSE: To assess the presence of mandibular canal bifurcation (BMC) and classify these variations by correlating findings with sex, age and facial skeletal pattern, measurements were made, including height, width, and distance from bifurcation to anatomical cortical bones. METHODS: BMC was identified in cone beam CT exams of 301 patients and classified according to its origin, location, direction, configuration and ending. The height and width of the MC before and after the bifurcation; height and width of the BMC; and distance from BMC to alveolar (C1), buccal (C2), lingual (C3) and basal (C4) bone cortices were measured. All data were correlated with sex, age, and facial skeletal pattern (class I, II, III). The significance level was 5%. RESULTS: 67 BMC (22.26%) were identified in 55 patients (18.28%). Bifurcations were more prevalent in females (p = 0.57), aged 18-39 years (p = 0.40), class I (p = 0.77). Single bifurcations, located in the posterior region of the mandible, originating in the MC, with a superior direction and ending in the retromolar foramen were more prevalent (p > 0.05). Mean cortical measurements were higher in male individuals, with significant differences only at C1 (p = 0.03). The mean height and width of BMC were 2.24 (± 0.62) and 1.75 (± 0.45) mm. There was no association between BMC classification and the variables studied (p > 0.05). CONCLUSION: Approximately 1/5 of the population studied had BMC. There were no associations of BMC presence or characteristics with sex, age, and facial skeletal pattern. The distance from bifurcation to alveolar (superior) cortical bone is greater in male individuals.

Clinical, Radiological, and Pathological Correlation of Mandibular Invasion in Carcinoma Bucco-alveolar Complex.

A prospective cross-sectional study was conducted to correlate clinically, radiologically, and pathologically the mandibular invasion in carcinoma bucco-alveolar complex. All biopsy-proven oral cavity cancer cases (64 patients) were assessed clinically and radiologically for involvement of the mandible. Preoperative clinicoradiological findings were compared with postoperative histopathological findings. In our study, oral cancer was 4 times more prevalent in males as compared to females and clinical evaluation was found to be highly sensitive in predicting mandibular invasion. Orthopantomogram showed sensitivity of 66.6% and specificity of 100%. CT scan showed sensitivity of 100% and specificity of 46% whereas MRI showed sensitivity of 54.5% and a specificity of 96%. MRI correlates well with final histopathology in predicting size of tumor. Prevalence of bony invasion in carcinoma oral cavity was 18%. We noted an inverse relation with tumor differentiation and mandibular invasion, and none of the verrucous carcinoma lesions showed mandibular invasion. Association of clinical T and N staging with postoperative histopathology was found to be statistically significant. Despite recent advances in molecular biology, radiological techniques, and newer modalities like visual surgical planning, exact measurement of bone invasion is still challenging. At present, CT scan and MRI along with clinical evaluation are widely used to evaluate mandibular invasion in carcinoma oral cavity, and all these are complementary to each other. The recent progress in tissue engineering technologies and stem cell biology has significantly promoted the development of regenerative reconstruction of jawbone defects.

Mandibular canal assessment in dentate and edentulous ridges of 400 Iraqi Arab and Kurdish populations using cone beam computed tomography.

Objectives: This study aims to compare differences in mandibular canal (MC) location between dentate and edentulous ridges, in the second premolar region as well as the first, second, and third molar regions using cone beam computed tomography (CBCT) of Arabic and Kurdish Iraqi populations. Materials and Methods: CBCT images of 400 subjects (200 Arabs, 200 Kurds) were collected from radiological archives. RadiAnt DICOM software (Medixant, Poland) was used for image analyses. Measurements were performed from MC to buccal and lingual alveolar crests and to buccal, lingual, and inferior aspect of the mandible for both dentate and edentulous ridges. Additionally, distance to the most superior aspect of residual edentulous ridge were performed. Independent t-test and Mann-Whitney U Test were performed utilising SPSS v.26. Results: Distances from MC to buccal and lingual alveolar crests were consistently lower in edentulous ridge compared to dentate ridge across all teeth regions. Distances to lingual and inferior border of the mandible were higher in edentulous ridge compared to dentate ridge of all teeth regions. Distances to buccal surface of the mandible varies with fluctuations of dentate and edentulous ridges displaying higher measurements. Distance to superior aspect of residual edentulous ridge revealed mean values in the range of 13.45 to 15.69 mm in Arabs and 13.96 to 16.37 mm in Kurds. Conclusions: Discrepancy in vertical position of MC was observed between dentate and edentulous ridges within Arab and Kurd populations. Horizontal position of MC was unaffected by tooth loss and found to be closer to lingual surface of all molars. The residual alveolar ridge was sufficient to accommodate the common length and width of dental implants. Clinical significance: The findings could play a crucial role in planning surgical interventions of the mandible, helping to prevent complications that might arise due to inadequate preoperative assessments.

Prognostic value of a mandibular canal staging system for primary lesions in patients with lower gingival squamous cell carcinoma: a multicenter, retrospective study.

BACKGROUND: The Union for International Cancer Control and American Joint Committee on Cancer tumor staging system is used globally for treatment planning. As it may be insufficient for tumor staging of lower gingival carcinomas, we proposed the mandibular canal tumor staging system. In this study, we aimed to compare the two systems for such tumor staging and to identify prognostic markers. METHODS: This multicenter, retrospective study included patients with lower gingival squamous cell carcinoma who underwent radical surgery during 2001-2018. We compared survival rates (Kaplan-Meier estimator) and patient stratification according to the two systems. RESULTS: The proposed system yielded more balanced patient stratification than the existing system. Progression in the tumor grade according to the proposed system was associated with a poorer prognosis. The 5-year overall and disease-specific survival rates for the entire cohort were 74.9% and 81.8%, respectively. Independent factors affecting overall survival were tumor stage according to the proposed system, excision margins, and number of positive nodes, whereas those affecting disease-specific survival were excision margins and number of positive nodes. CONCLUSIONS: Subsite-specific tumor classification should be used for patients with oral cancer, and our results suggest that mandibular canal tumor classification may be effective for patients with lower gingival carcinoma.

Mandibular Incisive Canal Morphometry: A Cone-Beam Computed Tomography Study in Jordan

SUMMARY: Mandibular incisive canal (MIC) and related mental foramen (MF) and anterior loop (AL) morphometrics are important landmarks in medical and dental clinical applications. The main aim of this retrospective study to determine the morphometry of the mandibular incisive canal (MIC) in a Jordanian population and to propose a new shape-pattern classification of the MIC. In addition, MF and AL morphometrics were determined. Carestream 3D imaging software was used on 100 Cone-Beam Computed Tomography (CBCT) of a Jordanian population to determine the MF, AL and MIC morphometrics. The detection prevalence of the MIC was 96 %. The right and left MIC showed four distinct line patterns, proposed for the first time in this paper. The line-patterns were angular (L-line), straight (I-line), curved (V-line) and wavy (W-line), with a prevalence of 41 %, 19 %, 25.5 %, and 10.5 %, respectively. MF was detected in all mandibles with a round shape in 58 % of the images. The most common horizontal and vertical positions of the MF were H4 and H3 (73.5 %) and V3 and V2 (95 %), respectively. An accessory MF was detected in 14.5 % of the samples and was more prevalent in males and on the right side. AL was detected in 92.5 % of the samples and exhibited a pattern prevalence of 25.5 %, 40 % and 27 % for types I, II and III, respectively. Results revealed that asymmetry and gender differences between right and left MIC, MF, AL and AMF was seen in patient's mandibles. In conclusion, this is the first study to propose and show that Mandibular incisive canal exhibits four potential line patterns (L, I, V and W lines patterns). Gender and ethnic variations of the mandibular canal landmarks morphometrics of both right and left hemi-mandible are important to be acknowledged in learning anatomy and when planning or performing dental and medical procedures.

Las relaciones de la morfometría del canal incisivo mandibular (MCI), del foramen mentoniano (FM) y del asa anterior (AA) son hitos importantes en las aplicaciones clínicas médicas y dentales. El objetivo principal de este estudio retrospectivo fue determinar la morfometría del MCI en una población jordana y proponer una nueva clasificación de patrón de forma del MCI. Además, se determinaron la morfometría de FM y AA. Se utilizó el software de imágenes 3D Carestream en 100 tomografías computarizadas de haz cónico (CBCT) de una población jordana para determinar la morfometría de FM, MCI y AA. La prevalencia de detección de MCI fue del 96 %. El MCI derecho e izquierdo mostraron cuatro patrones de líneas distintas, propuestas por primera vez en este artículo. Los patrones de líneas fueron angulares (línea L), rectos (línea I), curvos (línea V) y ondulados (línea W), con una prevalencia del 41 %, 19 %, 25,5 % y 10,5 % respectivamente. Se detectó el FM en todas las mandíbulas y con forma redonda en el 58 % de las imágenes. Las posiciones horizontal y vertical más comunes del FM fueron H4 y H3 (73,5 %) y V3 y V2 (95 %), respectivamente. Se detectó FM accesorio en el 14,5 % de las muestras y fue más prevalente en el sexo masculino y en el lado derecho. AA se detectó en el 92,5 % de las muestras y exhibió un patrón de prevalencia del 25,5 %, 40 % y 27 % para los tipos I, II y III, respectivamente. Los resultados revelaron asimetría y diferencias en el sexo entre MCI, FM, AA derecha e izquierda en las mandíbulas de los pacientes. En conclusión, este es el primer estudio que propone y muestra que el canal incisivo mandibular exhibe cuatro patrones de líneas potenciales (patrones de líneas L, I, V y W). Es importante reconocer las variaciones étnicas y de sexo de la morfometría de los puntos de referencia del canal mandibular de la hemimandíbula derecha e izquierda al estudiar y aprender anatomía y al planificar o realizar procedimientos médicos y dentales.

Deep learning-based automatic segmentation of the mandibular canal on panoramic radiographs: A multi-device study.

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion: This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

The alveolar canals and foramina in the first edition of Terminologia Oroanatomica: a preview.

Anatomy was initially developed out of necessity to decrease surgery complications. Over time, anatomists and surgeons have sometimes used different terms for the same anatomical structures, thus resulting in numerous discrepancies in terminology between anatomy and surgery. To avoid any confusion or misunderstanding and to better elucidate the oral anatomy terms, the Federative International Programme for Anatomical Terminology (FIPAT) organized a group of specialists on oral anatomy, Terminologia Oroanatomica (ToA) working group, composed of dentists, anatomy researchers, anatomy educators, oral and maxillofacial surgeons, and oral and maxillofacial radiologists. Within the ToA working group, major anatomical structures in the mandible, such as the mandibular canal, were focused and discussed to determine the most appropriate term, i.e., inferior alveolar canal. Although yet to be approved by the International Federation of Associations of Anatomists (IFAA), this article will preview some changes suggested by the ToA.

The diagnostic performance of impacted third molars in the mandible: A review of deep learning on panoramic radiographs.

Background: Mandibular third molar is prone to impaction, resulting in its inability to erupt into the oral cavity. The radiographic examination is required to support the odontectomy of impacted teeth. The use of computer-aided diagnosis based on deep learning is emerging in the field of medical and dentistry with the advancement of artificial intelligence (AI) technology. This review describes the performance and prospects of deep learning for the detection, classification, and evaluation of third molar-mandibular canal relationships on panoramic radiographs. Methods: This work was conducted using three databases: PubMed, Google Scholar, and Science Direct. Following the literature selection, 49 articles were reviewed, with the 12 main articles discussed in this review. Results: Several models of deep learning are currently used for segmentation and classification of third molar impaction with or without the combination of other techniques. Deep learning has demonstrated significant diagnostic performance in identifying mandibular impacted third molars (ITM) on panoramic radiographs, with an accuracy range of 78.91% to 90.23%. Meanwhile, the accuracy of deep learning in determining the relationship between ITM and the mandibular canal (MC) ranges from 72.32% to 99%. Conclusion: Deep learning-based AI with high performance for the detection, classification, and evaluation of the relationship of ITM to the MC using panoramic radiographs has been developed over the past decade. However, deep learning must be improved using large datasets, and the evaluation of diagnostic performance for deep learning models should be aligned with medical diagnostic test protocols. Future studies involving collaboration among oral radiologists, clinicians, and computer scientists are required to identify appropriate AI development models that are accurate, efficient, and applicable to clinical services.

Effect of metal artefact reduction level on the assessment of dental implant positioning by cone-beam computed tomography.

OBJECTIVES: This study evaluated the effect of metal artefact reduction (MAR) level and tube current on the assessment of dental implant positioning relative to the mandibular canal (MC) through cone-beam computed tomography (CBCT). METHODS: Titanium dental implants were placed in dried mandibles at 0.5-mm superior to the MC (group 1/n = 8) and 0.5-mm inside the MC with perforation of the cortex (group 2/n = 10). CBCT scans were obtained with different levels of MAR (off, medium, and high) and 2 tube currents (4 and 8 mA). Four examiners analysed the images and scored the contact between the implant and the MC using a 5-point scale. Sensitivity, specificity, area under receiver operating characteristic curve (ROC), and frequency of scores were calculated. Data were compared with analysis of variance 2-way and Tukey's test and scores with Chi-square test. RESULTS: Specificity and area under ROC curve decreased significantly when MAR level was high compared with MAR-medium and MAR-off. The frequency of score 3 (inconclusive) was the highest, and scores 1 and 5 (definitely no contact and definitely contact, respectively) were the lowest with MAR-high, regardless of the tube current. When MAR was off, there were higher frequencies of scores 1 and 5. CONCLUSIONS: The level of MAR influences the assessment of the relationship between the dental implant and the MC. MAR-high led to lower diagnostic accuracy compared with MAR-medium and off. ADVANCES IN KNOWLEDGE: This article shows that high level of MAR can interfere in the diagnostic of dental implant positioning relative to the MC, decreasing its accuracy.

Towards clinically applicable automated mandibular canal segmentation on CBCT.

OBJECTIVES: To develop a deep learning-based system for precise, robust, and fully automated segmentation of the mandibular canal on cone beam computed tomography (CBCT) images. METHODS: The system was developed on 536 CBCT scans (training set: 376, validation set: 80, testing set: 80) from one center and validated on an external dataset of 89 CBCT scans from 3 centers. Each scan was annotated using a multi-stage annotation method and refined by oral and maxillofacial radiologists. We proposed a three-step strategy for the mandibular canal segmentation: extraction of the region of interest based on 2D U-Net, global segmentation of the mandibular canal, and segmentation refinement based on 3D U-Net. RESULTS: The system consistently achieved accurate mandibular canal segmentation in the internal set (Dice similarity coefficient [DSC], 0.952; intersection over union [IoU], 0.912; average symmetric surface distance [ASSD], 0.046 mm; 95% Hausdorff distance [HD95], 0.325 mm) and the external set (DSC, 0.960; IoU, 0.924; ASSD, 0.040 mm; HD95, 0.288 mm). CONCLUSIONS: These results demonstrated the potential clinical application of this AI system in facilitating clinical workflows related to mandibular canal localization. CLINICAL SIGNIFICANCE: Accurate delineation of the mandibular canal on CBCT images is critical for implant placement, mandibular third molar extraction, and orthognathic surgery. This AI system enables accurate segmentation across different models, which could contribute to more efficient and precise dental automation systems.

Spatial Position and Anatomical Characteristics Associated with Impacted Third Molars Using a Map-Reading Strategy on Cone-Beam Computed Tomography Scans: A Retrospective Analysis.

(1) Background: This study assessed the spatial position and anatomical features associated with impacted third molars through a map-reading strategy employing cone-beam computed tomography (CBCT). (2) Methods: The positioning of impacted third molars on CBCT was assessed using Winter's and Pell and Gregory's classifications. External root resorption in mandibular second molars was categorized according to Herman's classification. Additionally, the relationship between the mandibular third molar root apex and the mandibular canal was examined. Comparative statistical analysis was conducted using Fisher's exact test, with a significance level considered as 5%. (3) Results: The results indicated that, based on Winter's classification, 48.06 % of impacted teeth were positioned mesioangularly. Employing Pell and Gregory's classification, 43.22% of the impacted molars fell into positions B and C, with 54.2% classified as Class II. A notable 69.7% of teeth exhibited no contact between the root apex and the mandibular canal, and external root resorption in the distal aspect of the second molar was absent in 88.7% of cases. (4) Conclusions: Utilizing the map-reading strategy with CBCT scans to assess the anatomical positions and characteristics of impacted third molars enhances professional confidence and sets a standard for quality and safety in the surgical procedure for patients.

System for automatically assessing the likelihood of inferior alveolar nerve injury.

Inferior alveolar nerve (IAN) injury is a severe complication associated with mandibular third molar (MM3) extraction. Consequently, the likelihood of IAN injury must be assessed before performing such an extraction. However, existing deep learning methods for classifying the likelihood of IAN injury that rely on mask images often suffer from limited accuracy and lack of interpretability. In this paper, we propose an automated system based on panoramic radiographs, featuring a novel segmentation model SS-TransUnet and classification algorithm CD-IAN injury class. Our objective was to enhance the precision of segmentation of MM3 and mandibular canal (MC) and classification accuracy of the likelihood of IAN injury, ultimately reducing the occurrence of IAN injuries and providing a certain degree of interpretable foundation for diagnosis. The proposed segmentation model demonstrated a 0.9 % and 2.6 % enhancement in dice coefficient for MM3 and MC, accompanied by a reduction in 95 % Hausdorff distance, reaching 1.619 and 1.886, respectively. Additionally, our classification algorithm achieved an accuracy of 0.846, surpassing deep learning-based models by 3.8 %, confirming the effectiveness of our system.

Three-dimensional evaluation of mandibular lingula: Comparisons of skeletal angle classifications and growth patterns.

OBJECTIVES: This study aimed to evaluate the position of the mandibular lingula (ML) in adult patients (aged between 18 and 35 years old) with different skeletal and growth patterns using cone-beam computed tomography (CBCT). DESIGN: Cross-sectional. SETTING: Dentistry department of University. SUBJECTS: Subjects comprised CBCT images of 150 adult patients, including 300 rami. METHODS AND MATERIALS: In total, 150 CBCT aged between 18 and 35 were selected and divided into three main groups of 50 samples based on their skeletal relationships (classes I, II and III). Patients were subdivided based on their growth pattern (vertical vs. horizontal), resulting in 25 samples per subgroup. Distances between the mandibular lingula and occlusal plane (ML-OP), sigmoid notch (ML-SN), external oblique ridge (ML-EOR), internal oblique ridge (ML-IOR), posterior border of the ramus (ML-PBR), inferior border of the ramus (ML-IBR), and horizontal and vertical distances to the mandibular foramen (ML-hMF and ML-vMF). One-way ANOVA variance analysis was employed to compare different angle classifications, and Bonferroni analysis was used for multiple comparisons. The Student's t-test was also used to compare growth patterns within each main group and genders within the subgroup. RESULTS: The study revealed statistically significant differences in the position of the mandibular lingula between different angle classifications, growth patterns, and genders. Class II samples showed a more anterior position of the ML, whereas Class III samples displayed a more posterior position of the ML. Patients with horizontal growth patterns and Angle Class III had a more posteriorly positioned ML. Gender differences were observed, particularly in Class I and Class III classifications, suggesting that gender may influence the variability of ML position in these specific classifications. CONCLUSION: The position of the mandibular lingula showed high variability among individuals with different angle classifications, growth patterns and genders.

Topography of the mandibular canal in male human skulls originating from different time periods.

BACKGROUND: Dynamic advances in dentistry, especially in implantology has inspired researchers to carry out many studies investigating the topography of the mandibular canal and its ethnic differences. The aim of the study was a comparative analysis of variations in the position and topography of the mandibular canal based on radiographic images of human mandibles originating from modern and medieval skulls. MATERIALS AND METHODS: Morphometric examination of 126 radiographs of skulls (92 modern and 34 medieval skulls) was included. The age and sex of individuals were determined based on the morphology of the skull, the obliteration of cranial sutures, and the degree of tooth wear. To define the topography of the mandibular canal on X-ray images, we took 8 anthropometric measurements. RESULTS: We observed significant differences in several parameters. The distance between the base of the mandible and the bottom of the mandibular canal, the distance between the top of the mandibular canal and the crest of the alveolar arch, and the height of the mandibular body. Significant asymmetry was found for two parameters of mandibles from modern skulls: the distance between the top of the mandibular canal and the crest of the alveolar arch at the level of the second molar (p < 0.05), and the distance between the mandibular foramen and the margin of the anterior mandibular ramus (p < 0.007). There were no significant differences between measurements taken on the right and left sides of the medieval skulls. CONCLUSIONS: Our study revealed differences in the position of the mandibular canal between modern and medieval skulls, confirming the presence of geographical and chronological differences between populations. Knowledge of variability in the position of the mandibular canal between different local populations is fundamental for the correct interpretation of findings from diagnostic radiological studies used in dental practice and in forensic odontology or analysis of archaeological bone materials.

The inferior alveolar nerve and its relationship to the mandibular canal.

Previous work on the mandibular canal, mental foramen, and mandibular foramen has focused on humans and some other non-primate mammals (with small sample sizes), but little work has investigated the mandibular canal and inferior alveolar nerve (IAN) across primates. However, it is important to understand the relationship between the IAN and mandibular canal due to the IAN's close relationship to the teeth and mastication, and thus dietary adaptations. While it is assumed that most bony canals within the skull grow around and form to pre-existing nervous structures, this relationship has never been validated for the IAN and mandibular canal. MicroCT scans of 273 individuals (131 females, 134 males, and 8 unknown sex) from 68 primate species and three mammalian outgroups, and diceCT scans of 66 individuals (35 females, 23 males, and 8 unknown sex) from 33 primate species and the same mammalian outgroups were used to create 3D models of the IAN and mandibular canal from which cross-sectional areas were taken at various points on the structures. Using qualitative descriptions, phylogenetic generalized least squares analysis, and phylogenetic ANOVAs, we were able to establish three main conclusions: (1) the mandibular canal is most often not a defined canal within the mandible of primates, (2) when the canal can be identified, the IAN does not comprise most of the space within, and (3) there are significant relationships between the IAN and the corresponding canals, with most showing isometry and the mental foramen/nerve showing negative allometry.