Perceptions and attitudes of dental students and dentists in South Korea toward artificial intelligence: a subgroup analysis based on professional seniority.

BACKGROUND: This study explored dental students' and dentists' perceptions and attitudes toward artificial intelligence (AI) and analyzed differences according to professional seniority. METHODS: In September to November 2022, online surveys using Google Forms were conducted at 2 dental colleges and on 2 dental websites. The questionnaire consisted of general information (8 or 10 items) and participants' perceptions, confidence, predictions, and perceived future prospects regarding AI (17 items). A multivariate logistic regression analysis was performed on 4 questions representing perceptions and attitudes toward AI to identify highly influential factors according to position, age, sex, residence, and self-reported knowledge level about AI of respondents. Participants were reclassified into 2 subgroups based on students' years in school and 4 subgroups based on dentists' years of experience. The chi-square test or Fisher's exact test was used to determine differences between dental students and dentists and between subgroups for all 17 questions. RESULTS: The study included 120 dental students and 96 dentists. Participants with high level of AI knowledge were more likely to be interested in AI compared to those with moderate or low level (adjusted OR 24.345, p < 0.001). Most dental students (60.8%) and dentists (67.7%) predicted that dental AI would complement human limitations. Dental students responded that they would actively use AI in almost all cases (40.8%), while dentists responded that they would use AI only when necessary (44.8%). Dentists with 11-20 years of experience were the most likely to disagree that AI could outperform skilled dentists (50.0%), and respondents with longer careers had higher response rates regarding the need for AI education in schools. CONCLUSIONS: Knowledge level about AI emerged as the factor influencing perceptions and attitudes toward AI, with both dental students and dentists showing similar views on recognizing the potential of AI as an auxiliary tool. However, students' and dentists' willingness to use AI differed. Although dentists differed in their confidence in the abilities of AI, all dentists recognized the need for education on AI. AI adoption is becoming a reality in dentistry, which requires proper awareness, proper use, and comprehensive AI education.

How well do large language model-based chatbots perform in oral and maxillofacial radiology?

OBJECTIVES: This study evaluated the performance of four large language model (LLM)-based chatbots by comparing their test results with those of dental students on an oral and maxillofacial radiology examination. METHODS: ChatGPT, ChatGPT Plus, Bard, and Bing Chat were tested on 52 questions from regular dental college examinations. These questions were categorized into three educational content areas: basic knowledge, imaging and equipment, and image interpretation. They were also classified as multiple-choice questions (MCQs) and short-answer questions (SAQs). The accuracy rates of the chatbots were compared with the performance of students, and further analysis was conducted based on the educational content and question type. RESULTS: The students' overall accuracy rate was 81.2%, while that of the chatbots varied: 50.0% for ChatGPT, 65.4% for ChatGPT Plus, 50.0% for Bard, and 63.5% for Bing Chat. ChatGPT Plus achieved a higher accuracy rate for basic knowledge than the students (93.8% vs. 78.7%). However, all chatbots performed poorly in image interpretation, with accuracy rates below 35.0%. All chatbots scored less than 60.0% on MCQs, but performed better on SAQs. CONCLUSIONS: The performance of chatbots in oral and maxillofacial radiology was unsatisfactory. Further training using specific, relevant data derived solely from reliable sources is required. Additionally, the validity of these chatbots' responses must be meticulously verified. ADVANCES IN KNOWLEDGE: This study is the first in the field of oral and maxillofacial radiology to assess the knowledge levels of four chatbots. We recommend further training in this domain for all chatbots, given their unsatisfactory performance.

Development and accuracy validation of a fat fraction imaging biomarker for sialadenitis in the parotid gland.

BACKGROUND: The diagnosis of sialadenitis, the most frequent disease of the salivary glands, is challenging when the symptoms are mild. In such cases, biomarkers can be used as definitive diagnostic indicators. Recently, biomarkers have been developed by extracting and analyzing pathological and morphological features from medical imaging. This study aimed to establish a diagnostic reference for sialadenitis based on the quantitative magnetic resonance imaging (MRI) biomarker IDEAL-IQ and assess its accuracy. METHODS: Patients with sialadenitis (n = 46) and control subjects (n = 90) that underwent MRI were selected. Considering that the IDEAL-IQ value is a sensitive fat fractional marker to the body mass index (BMI), all subjects were also categorized as under-, normal-, and overweight. The fat fraction of parotid gland in the control and sialadenitis groups were obtained using IDEAL-IQ map. The values from the subjects in the control and sialadenitis groups were compared in each BMI category. For comparison, t-tests and receiver operating characteristic (ROC) curve analyses were performed. RESULTS: The IDEAL-IQ fat faction of the control and sialadenitis glands were 38.57% and 23.69%, respectively, and the differences were significant. The values were significantly lower in the sialadenitis group (P), regardless of the BMI types. The area under the ROC curve (AUC) was 0.83 (cut-off value: 28.72) in patients with sialadenitis. The AUC for under-, normal-, and overweight individuals were 0.78, 0.81, and 0.92, respectively. CONCLUSIONS: The fat fraction marker based on the IDEAL-IQ method was useful as an objective indicator for diagnosing sialadenitis. This marker would aid less-experienced clinicians in diagnosing sialadenitis.

Canal configuration and root morphology of mandibular premolars using cone-beam computed tomography in a Korean population.

OBJECTIVES: This study aimed to investigate the anatomy of mandibular premolars from two perspectives-the canal configuration and radicular grooves-using cone-beam computed tomography (CBCT) in a large Korean population. MATERIALS AND METHODS: CBCT images of mandibles acquired from March 2018 to December 2019 for dental treatment were randomly selected. In each image, the root canal of premolars was classified into 8 types according to the canal merging or diverging pattern and the number of apical foramens. The presence and the location of radicular grooves were also assessed. Statistical analysis was performed. RESULTS: A total of 1463 first and 1448 s premolars from 732 patients (390 males, 342 females, mean age of 36.1 years) were evaluated. A single canal with one foramen predominated in both first (85.7%) and second (99.5%) premolars, while complete or partial multi-canals accounted for 14.3% and 0.5% of first and second premolars, respectively. The prevalence of radicular grooves was significantly higher in first premolars (13.2%) than in second premolars (0.5%) and in males (4.3%, n = 119) than in females (2.5%, n = 73). CONCLUSIONS: Although most premolars were complete single canals, the first premolars showed a relatively higher number of complex canals compared to the second premolar. In addition, radicular grooves in mandibular premolars were significantly more common in male patients. CLINICAL RELEVANCE: When planning the endodontic treatment of mandibular premolars, clinicians should be aware of their morphologic complexity, especially in the first premolar of male patients.

Anatomical analysis of mandibular posterior teeth for endodontic microsurgery: a cone-beam computed tomographic evaluation.

OBJECTIVE: The purpose of this study was to analyze the anatomical structures relevant for endodontic microsurgery in the mandibular posterior teeth using a cone-beam computed tomography (CBCT). MATERIAL AND METHODS: A total of 963 mandibular posterior teeth were analyzed in CBCT scans from 133 patients. The buccolingual and mesiodistal dimensions of the root and the buccal bone thickness overlying the root were measured at the site of root resection (apical 3 mm). At this location, the relationship between the buccal cortical bone and root was classified into three types (separated, contact, and exposed), and the distance from the root apex to the mandibular canal was measured. RESULTS: The thickest buccolingual dimension of the roots was found in the mesial roots of first molars, at 5.59 ± 0.97 mm. The buccal bone thickness overlying the root became thicker in posterior tooth locations. In the first premolar and first molar mesial root, contact was the most common type of relationship between the buccal cortical bone and root. As the position of the teeth became more posterior, the distance from the apex to the mandibular canal became shorter. CONCLUSIONS: As the position of the teeth became more posterior, the buccal bone thickness increased and the distance to the mandibular canal became closer; therefore, particular attention is required for posterior teeth. The first premolar and the first molar mesial root are often in contact with the buccal cortical bone, which may allow infections to spread to the buccal structure more easily and negatively affect for post-surgical healing. CLINICAL RELEVANCE: When planning and performing endodontic microsurgery, understanding the anatomical structure of the surgical site will help minimize tissue damage and reduce complications.

Microarchitectural changes in the mandibles of ovariectomized rats: a systematic review and meta-analysis.

BACKGROUND: This study aimed to examine radiologic microarchitectural changes in the mandibles of ovariectomized (OVX) rats through a systematic review and meta-analysis and to identify factors of the OVX rat model that influence on the bone microstructure. METHODS: Eligible articles were identified by searching electronic databases, including Embase, Medline, Web of Science, and KoreaMed, for articles published from January 1966 to November 2017. Two reviewers independently performed study selection, data extraction, and quality assessment. The pooled standardized mean difference (SMD) with 95% confidence intervals was calculated using a random-effects model. Subgroup analysis and meta-regression were performed to explore the effect of potential sources on the outcomes. The reliability of the results was assessed by sensitivity analysis and publication bias. RESULTS: Of 1160 studies, 16 studies (120 OVX and 120 control rats) were included in the meta-analysis. Compared to the control group, the OVX rats' trabecular bone volume fraction (SMD = - 2.41, P < 0.01, I2 = 81%), trabecular thickness (SMD = - 1.73, P < 0.01, I2 = 73%) and bone mineral density (SMD = - 0.95, P = 0.01, I2 = 71%) displayed the bone loss consistent with osteoporosis. The trabecular separation (SMD = 1.66, P < 0.01, I2 = 51%) has widen in the OVX mandibular bone in comparison to the control group. However, the trabecular number showed no indication to detect the osteoporosis (SMD = - 0.45, P = 0.38, I2 = 76%). The meta-regression indicated that longer post-OVX periods led to greater changes in bone mineral density (ß = - 0.104, P = 0.017). However, the rats' age at OVX was not linked to bone microstructure change. CONCLUSIONS: Using meta-regression and sensitivity analysis techniques, heterogeneity across the micro CT studies of OVX-induced osteoporosis was found. The major factors of heterogeneity were the region of interest and post-OVX period. Our assessment can assist in designing experiments to maximize the usefulness of OVX rat model.

Accessory mental foramina associated with neurovascular bundle in Korean population.

PURPOSE: To determine the prevalence and anatomical features of accessory mental foramina (AMFs) associated with neurovascular bundle using reformatted CBCT images in Korean population. MATERIALS AND METHODS: Three-dimensional, cross-sectional, and axial images of CBCT from total 446 patients were evaluated. To include only AMF associated with neurovascular bundle, the course of accessory nerve through AMF was evaluated on three-dimensional images. The prevalence, locations to mental foramen (MF), and distribution to the adjacent tooth of AMF were analyzed. Additionally, the distance from MF and dimension of AMF were measured. RESULTS: AMFs were found in 36 (8.1 %) patients in the 446 Koreans. The prevalence rate of AMFs in male was significant higher than that in female (p < 0.05). Most AMFs were located anterior-superior to MF. The shortest distance between the center of AMF and that of MF was 5.80 mm (SD ± 2.90, 1.40-13.0). Variance of location of AMF in relation to MF was greater in the horizontal direction than in the vertical direction. In related to adjacent tooth, AMF was mainly distributed between the first and second premolars. The horizontal dimension and vertical dimension of AMF were 1.54 mm (SD ± 1.41, 0.70-9.80) and 1.44 mm (SD ± 0.41, 0.70-2.30), respectively. CONCLUSIONS: Knowledge of AMF is important for performing effective nerve block and avoiding injuries to neurovascular bundles passing through AMF. Evaluation of the course of AMF in three-dimensional image of CBCT is effective for confirming AMF associated with inferior alveolar nerve.

Cone beam CT findings of retromolar canals in a Korean population.

PURPOSE: The identification of the variation involving the mandibular canal is important during surgical procedures in the mandible. This study aims to investigate the incidence, course, width, and location of retromolar canals in a Korean population using cone beam CT. METHODS: The cross-sectional, sagittal, and three dimensional images from volumetric cone beam CT data of 446 patients were reconstructed using imaging analysis software for the presence of a retromolar canal. Retromolar canals were classified into three types according to the courses. The width and location (distance from the second molar) of retromolar canals were evaluated. RESULTS: A retromolar canal was observed in 8.5 % of patients (38/446). Most retromolar canals had vertically curved courses (Type 1, 66.7 %), followed by horizontally curved courses (Type 2, 20 %). Type 3 retromolar canals, which run independently from separate foramina in the mandibular ramus, were rare (13.3 %). The mean width of a retromolar canal was 1.13 mm (SD ± 0.38, 0.60-2.00), and the mean distance to the second molar was 14.08 mm (SD ± 3.85, 8.50-24.00). CONCLUSIONS: The incidence of retromolar canals in the Korean population was lower than that reported in the investigated ethnic groups. In the presence of a retromolar canal, local anesthesia may be ineffective. The damage to a retromolar canal may be unavoidable during surgical procedures involving the mandible and may result in paresthesia, excessive bleeding, postoperative hematoma, or traumatic neuroma.

Double mandibular foramen leading to the accessory canal on the mandibular ramus.

PURPOSE: The knowledge of the variation in the mandibular foramen and canal is clinically significant in surgical procedures of the mandible. This study aims to evaluate the anatomical characteristics of double mandibular foramen leading to the accessory canal on the mandibular ramus using cone beam CT. METHODS: The sagittal, cross-sectional, and three-dimensional images of cone beam CT data from 446 patients were evaluated in the presence of double mandibular foramen and the accessory canal passing through the foramen. The accessory canals were classified into two types according to the configuration (forward and retromolar type), and the location of double mandibular foramen was recorded. RESULTS: The eight double mandibular foramina leading to the accessory canals were observed in six patients out of 446 patients (1.35 % of population). Regarding the configuration of the accessory canal, there were two forward types and six retromolar types. All double mandibular foramina were located above the mandibular foramina on the medial aspect of the mandibular ramus. CONCLUSION: Three-dimensional images of cone beam CT data are useful in confirming the presence of double mandibular foramen leading to the accessory canal. The variation may cause failure in the routine mandibular nerve block anesthesia and it is often vulnerable during surgical procedures involving the mandibular ramus. Also, double mandibular foramen is considered as an easy route for tumor cell to spread following the radiotherapy. Therefore, the variation should be carefully investigated using reconstructed cone beam CT images in planning of dental surgery or radiotherapy in the mandible.

CT and MR imaging findings of head and neck chondrosarcoma.

OBJECTIVES: This study investigated the imaging features of head and neck chondrosarcoma (HNCS) according to its origin and pathologic subtype. METHODS: Patients who were pathologically diagnosed with HNCS between January 2000 and April 2022 were retrospectively reviewed. Lesions were classified based on their origin and pathologic subtype. The size and margin were evaluated on the image. Internal calcification and the effects on adjacent bone were assessed using computed tomography (CT) images, while signal intensity and contrast enhancement patterns were analyzed using magnetic resonance (MR) imaging. RESULTS: Thirteen HNCSs were included in this study: 8 bone tumors (61.5%) and 5 soft tissue tumors (38.5%). The bone tumors were pathologically diagnosed as conventional (n = 5) and mesenchymal type (n = 3). Soft tissue tumors were defined as myxoid type. The main symptoms were swelling (90.9%) and pain (72.7%). The lesions measured 4.5 cm on average. The margins showed benign and well-defined except for the mesenchymal type. On CT, most bone tumors (75%) showed internal calcification with remodeling or destruction of the adjacent bone. No soft tissue tumors, except one case, showed internal calcification or destruction of the adjacent bone. MR imaging features were non-specific (T2 high signal intensity and contrast enhancement). CONCLUSIONS: HCNS showed various imaging findings according to their origin and pathologic subtype. HNCS should be differentiated if a bone tumor shows internal calcification and affects the adjacent bone. When diagnosing slow-growing soft tissue tumors, even if low possibility, HNCS should be considered.

A rare case report of ameloblastic fibrodentinoma with imaging features in a pediatric patient.

Ameloblastic fibrodentinoma (AFD) is a rare benign odontogenic tumor that resembles an ameloblastic fibroma with dysplastic dentin. This report presents a rare case of mandibular AFD with imaging features in a young patient. Panoramic radiography and computed tomography revealed a well-defined lesion with internal septa and calcified foci, causing inferior displacement of the adjacent molars as well as buccolingual cortical thinning and expansion of the posterior mandible. The lesion was surgically removed via mass excision, and the involved tooth was extracted under general anesthesia. During the 5-year follow-up period, no evidence of recurrence was observed. Radiologic features of AFD typically reveal a moderately to well-defined mixed lesion with varying degrees of radiopacity, reflecting the extent of dentin formation. Radiologists should consider AFD in the differential diagnosis when encountering a multilocular lesion with little dense radiopacity, particularly if it is associated with delayed eruption, impaction, or absence of involved teeth, on radiographic images of young patients.

Attention-guided jaw bone lesion diagnosis in panoramic radiography using minimal labeling effort.

Developing a deep-learning-based diagnostic model demands extensive labor for medical image labeling. Attempts to reduce the labor often lead to incomplete or inaccurate labeling, limiting the diagnostic performance of models. This paper (i) constructs an attention-guiding framework that enhances the diagnostic performance of jaw bone pathology by utilizing attention information with partially labeled data; (ii) introduces an additional loss to minimize the discrepancy between network attention and its label; (iii) introduces a trapezoid augmentation method to maximize the utility of minimally labeled data. The dataset includes 716 panoramic radiograph data for jaw bone lesions and normal cases collected and labeled by two radiologists from January 2019 to February 2021. Experiments show that guiding network attention with even 5% of attention-labeled data can enhance the diagnostic accuracy for pathology from 92.41 to 96.57%. Furthermore, ablation studies reveal that the proposed augmentation methods outperform prior preprocessing and augmentation combinations, achieving an accuracy of 99.17%. The results affirm the capability of the proposed framework in fine-grained diagnosis using minimally labeled data, offering a practical solution to the challenges of medical image analysis.

Evaluation of masticatory muscles in temporomandibular joint disorder patients using quantitative MRI fat fraction analysis-Could it be a biomarker?

Temporomandibular joint disorders (TMDs) are closely related to the masticatory muscles, but objective and quantitative methods to evaluate muscle are lacking. IDEAL-IQ, a type of chemical shift-encoded magnetic resonance imaging (CSE-MRI), can quantify the fat fraction (FF). The purpose of this study was to develop an MR IDEAL-IQ-based method for quantitative muscle diagnosis in TMD patients. A total of 65 patients who underwent 3 T MRI scans, including CSE-MRI sequences, were retrospectively included. MRI diagnoses and clinical data were reviewed. There were 19 patients in the normal group and 46 patients in the TMD group with unilateral disc displacement. The TMD group was subdivided into those with and without clenching. The right and left FF values of the masseter, medial, and lateral pterygoid muscles were measured twice by two oral radiologists on CSE-MRI, and the average value was used. FF measurements using CSE-MRI showed excellent intra- and inter-observer agreement (ICC > 0.889 for both). There were no statistically significant differences between the right and left FF values in the masseter, medial pterygoid, and lateral pterygoid of the normal group (p > 0.05). A statistically significant difference was found in the TMD group without clenching, in which the masseter muscle had a statistically significantly lower FF value on the disc displacement side (3.94 ± 1.61) than on the normal side (4.52 ± 2.24) (p < 0.05). CSE-MRI, which can reproducibly quantify muscle FF values, is expected to be a biomarker for objective muscle evaluation in TMD patients. The masseter muscle is expected to be particularly useful compared to other masticatory muscles, but further research is needed.

Automatic diagnosis of true proximity between the mandibular canal and the third molar on panoramic radiographs using deep learning.

Evaluating the mandibular canal proximity is crucial for planning mandibular third molar extractions. Panoramic radiography is commonly used for radiological examinations before third molar extraction but has limitations in assessing the true contact relationship between the third molars and the mandibular canal. Therefore, the true relationship between the mandibular canal and molars can be determined only through additional cone-beam computed tomography (CBCT) imaging. In this study, we aimed to develop an automatic diagnosis method based on a deep learning model that can determine the true proximity between the mandibular canal and third molars using only panoramic radiographs. A total of 901 third molars shown on panoramic radiographs were examined with CBCT imaging to ascertain whether true proximity existed between the mandibular canal and the third molar by two radiologists (450 molars: true contact, 451 molars: true non-contact). Three deep learning models (RetinaNet, YOLOv3, and EfficientDet) were developed, with performance metrics of accuracy, sensitivity, and specificity. EfficientDet showed the highest performance, with an accuracy of 78.65%, sensitivity of 82.02%, and specificity of 75.28%. The proposed deep learning method can be helpful when clinicians must evaluate the proximity of the mandibular canal and a third molar using only panoramic radiographs without CBCT.

Korean dental hygiene students' perceptions and attitudes toward artificial intelligence: An online survey.

OBJECTIVES: This study investigated Korean dental hygiene students' perceptions and attitudes toward artificial intelligence (AI) and aimed to identify needs for education to strengthen professional competencies. METHODS: A 24-question online survey was conducted to the dental hygiene students from four Korean schools in 2021. The questionnaire included seven questions on basic characteristics and 17 AI-related questions on the student's attitudes toward AI, the confidence in AI, predictions about AI, and its future prospects. Responses were analyzed according to the frequencies and correlations between the participants' subjective level of knowledge about AI and questions using chi-square test. RESULTS: Invitations were sent out to 1310 students and 800 (61.1%) participated. Note that 44.2% of participants were interested in AI, and 93.1% accessed AI-related information through the internet. Participants expressed lower confidence in AI's diagnosis (14.8%) and judgment (8.1%) than in those of humans, and 21.9% believed AI would replace their job. The proportions of participants with positive perceptions of the usefulness and the potential for improvement of AI in dentistry were 65.5% and 55.4%, respectively. Participants from schools who had existing AI knowledge expressed higher demands for AI-related content as compared to those who did not (p < 0.05). CONCLUSION: Although dental hygiene students expressed low level of confidence in AI, they were interested in AI and had positive views of its application and potential for improvement. However, the fact they had little AI-related information from dental hygiene curriculum strongly suggests the need for AI-related lectures in schools to prepare for the future.

Automatic diagnosis of retention pseudocyst in the maxillary sinus on panoramic radiographs using a convolutional neural network algorithm.

The evaluation of the maxillary sinus is very important in dental practice such as tooth extraction and implantation because of its proximity to the teeth, but it is not easy to evaluate because of the overlapping structures such as the maxilla and the zygoma on panoramic radiographs. When doom-shaped retention pseudocysts are observed in sinus on panoramic radiographs, they are often misdiagnosed as cysts or tumors, and additional computed tomography is performed, resulting in unnecessary radiation exposure and cost. The purpose of this study was to develop a deep learning model that automatically classifies retention pseudocysts in the maxillary sinuses on panoramic radiographs. A total of 426 maxillary sinuses from panoramic radiographs of 213 patients were included in this study. These maxillary sinuses included 86 sinuses with retention pseudocysts, 261 healthy sinuses, and 79 sinuses with cysts or tumors. An EfficientDet model first introduced by Tan for detecting and classifying the maxillary sinuses was developed. The developed model was trained for 200 times on the training and validation datasets (342 sinuses), and the model performance was evaluated in terms of accuracy, sensitivity, and specificity on the test dataset (21 retention pseudocysts, 43 healthy sinuses, and 20 cysts or tumors). The accuracy of the model for classifying retention pseudocysts was 81%, and the model also showed higher accuracy for classifying healthy sinuses and cysts or tumors (98% and 90%, respectively). One of the 21 retention pseudocysts in the test dataset was misdiagnosed as a cyst or tumor. The proposed model for automatically classifying retention pseudocysts in the maxillary sinuses on panoramic radiographs showed excellent diagnostic performance. This model could help clinicians automatically diagnose the maxillary sinuses on panoramic radiographs.

Comparison of the clinical usefulness of structured and free-text reports for interpretation of jaw lesions on cone beam computed tomography images.

OBJECTIVE: This study compared the clinical usefulness of structured reports (SRs) and free-text reports (FTRs) of lesions depicted on cone beam computed tomography (CBCT) images from the perspectives of report providers and receivers. STUDY DESIGN: In total, 36 CBCT images of jaw lesions obtained between February 2020 and August 2020 were evaluated. A working group of 3 oral and maxillofacial radiologists (OMRs) established a reporting system and prepared reports. Evaluation group I (2 OMRs) wrote SRs and FTRs for each case and assessed the reporting process for the criteria of convenience and organization. Evaluation group II (3 general practitioners [GPs] and 3 oral and maxillofacial surgeons [OMSs]) assessed the reports for the criteria of productivity, consistency, and organization. A 5-point Likert scale was used to assess the usefulness of each report. Scores were statistically compared according to report type with the paired Wilcoxon signed-rank test. RESULTS: The SRs scored significantly higher for all criteria as assessed by evaluation group I and the GPs of group II (P < .001). The FTRs scored significantly higher for productivity and organization as assessed by the OMSs of group II (P = .005 for both criteria). CONCLUSIONS: The clinical usefulness of reports may differ according to roles of the report recipients in diagnosis and treatment.

Harmonization of robust radiomic features in the submandibular gland using multi-ultrasound systems: a preliminary study.

OBJECTIVE: This study aimed to identify robust radiomic features in multiultrasonography of the submandibular gland and normalize the interdevice discrepancies by applying a machine-learning-based harmonization method. METHODS: Ultrasonographic images of normal submandibular gland of young healthy adults, aged between 20 and 40 years, were selected from two different devices. In a total of 30 images, the region of interest was determined along the border of gland parenchyma, and 103 radiomic features were extracted using A-VIEW. The coefficient of variation (CV) was obtained for individual features, and the features showing CV less than 10% were selected. For the selected features, the interdevice discrepancy was normalized using machine-learning method, called the ComBat harmonization. Median differences of the features between the two scanners, before and after harmonization, were compared using Mann-Whitney U-test; confidence interval of 95%. RESULTS: Among total 103 radiomic features, 17 features were selected as robust, showing CV less than 10% in both scanners. All values of selected features, except two, showed a statistical difference between the two devices. After applying the ComBat harmonization method, the median and distribution of the 16 features were harmonized to show no significant difference between the two scanners (p > 0.05). One feature remained different (p ≤ 0.05). CONCLUSION: On ultrasonographic examination, robust radiomic features for normal submandibular gland were obtained and interdevice normalization was efficiently conducted using ComBat harmonization. Our findings would be useful for multidevices or multicenter studies based on clinical ultrasonographic imaging data to improve the accuracy of the overall diagnostic model.

Development of deep learning model and evaluation in real clinical practice of lingual mandibular bone depression (Stafne cyst) on panoramic radiographs.

OBJECTIVES: Lingual mandibular bone depression (LMBD) is a developmental bony defect in the lingual aspect of the mandible that does not require any surgical treatment. It is sometimes confused with a cyst or another radiolucent pathologic lesion on panoramic radiography. Thus, it is important to differentiate LMBD from true pathological radiolucent lesions requiring treatment. This study aimed to develop a deep learning model for the fully automatic differential diagnosis of LMBD from true pathological radiolucent cysts or tumors on panoramic radiographs without a manual process and evaluate the model's performance using a test dataset that reflected real clinical practice. METHODS: A deep learning model using the EfficientDet algorithm was developed with training and validation data sets (443 images) consisting of 83 LMBD patients and 360 patients with true pathological radiolucent lesions. The test data set (1500 images) consisted of 8 LMBD patients, 53 patients with pathological radiolucent lesions, and 1439 healthy patients based on the clinical prevalence of these conditions in order to simulate real-world conditions, and the model was evaluated in terms of accuracy, sensitivity, and specificity using this test data set. RESULTS: The model's accuracy, sensitivity, and specificity were more than 99.8%, and only 10 out of 1500 test images were erroneously predicted. CONCLUSION: Excellent performance was found for the proposed model, in which the number of patients in each group was composed to reflect the prevalence in real-world clinical practice. The model can help dental clinicians make accurate diagnoses and avoid unnecessary examinations in real clinical settings.

Impact of physiological parameters on the parotid gland fat fraction in a normal population.

Quantifying physiological fat tissue in the organs is important to further assess the organ's pathologic status. This study aimed to investigate the impact of body mass index (BMI), age, and sex on the fat fraction of normal parotid glands. Patients undergoing magnetic resonance imaging (MRI) of iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL-IQ) due to non-salivary gland-related disease were reviewed. Clinical information of individual patients was categorized into groups based on BMI (under/normal/overweight), age (age I/age II/age III), and sex (female/male) and an inter-group comparison of the fat fraction values of both parotid glands was conducted. Overall, in the 626 parotid glands analyzed, the fat fraction of the gland was 35.80%. The mean fat fraction value increased with BMI (30.23%, 35.74%, and 46.61% in the underweight, normal and overweight groups, respectively [p < 0.01]) and age (32.42%, 36.20%, and 41.94% in the age I, II, and III groups, respectively [p < 0.01]). The fat content of normal parotid glands varies significantly depending on the body mass and age regardless of sex. Therefore, the patient's age and body mass should be considered when evaluating fatty change in the parotid glands in imaging results.