Dento maxillo facial radiology最新文献

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.

Objectives: To elucidate the relationships between the maximum standardized uptake value (SUVmax) of alveolar bone and those of lymph nodes (LNs) around the neck on 18F-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography (PET).

Methods: The SUVmax values of alveolar bone and of level IA, level IB, and level IIA LNs of 174 patients, including those with and without active odontogenic inflammation, on PET/CT performed for a health check were retrospectively evaluated. The upper and lower jaws were divided into four blocks (right maxilla, left maxilla, right mandible, and left mandible). The SUVmax values of each block and of the LNs were calculated. The differences in the SUVmax of each LN level between patients with and without odontogenic inflammation, and the relationship between the SUVmax values of alveolar bone and of the LNs were analysed statistically.

Results: Significant differences in SUVmax values of bilateral level IB and IIA LNs were found between patients with and without odontogenic inflammation (Mann-Whitney U test: right level IB, P = .008; left level IB, P = .006; right level IIA, P < .001; left level IIA, P = .002), but not in bilateral level IA LNs (Mann-Whitney U test: right level IA, P = .432; left level IA, P = .549). The inflammatory site with the highest SUVmax in level IB LNs was the ipsilateral mandible (multivariate analysis: right, beta = 0.398, P < .001; left, beta = 0.472, P < .001), and the highest SUVmax in level IIA LNs was the ipsilateral maxilla (multivariate analysis: right, beta = 0.223, P = .002; left, beta = 0.391, P < .001).

Conclusions: The SUVmax values of level IB and IIA LNs were associated with a tendency towards a higher SUVmax value of alveolar bone on 18F-FDG-PET.

Objective: This study aims to develop a facial vascular enhancement imaging system and analyze vascular distribution in the facial region to assess its potential in preventing unintended intravascular injections during cosmetic facial filling procedures.

Methods: A facial vascular enhancement imaging system based on optical detection technology was designed, and volunteers were recruited. The system was utilized to detect and analyze vascular distribution in various anatomical regions of the faces. The vascular visualization-enhanced (VVE) images generated by the system were compared with visible light images to validate the vascular visualization capability of the system. Additionally, the reliability of vascular visualization was assessed by comparing the observed vascular patterns in the VVE images with those in near-infrared light images.

Results: Thirty volunteers were recruited. The VVE images produced by the system demonstrated a significant capacity to identify vascular morphology and yielded a higher vessel count compared to visible light images, particularly in the frontal, orbital, perioral, mental, temporal, cheek, and parotid masseter regions (P < .05). The temporal region exhibited the highest vascular density, followed by the cheek region and then the frontal region. Reliability analysis of vascular visualization enhancement indicated that the system's imaging of facial vasculature not only demonstrated reliability but also enhanced physicians' visual perception.

Conclusion: Blood vessel distribution varies across facial regions. The facial vascular enhancement imaging system facilitates real-time and clear visualization of facial vasculature, offering immediate visual feedback to surgeons. This innovation holds promise for enhancing the safety and effectiveness of facial filling procedures.

Objectives: To assess the performance of deep learning (DL) in the detection, classification, and segmentation of maxillary sinus diseases.

Methods: An electronic search was conducted by two reviewers on databases including PubMed, Scopus, Cochrane, and IEEE. All English papers published no later than February 7, 2024, were evaluated. Studies related to DL for diagnosing maxillary sinus diseases were also searched in journals manually.

Results: Fourteen of 1167 studies were eligible according to the inclusion criteria. All studies trained DL models based on radiographic images. Six studies applied to detection tasks, one focused on classification, two segmented lesions, and five studies made a combination of two types of DL models. The accuracy of the DL algorithms ranged from 75.7% to 99.7%, and the area under curves (AUC) varied between 0.7 and 0.997.

Conclusion: DL can accurately deal with the tasks of diagnosing maxillary sinus diseases. Students, residents, and dentists could be assisted by DL algorithms to diagnose and make rational decisions on implant treatment related to maxillary sinuses.

Objectives: To evaluate magnetic susceptibility artefacts produced by orthodontic wires on MRI and the influence of wire properties and MRI image sequences on the magnitude of the artefact.

Methods: Arch form orthodontic wires [four stainless steels (SS), one cobalt chromium (CC) alloy, 13 titanium (Ti) alloys] were embedded in a polyester phantom, and scanned using a 1.5-T superconducting magnet scanner with an eight-channel phased-array coil. All wires were scanned with T1-weighted spin echo (SE) and gradient echo (GRE) sequences according to the American Society for Testing and Materials (ASTM) F2119-07 standard. The phantom also scanned other eight sequences. Artefacts were measured using the ASTM F2119-07 definition and OsiriX software. Artefact volume was analysed according to metal composition, wire length, number of wires, wire thickness, and imaging sequence as factors.

Results: With SE/GRE, black/white artefacts volumes from all SS wires were significantly larger than those produced by CC and Ti wires (P < .01). With the GRE, the black artefacts volume was the highest with the SS wires. With the SE, the black artefacts volume was small, whereas white artefacts were noticeable. The cranio-caudal extent of the artefacts was significantly longer with SS wires (P < .01). Although a direct relationship of wire length, number of wires, and wire thickness with artefact volume was noted, these factors did not influence artefact extension in the cranio-caudal direction.

Conclusions: Ferromagnetic/paramagnetic orthodontic wires create artefacts due to local alteration of magnetic field homogeneity. The SS-type wires produced the largest artefacts followed by CC and Ti.

Objectives: The aim of this study was to identify cone-beam computed tomography (CBCT) protocols that offer an optimal balance between effective dose (ED) and 3D model for orthognathic virtual surgery planning, using CT as a reference, and to assess whether such protocols can be defined based on technical image quality metrics.

Methods: Eleven CBCT (VISO G7, Planmeca Oy, Helsinki, Finland) scan protocols were selected out of 32 candidate protocols, based on ED and technical image quality measurements. Next, an anthropomorphic RANDO SK150 phantom was scanned using these 11 CBCT protocols and 2 CT scanners for bone quantity assessments. The resulting DICOM (Digital Imaging and Communications in Medicine) files were converted into Standard Tessellation Language (STL) models that were used for bone volume and area measurements in the predefined orbital region to assess the validity of each CBCT protocol for virtual surgical planning.

Results: The highest CBCT bone volume and area of the STL models were obtained using normal dose protocol (F2) and ultra-low dose protocol (J13), which resulted in 48% and 96% of the mean STL bone volume and 48% and 95% of the bone area measured on CT scanners, respectively.

Conclusions: The normal dose CBCT protocol "F2" offered optimal bone area and volume balance for STL. The optimal CBCT protocol can be defined using contrast-to-noise ratio and modulation transfer function values that were similar to those of the reference CT scanners'. CBCT scanners with selected protocols can offer a viable alternative to CT scanners for acquiring STL models for virtual surgical planning at a lower effective dose.

Objectives: To assess the influence of two conventional and one adapted cheek and lip retractors and three emissivity setting values on intraoral infrared thermography (IT) temperature values.

Methods: The sample was composed by 50 volunteers. Three cheek and lip retractors were tested: Group 1-flex retractor (FR); Group 2-FR adapted with Styrofoam; Group 3-U-type retractor (UR) for cheek and lip. All thermograms were acquired using FLIR T650 infrared camera. A set of three thermograms in frontal norm were acquired for each lip and cheek retractor at 0.91, 0.96, and 0.98ε, with an interval of 15 min between each set of images to avoid thermal interference. All images were assessed by two observers. The ROIs' mean temperature of the four upper incisors was recorded. Two-way ANOVA and Sidak post-test were used for data assessment with a significance level of 5%.

Results: Group 3 showed higher mean temperature than Groups 1 and 2 at all emissivity settings for all assessed teeth (P < .05). 0.91ε showed higher temperature than 0.96ε and 0.98ε for all assessed variables (P < .01). Contralateral teeth assessed using Group 3 at 0.91ε showed statistical differences between each other (P < .05). No statistical difference was observed between contralateral teeth assessed using Groups 1 and 2 at 0.96ε and 0.98ε (P > .05).

Conclusions: The choice of cheek and lip retractor and emissivity setting can interfere on intraoral IT temperature values. U-type cheek and lip retractor and 0.91ε setting should not be used for IT image acquisition when assessing dental tissues.

Objectives: Currently, there is no reliable automated measurement method to study the changes in the condylar process after orthognathic surgery. Therefore, this study proposes an automated method to measure condylar changes in patients with skeletal class II malocclusion following surgical-orthodontic treatment.

Methods: Cone-beam CT (CBCT) scans from 48 patients were segmented using the nnU-Net network for automated maxillary and mandibular delineation. Regions unaffected by orthognathic surgery were selectively cropped. Automated registration yielded condylar displacement and volume calculations, each repeated three times for precision. Logistic regression and linear regression were used to analyse the correlation between condylar position changes at different time points.

Results: The Dice score for the automated segmentation of the condyle was 0.971. The intraclass correlation coefficients (ICCs) for all repeated measurements ranged from 0.93 to 1.00. The results of the automated measurement showed that 83.33% of patients exhibited condylar resorption occurring six months or more after surgery. Logistic regression and linear regression indicated a positive correlation between counterclockwise rotation in the pitch plane and condylar resorption (P < .01). And a positive correlation between the rotational angles in both three planes and changes in the condylar volume at six months after surgery (P ≤ .04).

Conclusions: This study's automated method for measuring condylar changes shows excellent repeatability. Skeletal class II malocclusion patients may experience condylar resorption after bimaxillary orthognathic surgery, and this is correlated with counterclockwise rotation in the sagittal plane.

Advances in knowledge: This study proposes an innovative multi-step registration method based on CBCT, and establishes an automated approach for quantitatively measuring condyle changes post-orthognathic surgery. This method opens up new possibilities for studying condylar morphology.

Cystic lesions of the gnathic bones present challenges in differential diagnosis. In recent years, artificial intelligence (AI) represented by deep learning (DL) has rapidly developed and emerged in the field of dental and maxillofacial radiology (DMFR). Dental radiography provides a rich resource for the study of diagnostic analysis methods for cystic lesions of the jaws and has attracted many researchers. The aim of the current study was to investigate the diagnostic performance of DL for cystic lesions of the jaws. Online searches were done on Google Scholar, PubMed, and IEEE Xplore databases, up to September 2023, with subsequent manual screening for confirmation. The initial search yielded 1862 titles, and 44 studies were ultimately included. All studies used DL methods or tools for the identification of a variable number of maxillofacial cysts. The performance of algorithms with different models varies. Although most of the reviewed studies demonstrated that DL methods have better discriminative performance than clinicians, further development is still needed before routine clinical implementation due to several challenges and limitations such as lack of model interpretability, multicentre data validation, etc. Considering the current limitations and challenges, future studies for the differential diagnosis of cystic lesions of the jaws should follow actual clinical diagnostic scenarios to coordinate study design and enhance the impact of AI in the diagnosis of oral and maxillofacial diseases.

Objectives: Panoramic radiography is one of the most commonly used diagnostic modalities in dentistry. Automatic recognition of panoramic radiography helps dentists in decision support. In order to improve the accuracy of the detection of dental structural problems in panoramic radiographs, we have improved the You Only Look Once (YOLO) network and verified the feasibility of this new method in aiding the detection of dental problems.

Methods: We propose a Deformable Multi-scale Adaptive Fusion Net (DMAF-Net) to detect 5 types of dental situations (impacted teeth, missing teeth, implants, crown restorations, and root canal-treated teeth) in panoramic radiography by improving the YOLO network. In DMAF-Net, we propose different modules to enhance the feature extraction capability of the network as well as to acquire high-level features at different scales, while using adaptively spatial feature fusion to solve the problem of scale mismatches of different feature layers, which effectively improves the detection performance. In order to evaluate the detection performance of the models, we compare the experimental results of different models in the test set and select the optimal results of the models by calculating the average of different metrics in each category as the evaluation criteria.

Results: About 1474 panoramic radiographs were divided into training, validation, and test sets in the ratio of 7:2:1. In the test set, the average precision and recall of DMAF-Net are 92.7% and 87.6%, respectively; the mean Average Precision (mAP0.5 and mAP[0.5:0.95]) are 91.8% and 63.7%, respectively.

Conclusions: The proposed DMAF-Net model improves existing deep learning models and achieves automatic detection of tooth structure problems in panoramic radiographs. This new method has great potential for new computer-aided diagnostic, teaching, and clinical applications in the future.