Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population.

Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.

A novel method for measuring the direction and angle of Central ray and predicting rotation center via panorama phantom.

OBJECTIVES: The aim of this study is to propose and evaluate a novel method for measuring the central ray direction and detecting the rotation center of panoramic radiography using the panorama phantom. METHODS: To determine the central ray direction, two points passing through the same x-coordinate in a panoramic radiograph were identified and connected. The angles formed by the central ray with the midline and the angle to the arch form were measured using mathematical calculations. Further, by analyzing the continuous changes in the central ray obtained in this manner, the movement of the rotation center was detected and visualized. RESULTS: The angle between the central ray and the midline exhibited a progressive decrease from the anterior to the posterior direction. With regards to the arch form, the angle of the central ray exhibited an increasing pattern as it moved from the anterior to the posterior direction, culminating in its peak value at the lower second premolar cusp region, followed by a consistent decrease. The rotation center approximately started from the distolateral aspect of the coronoid process and then anteromedially moved to the midline in a curved line passing between the mandibular notch and coronoid process. CONCLUSIONS: By using the panorama phantom, we successfully obtained the central ray direction and detected the rotation center of the panoramic radiography.

Automatic classification of 3D positional relationship between mandibular third molar and inferior alveolar canal using a distance-aware network.

The purpose of this study was to automatically classify the three-dimensional (3D) positional relationship between an impacted mandibular third molar (M3) and the inferior alveolar canal (MC) using a distance-aware network in cone-beam CT (CBCT) images. We developed a network consisting of cascaded stages of segmentation and classification for the buccal-lingual relationship between the M3 and the MC. The M3 and the MC were simultaneously segmented using Dense121 U-Net in the segmentation stage, and their buccal-lingual relationship was automatically classified using a 3D distance-aware network with the multichannel inputs of the original CBCT image and the signed distance map (SDM) generated from the segmentation in the classification stage. The Dense121 U-Net achieved the highest average precision of 0.87, 0.96, and 0.94 in the segmentation of the M3, the MC, and both together, respectively. The 3D distance-aware classification network of the Dense121 U-Net with the input of both the CBCT image and the SDM showed the highest performance of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve, each of which had a value of 1.00. The SDM generated from the segmentation mask significantly contributed to increasing the accuracy of the classification network. The proposed distance-aware network demonstrated high accuracy in the automatic classification of the 3D positional relationship between the M3 and the MC by learning anatomical and geometrical information from the CBCT images.

Comparison of 2D, 2.5D, and 3D segmentation networks for maxillary sinuses and lesions in CBCT images.

BACKGROUND: The purpose of this study was to compare the segmentation performances of the 2D, 2.5D, and 3D networks for maxillary sinuses (MSs) and lesions inside the maxillary sinus (MSL) with variations in sizes, shapes, and locations in cone beam CT (CBCT) images under the same constraint of memory capacity. METHODS: The 2D, 2.5D, and 3D networks were compared comprehensively for the segmentation of the MS and MSL in CBCT images under the same constraint of memory capacity. MSLs were obtained by subtracting the prediction of the air region of the maxillary sinus (MSA) from that of the MS. RESULTS: The 2.5D network showed the highest segmentation performances for the MS and MSA compared to the 2D and 3D networks. The performances of the Jaccard coefficient, Dice similarity coefficient, precision, and recall by the 2.5D network of U-net + + reached 0.947, 0.973, 0.974, and 0.971 for the MS, respectively, and 0.787, 0.875, 0.897, and 0.858 for the MSL, respectively. CONCLUSIONS: The 2.5D segmentation network demonstrated superior segmentation performance for various MSLs with an ensemble learning approach of combining the predictions from three orthogonal planes.

Developing evidence-based clinical imaging guidelines of justification for radiographic examination after dental implant installation.

BACKGROUND: This study aimed to develop evidence-based clinical imaging guidelines to assess the proper implant location following implant surgery and identify potential complications during follow-up. METHODS: The guideline development process employed an adaptation methodology in accordance with the Korean clinical imaging guidelines (K-CIG). Core (Ovid-Medline, Ovid-Embase, National Guideline Clearinghouse, and Guideline International Network) and domestic databases (KoreaMed, KMbase, and KoMGI) were searched used to retrieve guidelines, and two reviewers analyzed the retrieved articles. The articles were included in this review using well-established inclusion criteria. RESULTS: Our online search identified 66 articles, of which 3 were selected for the development of the guidelines. Consequently, based on these three guidelines, we formulated distinct recommendations regarding the appropriate imaging modalities that should be used following implant placement. CONCLUSIONS: Conventional imaging (e.g., periapical or panoramic radiography) should be the first choice for assessing the implant following its placement and osseointegration. The metal artifacts in Cone Beam Computed Tomography (CBCT) should be considered. However, CBCT is recommended for patients with sensory abnormalities following dental implant surgery to evaluate and identify the underlying cause of implant complications and to determine the appropriate treatment.

Correlation between spatial resolution and ball distortion rate of panoramic radiography.

BACKGROUND: The purpose of this study was to analyze the correlation between spatial resolution and ball distortion rate of panoramic radiography and to elucidate the minimum criterion for ball distortion rate, which is very relevant to clinical readability. METHODS: Horizontal and vertical spatial resolution and ball distortion rates were calculated in the same position, such as the incisor, premolar, molar, and temporomandibular joint area with various object depths corresponding to 48 mm. Three devices were evaluated. A region showing spatial resolution above the reference standard was selected, and the ball distortion rate corresponding to the same region was divided into horizontal and vertical phantom groups. The mean and standard deviation of the obtained ball distortion rates were calculated. Student's t-test was used to statistically analyze the mean difference in ball distortion rates between vertical and horizontal phantom groups. RESULTS: In all devices, the horizontal line pair phantom, but not the vertical line pair phantom, was readable in all areas measured at the line pair value of at least 1.88 lp/mm. The line pair value tended to be higher toward the center and lower toward the outside. The ball distortion rate tended to decrease closer to the center and increased further away. In addition, ball distortion rates could not be measured at some areas as they were not recognized as balls due to the high degree of distortion at the outermost and innermost sides. The number of balls satisfying the reference value using the horizontal line pair phantom was 102 (mean of ball distortion rates, 20.98; standard deviation, 15.25). The number of balls satisfying the reference value using the vertical line pair phantom was 49 (mean of ball distortion rates, 16.33; standard deviation, 14.25). However, mean ball distortion rate was not significantly different between the two groups. CONCLUSIONS: Image layer of panoramic radiography could be evaluated by the spatial resolution using horizontal and vertical line pair phantoms and by assessing ball distortion rates through a ball-type panorama phantom. A ball distortion rate of 20% could be used as a threshold to evaluate the image layer of panoramic radiography.

Quantitative Augmented Reality-Assisted Free-Hand Orthognathic Surgery Using Electromagnetic Tracking and Skin-Attached Dynamic Reference.

The purpose of this study was to develop a quantitative AR-assisted free-hand orthognathic surgery method using electromagnetic (EM) tracking and skin-attached dynamic reference. The authors proposed a novel, simplified, and convenient workflow for augmented reality (AR)-assisted orthognathic surgery based on optical marker-less tracking, a comfortable display, and a non-invasive, skin-attached dynamic reference frame. The 2 registrations between the physical (EM tracking) and CT image spaces and between the physical and AR camera spaces, essential processes in AR-assisted surgery, were pre-operatively performed using the registration body complex and 3D depth camera. The intraoperative model of the maxillary bone segment (MBS) was superimposed on the real patient image with the simulated goal model on a flat-panel display, and the MBS was freely handled for repositioning with respect to the skin-attached dynamic reference tool (SRT) with quantitative visualization of landmarks of interest using only EM tracking. To evaluate the accuracy of AR-assisted Le Fort I surgery, the MBS of the phantom was simulated and repositioned by 6 translational and three rotational movements. The mean absolute deviations (MADs) between the simulation and post-operative positions of MBS landmarks by the SRT were 0.20, 0.34, 0.29, and 0.55 mm in x- (left lateral, right lateral), y- (setback, advance), and z- (impaction, elongation) directions, and RMS, respectively, while those by the BRT were 0.23, 0.37, 0.30, and 0.60 mm. There were no significant differences between the translation and rotation surgeries or among surgeries in the x-, y-, and z-axes for the SRT. The MADs in the x-, y-, and z-axes exhibited no significant differences between the SRT and BRT. The developed method showed high accuracy and reliability in free-hand orthognathic surgery using EM tracking and skin-attached dynamic reference.

Therapeutic effect of intraductal saline irrigation in chronic obstructive sialadenitis.

BACKGROUND: The aim of the present study was to evaluate the effectiveness of intraductal irrigation using normal saline in chronic obstructive sialadenitis. METHODS: Patients who had one of the following symptoms were recruited: pain, swelling, stiffness, and dry mouth. A total of 58 salivary glands in 33 patients were diagnosed as having sialadenitis using sialography and ultrasonography. The patients were divided into two groups (swelling group and dry mouth group), according to the major complaint. Repeated intraductal irrigation was performed on each gland. Difference of symptom severity evaluated using numerical rating scale (NRS), and ductal width measured using ultrasonography were compared between the two groups. RESULTS: The average NRS score was significantly decreased from 6.0 to 3.3 after 3-5 visits of intraductal irrigation (P < 0.05). The reduction in NRS was greater in the swelling group than in the dry mouth group, although the difference between the groups was not statistically significant. There was no change of ductal width before and after the irrigation. CONCLUSIONS: Intraductal irrigation according to this study method using normal saline is a simple treatment for the patients with chronic obstructive sialadenitis. It provides a conservative treatment option reducing the subjective symptoms.

Can deep learning identify humans by automatically constructing a database with dental panoramic radiographs?

The aim of this study was to propose a novel method to identify individuals by recognizing dentition change, along with human identification process using deep learning. Recent and past images of adults aged 20-49 years with more than two dental panoramic radiographs (DPRs) were assumed as postmortem (PM) and antemortem (AM) images, respectively. The dataset contained 1,029 paired PM-AM DPRs from 2000 to 2020. After constructing a database of AM dentition, the degree of similarity was calculated and sorted in descending order. The matched rank of AM identical to an unknown PM was measured by extracting candidate groups (CGs). The percentage of rank was calculated as the success rate, and similarity scores were compared based on imaging time intervals. The matched AM images were ranked in the CG with success rates of 83.2%, 72.1%, and 59.4% in the imaging time interval for extracting the top 20.0%, 10.0%, and 5.0%, respectively. The success rates depended on sex, and were higher for women than for men: the success rates for the extraction of the top 20.0%, 10.0%, and 5.0% were 97.2%, 81.1%, and 66.5%, respectively, for women and 71.3%, 64.0%, and 52.0%, respectively, for men. The similarity score differed significantly between groups based on the imaging time interval of 17.7 years. This study showed outstanding performance of convolutional neural network using dental panoramic radiographs in effectively reducing the size of AM CG in identifying humans.

Primary malignant melanoma of the parotid gland, a rare case arising from a duct: a case report and literature review.

Malignant melanoma of the parotid gland is an unusual tumor in the head and neck region, and most parotid melanoma is reported as a metastatic lesion of cutaneous malignant melanoma. We report a case of primary malignant melanoma arising in the parotid gland duct with diagnostic challenge. The patient was a 68-year-old man who complained of repeated right facial swelling that presented 3 months prior. Swelling was detected along the Stensen's duct of the cheek, and brown-colored saliva-like fluid was aspirated. On MR and CT images, a fluid-filled duct with small nodule and heterogeneously enhancing mass in the parotid parenchyma was detected. The nodular mass on the ductal wall grew rapidly, and the hyperintense T1 signal became significant on follow-up images. The final diagnosis via histopathologic examination using biopsy and parotidectomy specimen revealed the lesion as malignant melanoma of the duct and pleomorphic adenoma of the parenchyma. Even if the incidence of primary malignant melanoma is very low among tumors occurring in the parotid gland, efforts supporting an early diagnosis using imaging characteristics are important.

CACSNet for automatic robust classification and segmentation of carotid artery calcification on panoramic radiographs using a cascaded deep learning network.

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.

Developing evidence-based clinical imaging guidelines for the diagnosis of vertically fractured teeth.

OBJECTIVES: This study aimed to develop an evidence-based clinical imaging guideline for teeth suspected with vertical root fractures. METHODS: An adaptation methodology based on the Korean Clinical Imaging Guidelines (K-CIG) was used in the guideline development process. After searching for guidelines using major databases such as Ovid-Medline, Elsevier-Embase, National Guideline Clearinghouse, and Guideline International Network, as well as domestic databases such as KoreaMed, KMbase, and KoMGI, two reviewers analyzed the retrieved articles. The retrieved articles were included in this review using well-established inclusion criteria. RESULTS: Twenty articles were identified through an online search, of which three were selected for guideline development. Based on these three guidelines, this study developed specific recommendations concerning the optimal imaging modality for diagnosing teeth suspected of vertical root fractures. CONCLUSIONS: Periapical radiography is the preferred method for assessing teeth with mastication-related pain and suspected vertical root fractures. However, if intraoral radiographs do not provide sufficient information about root fractures, a small FOV CBCT may be considered. However, the use of CBCT in endodontically treated teeth is significantly constrained by the presence of artificial shading.

Underlying bone change in oral squamous cell carcinoma observed from magnetic resonance imaging and computed tomography: Potential implications for tumor aggressiveness and prognosis.

Background: /purposeIn cases where oral squamous cell carcinoma (OSCC) invades the jawbone, clinicians frequently observe abnormal attenuation on computed tomography (CT) and pathologic signal intensity (SI) on magnetic resonance (MR) imaging of the affected underlying bone marrow. This study introduced a concept of "underlying bone change" to examine its association with clinicopathological features and prognosis of OSCC, as well as its correlation with medullary invasion. Materials and methods: We enrolled 93 consecutive patients diagnosed with OSCC, who underwent mandibulectomy between 2010 and 2016. CT and MR images, along with electronic medical records, were reviewed to evaluate correlations between underlying bone changes, clinicopathological features, five-year overall survival, and medullary invasion. Results: Of the 93 patients, 69 (74.2%) exhibited underlying bone sclerosis on CT, and 74 (79.6%) displayed pathological SI on MR images. These underlying bone changes correlated with the T stage and recurrence, but not with overall survival.Medullary invasion, observed in 61 (65.6%) patients, was strongly associated with T and TNM stages and was linked to poorer overall survival.The underlying bone changes on CT and MR images were positively associated with medullary invasion; however, no significant differences were found in the occurrence of underlying bone changes between the subtypes based on the extent of medullary invasion. Conclusion: Underlying bone changes on CT and MR images can provide valuable insights into the aggressiveness of bone invasion by OSCC. Accurate interpretation of these imaging findings might be crucial for correctly delineating surgical margins and preventing the overestimation of tumor extent.

Radiological manifestations and clinical findings of patients with oncologic and osteoporotic medication-related osteonecrosis of the jaw.

Medication-related osteonecrosis of the jaw (MRONJ) poses a challenging form of osteomyelitis in patients undergoing antiresorptive therapies in contrast to conventional osteomyelitis. This study aimed to compare the clinical and radiological features of MRONJ between patients receiving low-dose medications for osteoporosis and those receiving high-dose medications for oncologic purposes. The clinical, panoramic radiographic, and computed tomography data of 159 patients with MRONJ (osteoporotic group, n = 120; oncologic group, n = 39) who developed the condition after using antiresorptive medications for the management of osteoporosis or bone malignancy were analyzed. The osteoporotic group was older (75.8 vs. 60.4 years, p < 0.01) and had a longer duration of medication usage than the oncologic group (58.1 vs. 28.0 months, p < 0.01). Pus discharge and swelling were more common in the osteoporotic group (p < 0.05), whereas bone exposure was more frequent in the oncologic group (p < 0.01). The mandibular cortical index (MCI) in panoramic radiographs was higher in the osteoporotic group (p < 0.01). The mean sequestra size was larger in the oncologic group than in the osteoporotic group (15.3 vs. 10.6 mm, p < 0.05). The cured rate was significantly higher in the osteoporotic group (66.3% vs. 33.3%, p < 0.01). Oncologic MRONJ exhibited distinct clinical findings including rapid disease onset, fewer purulent signs, and lower cure rates than osteoporotic MRONJ. Radiological features such as sequestrum size on CT scan, and MCI values on panoramic radiographs, may aid in differentiating MRONJ in osteoporotic and oncologic patients.

Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network.

OBJECTIVES: This study aimed to develop a robust and accurate deep learning network for detecting the posterior superior alveolar artery (PSAA) in dental cone-beam CT (CBCT) images, focusing on the precise localization of the centre pixel as a critical centreline pixel. METHODS: PSAA locations were manually labelled on dental CBCT data from 150 subjects. The left maxillary sinus images were horizontally flipped. In total, 300 datasets were created. Six different deep learning networks were trained, including 3D U-Net, deeply supervised 3D U-Net (3D U-Net DS), multi-scale deeply supervised 3D U-Net (3D U-Net MSDS), 3D Attention U-Net, 3D V-Net, and 3D Dense U-Net. The performance evaluation involved predicting the centre pixel of the PSAA. This was assessed using mean absolute error (MAE), mean radial error (MRE), and successful detection rate (SDR). RESULTS: The 3D U-Net MSDS achieved the best prediction performance among the tested networks, with an MAE measurement of 0.696 ± 1.552 mm and MRE of 1.101 ± 2.270 mm. In comparison, the 3D U-Net showed the lowest performance. The 3D U-Net MSDS demonstrated a SDR of 95% within a 2 mm MAE. This was a significantly higher result than other networks that achieved a detection rate of over 80%. CONCLUSIONS: This study presents a robust deep learning network for accurate PSAA detection in dental CBCT images, emphasizing precise centre pixel localization. The method achieves high accuracy in locating small vessels, such as the PSAA, and has the potential to enhance detection accuracy and efficiency, thus impacting oral and maxillofacial surgery planning and decision-making.

Clinical and radiological features of malformed mesiodens in the nasopalatine canal: an observational study.

OBJECTIVES: The purpose of this study is to investigate the morphological changes that occur when mesiodens is located within the nasopalatine canal, as well as clinical characteristics. METHODS: Clinical records and CT images of patients who had mesiodens in the nasopalatine canal were retrospectively analysed. In addition to demographic information, clinical symptoms and complications associated with extraction of mesiodens were recorded. Using CT images, number, location, size, and tooth morphology were evaluated. RESULTS: This study included 32 patients and 38 mesiodens within the nasopalatine canal. Supernumerary teeth exhibited a characteristic feature of thin and elongated shape in the canal (narrow width and elongation were observed in 96.6% and 53.3% of the patients, respectively). Fusion was found in 4 patients and dilaceration in 12. A complication occurred in 2 patients, which was tooth remnant, not a neurologic complication. Only 5 mesiodens could be detected in the nasopalatine canal on panoramic images. CONCLUSIONS: Morphological abnormalities in mesiodens within the nasopalatine canal were frequently detected, and these could be effectively diagnosed through 3D imaging analysis.

Automatic segmentation and classification of frontal sinuses for sex determination from CBCT scans using a two-stage anatomy-guided attention network.

Sex determination is essential for identifying unidentified individuals, particularly in forensic contexts. Traditional methods for sex determination involve manual measurements of skeletal features on CBCT scans. However, these manual measurements are labor-intensive, time-consuming, and error-prone. The purpose of this study was to automatically and accurately determine sex on a CBCT scan using a two-stage anatomy-guided attention network (SDetNet). SDetNet consisted of a 2D frontal sinus segmentation network (FSNet) and a 3D anatomy-guided attention network (SDNet). FSNet segmented frontal sinus regions in the CBCT images and extracted regions of interest (ROIs) near them. Then, the ROIs were fed into SDNet to predict sex accurately. To improve sex determination performance, we proposed multi-channel inputs (MSIs) and an anatomy-guided attention module (AGAM), which encouraged SDetNet to learn differences in the anatomical context of the frontal sinus between males and females. SDetNet showed superior sex determination performance in the area under the receiver operating characteristic curve, accuracy, Brier score, and specificity compared with the other 3D CNNs. Moreover, the results of ablation studies showed a notable improvement in sex determination with the embedding of both MSI and AGAM. Consequently, SDetNet demonstrated automatic and accurate sex determination by learning the anatomical context information of the frontal sinus on CBCT scans.

Automatic detection and classification of nasopalatine duct cyst and periapical cyst on panoramic radiographs using deep convolutional neural networks.

OBJECTIVE: The aim of this study was to evaluate a deep convolutional neural network (DCNN) method for the detection and classification of nasopalatine duct cysts (NPDC) and periapical cysts (PAC) on panoramic radiographs. STUDY DESIGN: A total of 1,209 panoramic radiographs with 606 NPDC and 603 PAC were labeled with a bounding box and divided into training, validation, and test sets with an 8:1:1 ratio. The networks used were EfficientDet-D3, Faster R-CNN, YOLO v5, RetinaNet, and SSD. Mean average precision (mAP) was used to assess performance. Sixty images with no lesion in the anterior maxilla were added to the previous test set and were tested on 2 dentists with no training in radiology (GP) and on EfficientDet-D3. The performances were comparatively examined. RESULTS: The mAP for each DCNN was EfficientDet-D3 93.8%, Faster R-CNN 90.8%, YOLO v5 89.5%, RetinaNet 79.4%, and SSD 60.9%. The classification performance of EfficientDet-D3 was higher than that of the GPs' with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 94.4%, 94.4%, 97.2%, 94.6%, and 97.2%, respectively. CONCLUSIONS: The proposed method achieved high performance for the detection and classification of NPDC and PAC compared with the GPs and presented promising prospects for clinical application.

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.

A case report of an unusual temporomandibular joint mass: Nodular fasciitis.

Nodular fasciitis (NF) is a benign myofibroblastic proliferation that grows very rapidly, mimicking a sarcoma on imaging. It is treated by local excision, and recurrence has been reported in only a few cases, even when excised incompletely. The most prevalent diagnoses of temporomandibular joint (TMJ) masses include synovial chondromatosis, pigmented villonodular synovitis, and sarcomas. Cases of NF in the TMJ are extremely rare, and only 3 cases have been reported to date. Due to its destructive features and rarity, NF has often been misdiagnosed as a more aggressive lesion, which could expose patients to unnecessary and invasive treatment approaches beyond repair. This report presents a case of NF in the TMJ, focusing on various imaging features, along with a literature review aiming to determine the hallmark features of NF in the TMJ and highlight the diagnostic challenges.