Dento maxillo facial radiology最新文献

Carotid calcifications in panoramic radiographs can predict vascular risk. 全景照片中的颈动脉钙化可预测血管风险。

IF 2.9 2区医学

Dento maxillo facial radiology Pub Date : 2024-11-20 DOI: 10.1093/dmfr/twae057

Maria Garoff, Jan Ahlqvist, Eva Levring Jäghagen, Per Wester, Elias Johansson

{"title":"Carotid calcifications in panoramic radiographs can predict vascular risk.","authors":"Maria Garoff, Jan Ahlqvist, Eva Levring Jäghagen, Per Wester, Elias Johansson","doi":"10.1093/dmfr/twae057","DOIUrl":"https://doi.org/10.1093/dmfr/twae057","url":null,"abstract":"Objectives: Carotid artery calcification (CAC) is occasionally detected in panoramic radiographs (PR). Bilateral vessel-outlining (BVO) CACs are independent risk markers for future vascular events and have been associated with large plaque area. If accounting for plaque area, BVO CACs may no longer be an independent risk marker for vascular events. The aim of this study was to explore the association between BVO CACs and vascular events and its relationship with carotid ultrasound plaque area.Methods: In this cohort study we prospectively included 212 consecutive participants with CACs detected in PR that were performed to plan and evaluate odontologic treatment. Of these 212, 43 (20%) had BVO CACs. Plaque area was assessed with ultrasound at baseline. Primary outcome was major adverse cardiovascular events (MACE) during follow-up.Results: Vessel-outlining CAC was associated with larger plaque area on the same side (p = 0.03) and BVO CACs were associated with larger total plaque area (both sides summed) than other CAC features (p = 0.004). Mean follow-up was 7.0 years and 72 (34%) participants had more than one MACE. In bivariable analyses, both BVO CACs (HR 2.5, p < 0.001) and total plaque area (HR 1.8 per cm2, p = 0.008) were associated with MACE. When entering BVO CACs, plaque area and other relevant co-variates in a multivariable model, BVO CACs were virtually unchanged (HR 2.4, p = 0.001), but total plaque area was no longer significant (HR 1.0, p = 0.92).Conclusion: Present results support the contention that BVO CACs are a stronger predictor for future vascular events than carotid ultrasound plaque area.","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142681094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preparing for downstream tasks in AI for dental radiology: a baseline performance comparison of deep learning models. 为牙科放射学人工智能的下游任务做准备：深度学习模型的基线性能比较。

IF 2.9 2区医学

Dento maxillo facial radiology Pub Date : 2024-11-19 DOI: 10.1093/dmfr/twae056

Fara A Fernandes, Mouzhi Ge, Georgi Chaltikyan, Martin W Gerdes, Christian W Omlin

{"title":"Preparing for downstream tasks in AI for dental radiology: a baseline performance comparison of deep learning models.","authors":"Fara A Fernandes, Mouzhi Ge, Georgi Chaltikyan, Martin W Gerdes, Christian W Omlin","doi":"10.1093/dmfr/twae056","DOIUrl":"https://doi.org/10.1093/dmfr/twae056","url":null,"abstract":"Objectives: To compare the performance of the convolutional neural network (CNN) with the vision transformer (ViT) and the gated multilayer perceptron (gMLP) in the classification of radiographic images of dental structures.Methods: Retrospectively collected 2-dimensional images derived from cone beam computed tomographic volumes were used to train CNN, ViT and gMLP architectures as classifiers for 4 different cases. Cases selected for training the architectures were the classification of the radiographic appearance of maxillary sinuses, maxillary and mandibular incisors, presence or absence of the mental foramen and the positional relationship of the mandibular third molar to the inferior alveolar nerve canal. The performance metrics (sensitivity, specificity, precision, accuracy and f1-score) and area under curve (AUC) - receiver operating characteristic and precision-recall curves were calculated.Results: The ViT with an accuracy of 0.74-0.98, performed on par with the CNN model (accuracy 0.71-0.99) in all tasks. The gMLP displayed marginally lower performance (accuracy 0.65-0.98) as compared to the CNN and ViT. For certain tasks, the ViT outperformed the CNN. The AUCs ranged from 0.77-1.00 (CNN), 0.80-1.00 (ViT) and 0.73-1.00 (gMLP) for all of the 4 cases.Conclusions: The difference in performance of the ViT, gMLP and the CNN (the current state-of-the-art) was significant in certain tasks. This difference in model performance for various tasks proves that capabilities of different architectures may be leveraged.Advances in knowledge: The vision transformer, followed by the gated multilayer perceptron are deep learning models that exhibit comparable performance with the convolutional neural network in the classification of dental radiographic images.","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142674739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gray values and noise behavior of cone-beam computed tomography machines-an in vitro study. 锥形束计算机断层扫描机的灰度值和噪声行为--体外研究。

IF 2.9 2区医学

Dento maxillo facial radiology Pub Date : 2024-11-19 DOI: 10.1093/dmfr/twae053

Nicolly Oliveira-Santos, Hugo Gaêta-Araujo, Rubens Spin-Neto, Dorothea Dagassan-Berndt, Michael M Bornstein, Matheus L Oliveira, Francisco Haiter-Neto, Deborah Queiroz Freitas, Ralf Schulze

{"title":"Gray values and noise behavior of cone-beam computed tomography machines-an in vitro study.","authors":"Nicolly Oliveira-Santos, Hugo Gaêta-Araujo, Rubens Spin-Neto, Dorothea Dagassan-Berndt, Michael M Bornstein, Matheus L Oliveira, Francisco Haiter-Neto, Deborah Queiroz Freitas, Ralf Schulze","doi":"10.1093/dmfr/twae053","DOIUrl":"https://doi.org/10.1093/dmfr/twae053","url":null,"abstract":"Objectives: To systematically evaluate the mean gray values (MGV) and noise provided by bone and soft tissue equivalent materials and air imaged with varied acquisition parameters in nine cone-beam computed tomography (CBCT) machines.Methods: The DIN6868-161 phantom, composed of bone and soft tissue equivalent material and air gap, was scanned in nine CBCT machines. Tube current (mA) and tube voltage (kV), field of view (FOV) size, and rotation angle were varied over the possible range. The effect of the acquisition parameters on the MGV and contrast-to-noise indicator (CNI) was analyzed by Kruskal Wallis and Dunn-Bonferroni tests for each machine independently (α = 0.05).Results: Tube current did not influence MGV in most machines. Viso G7 and Veraview X800 presented a decrease in the MGV for increasing kV. For ProMax 3D MAX and X1, the kV did not affect the MGV. For the majority of machines, MGV decreased with increasing FOV height. In general, the rotation angle did not affect the MGV. In addition, CNI was lower with lower radiation and large FOV and did not change from 80 kV in all machines.Conclusions: The MGV and noise provided by the tested phantom vary largely among machines. The MGV is mainly influenced by the FOV size, especially for bone equivalent radiodensity. For most machines, when the acquisition parameters affect the MGV, the MGV decrease with the increase in the acquisition parameters.Advances in knowledge: Knowing the expected GV behavior in different exposure conditions hold potential for future calibration of MGV among CBCT machines.","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporomandibular joint assessment in MRI images using artificial intelligence tools: Where are we now? A systematic review. 利用人工智能工具对磁共振成像图像中的颞下颌关节进行评估：我们现在在哪里？系统综述。

IF 2.9 2区医学

Dento maxillo facial radiology Pub Date : 2024-11-19 DOI: 10.1093/dmfr/twae055

Mitul Manek, Ibraheem Maita, Diego Filipe Bezerra Silva, Daniela Pita de Melo, Paul W Major, Jacob L Jaremko, Fabiana T Almeida

{"title":"Temporomandibular joint assessment in MRI images using artificial intelligence tools: Where are we now? A systematic review.","authors":"Mitul Manek, Ibraheem Maita, Diego Filipe Bezerra Silva, Daniela Pita de Melo, Paul W Major, Jacob L Jaremko, Fabiana T Almeida","doi":"10.1093/dmfr/twae055","DOIUrl":"https://doi.org/10.1093/dmfr/twae055","url":null,"abstract":"Objectives: To summarize the current evidence on the performance of artificial intelligence (AI) algorithms for the temporomandibular joint (TMJ) disc assessment and TMJ internal derangement diagnosis in magnetic resonance imaging (MRI) images.Methods: Studies were gathered by searching five electronic databases and partial grey literature up to May 27th, 2024. Studies in humans using AI algorithms to detect or to diagnose internal derangements in MRI images were included. The methodological quality of the studies was evaluated using the Quality Assessment Tool for Diagnostic of Accuracy Studies-2 (QUADAS-2) and a proposed checklist for dental AI studies.Results: Thirteen studies were included in this systematic review. Most of studies assessed disc position. One study assessed disc perforation. A high heterogeneity related to the patient selection domain was found between the studies. The studies used a variety of AI approaches and performance metrics with CNN based models being the most used. A high performance of AI models compared to humans was reported with accuracy ranging from 70% to 99%.Conclusions: The integration of AI, particularly deep learning, in TMJ MRI shows promising results as a diagnostic-assistance tool to segment TMJ structures and to classify disc position. Further studies exploring more diverse and multicenter data will improve the validity and generalizability of the models before being implemented in clinical practice.","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142674990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel method for measuring the direction and angle of Central ray and predicting rotation center via panorama phantom. 通过全景模型测量中心射线的方向和角度并预测旋转中心的新方法。

IF 2.9 2区医学

Dento maxillo facial radiology Pub Date : 2024-10-18 DOI: 10.1093/dmfr/twae050

Byung-Ju Joh, Sam-Sun Lee, Han-Gyeol Yeom, Gyu-Dong Jo, Jo-Eun Kim, Kyung-Hoe Huh, Won-Jin Yi, Min-Suk Heo

{"title":"A novel method for measuring the direction and angle of Central ray and predicting rotation center via panorama phantom.","authors":"Byung-Ju Joh, Sam-Sun Lee, Han-Gyeol Yeom, Gyu-Dong Jo, Jo-Eun Kim, Kyung-Hoe Huh, Won-Jin Yi, Min-Suk Heo","doi":"10.1093/dmfr/twae050","DOIUrl":"https://doi.org/10.1093/dmfr/twae050","url":null,"abstract":"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.","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142460408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using cone-beam CT for appropriate nostril selection in nasotracheal intubation. 使用锥形束计算机断层扫描在鼻气管插管中选择合适的鼻孔。

IF 2.9 2区医学

Dento maxillo facial radiology Pub Date : 2024-10-01 DOI: 10.1093/dmfr/twae038

Funda Arun, Derya Icoz, Ahmet Akti, Gokhan Gurses

{"title":"Using cone-beam CT for appropriate nostril selection in nasotracheal intubation.","authors":"Funda Arun, Derya Icoz, Ahmet Akti, Gokhan Gurses","doi":"10.1093/dmfr/twae038","DOIUrl":"10.1093/dmfr/twae038","url":null,"abstract":"Objectives: Nasotracheal intubation is a standard blind procedure associated with various complications. The selection of the appropriate nostril is crucial to preventing most of these complications. The present study aimed to evaluate the predictive ability of cone-beam CT (CBCT) images to select the correct nostril for nasotracheal intubation.Methods: The study encompassed 60 patients who underwent maxillofacial surgery with nasotracheal intubation under general anaesthesia. While the anaesthetist made the appropriate nostril selection clinically according to a simple occlusion test and spatula test, the radiologist made the selection after analysing various CBCT findings such as the angle and direction of nasal septum deviation (NSD), minimum bone distance along the intubation path, and the presence of inferior turbinate hypertrophy. The appropriateness of these choices made blindly at different times was evaluated using descriptive statistics, chi-squared test, and independent samples t-test.Results: The study found that 83.3% of the suggested nostril intubations were successful. We also observed that intubation duration was longer when inferior turbinate hypertrophy was present (P = .031). However, there was no statistical relationship between the presence of epistaxis and septal deviation (P = .395). Nonetheless, in 64.3% of cases with epistaxis, the intubated nostril and the septum deviation direction were the same.Conclusions: Pre-operative evaluations using CBCT can aid anaesthetists for septum deviation and turbinate hypertrophy, as both can impact intubation success rates and duration.","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"515-520"},"PeriodicalIF":2.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141787492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

In vitro accuracy of ultra-low dose cone-beam CT for detection of proximal caries. 超低剂量锥形束计算机断层扫描在体外检测近端龋齿的准确性。

IF 2.9 2区医学

Dento maxillo facial radiology Pub Date : 2024-10-01 DOI: 10.1093/dmfr/twae030

Aria Taeby, Seyyed Amir Seyyedi, Maryam Mostafavi

{"title":"In vitro accuracy of ultra-low dose cone-beam CT for detection of proximal caries.","authors":"Aria Taeby, Seyyed Amir Seyyedi, Maryam Mostafavi","doi":"10.1093/dmfr/twae030","DOIUrl":"10.1093/dmfr/twae030","url":null,"abstract":"Objectives: This study aimed to assess the accuracy of ultra-low dose (ULD) cone-beam CT (CBCT) for detection of proximal caries.Methods: This in vitro study evaluated 104 molar and premolar teeth. The teeth were mounted in dry skulls and underwent CBCT with 4 protocols of high-resolution (HR), normal (NORM), ULD-HR, and ULD-NORM; 78 CBCT images were scored by 3 observers for the presence and penetration depth of caries twice with a 2-week interval using a 5-point Likert scale. The teeth were then sectioned and observed under a stereomicroscope (gold standard). The 4 protocols were compared with each other and with the gold standard. The receiver operating characteristic curve was drawn, and the area under the curve (AUC) was calculated and compared by the Chi-square test (alpha = .05).Results: The interobserver agreement ranged from 0.5233 to 0.6034 for ULD-NORM, 0.5380 to 0.6279 for NORM, 0.5856 to 0.6300 for ULD-HR, and 0.6614 to 0.7707 for HR images. The intra-observer agreement ranged from 0.6027 to 0.8812 for ULD-HR, 0.7083 to 0.7556 for HR, 0.6076 to 0.9452 for ULD-NORM, and 0.7012 to 0.9221 for NORM images. Comparison of AUC revealed no significant difference between NORM and ULD-NORM (P > .05), or HR and ULD-HR (P > .05). The highest AUC belonged to HR (0.8529) and the lowest to NORM (0.7774).Conclusions: Considering the significant reduction in radiation dose in ULD CBCT and its acceptable diagnostic accuracy for detection of proximal caries, this protocol may be used for detection of proximal carious lesions and assessment of their depth.","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"459-467"},"PeriodicalIF":2.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141558308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Striving to include the most recent trends and innovations, while also honouring our past. 我们努力将最新的趋势和创新纳入其中，同时也向我们的过去致敬。

IF 2.9 2区医学

Dento maxillo facial radiology Pub Date : 2024-10-01 DOI: 10.1093/dmfr/twae052

Michael M Bornstein

引用次数: 0

Automatic classification and segmentation of multiclass jaw lesions in cone-beam CT using deep learning. 利用深度学习对锥形束计算机断层扫描中的多类颌骨病变进行自动分类和分割。

IF 2.9 2区医学

Dento maxillo facial radiology Pub Date : 2024-10-01 DOI: 10.1093/dmfr/twae028

Wei Liu, Xiang Li, Chang Liu, Ge Gao, Yutao Xiong, Tao Zhu, Wei Zeng, Jixiang Guo, Wei Tang

{"title":"Automatic classification and segmentation of multiclass jaw lesions in cone-beam CT using deep learning.","authors":"Wei Liu, Xiang Li, Chang Liu, Ge Gao, Yutao Xiong, Tao Zhu, Wei Zeng, Jixiang Guo, Wei Tang","doi":"10.1093/dmfr/twae028","DOIUrl":"10.1093/dmfr/twae028","url":null,"abstract":"Objectives: To develop and validate a modified deep learning (DL) model based on nnU-Net for classifying and segmenting five-class jaw lesions using cone-beam CT (CBCT).Methods: A total of 368 CBCT scans (37 168 slices) were used to train a multi-class segmentation model. The data underwent manual annotation by two oral and maxillofacial surgeons (OMSs) to serve as ground truth. Sensitivity, specificity, precision, F1-score, and accuracy were used to evaluate the classification ability of the model and doctors, with or without artificial intelligence assistance. The dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and segmentation time were used to evaluate the segmentation effect of the model.Results: The model achieved the dual task of classifying and segmenting jaw lesions in CBCT. For classification, the sensitivity, specificity, precision, and accuracy of the model were 0.871, 0.974, 0.874, and 0.891, respectively, surpassing oral and maxillofacial radiologists (OMFRs) and OMSs, approaching the specialist. With the model's assistance, the classification performance of OMFRs and OMSs improved, particularly for odontogenic keratocyst (OKC) and ameloblastoma (AM), with F1-score improvements ranging from 6.2% to 12.7%. For segmentation, the DSC was 87.2% and the ASSD was 1.359 mm. The model's average segmentation time was 40 ± 9.9 s, contrasting with 25 ± 7.2 min for OMSs.Conclusions: The proposed DL model accurately and efficiently classified and segmented five classes of jaw lesions using CBCT. In addition, it could assist doctors in improving classification accuracy and segmentation efficiency, particularly in distinguishing confusing lesions (eg, AM and OKC).","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"439-446"},"PeriodicalIF":2.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141466868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced multistage deep learning for diagnosing anterior disc displacement in the temporomandibular joint using MRI. 利用磁共振成像诊断颞下颌关节前椎间盘移位的增强型多级深度学习。

IF 2.9 2区医学

Dento maxillo facial radiology Pub Date : 2024-10-01 DOI: 10.1093/dmfr/twae033

Chang-Ki Min, Won Jung, Subin Joo

{"title":"Enhanced multistage deep learning for diagnosing anterior disc displacement in the temporomandibular joint using MRI.","authors":"Chang-Ki Min, Won Jung, Subin Joo","doi":"10.1093/dmfr/twae033","DOIUrl":"10.1093/dmfr/twae033","url":null,"abstract":"Objectives: This study aimed to propose a new method for the automatic diagnosis of anterior disc displacement of the temporomandibular joint (TMJ) using MRI and deep learning. By using a multistage approach, the factors affecting the final result can be easily identified and improved.Methods: This study introduces a multistage automatic diagnostic technique using deep learning. This process involves segmenting the target from MR images, extracting distance parameters, and classifying the diagnosis into 3 classes. MRI exams of 368 TMJs from 204 patients were evaluated for anterior disc displacement. In the first stage, 5 algorithms were used for the semantic segmentation of the disc and condyle. In the second stage, 54 distance parameters were extracted from the segments. In the third stage, a rule-based decision model was developed to link the parameters with the expert diagnosis results.Results: In the first stage, DeepLabV3+ showed the best result (95% Hausdorff distance, Dice coefficient, and sensitivity of 6.47 ± 7.22, 0.84 ± 0.07, and 0.84 ± 0.09, respectively). This study used the original MRI exams as input without preprocessing and showed high segmentation performance compared with that of previous studies. In the third stage, the combination of SegNet and a random forest model yielded an accuracy of 0.89 ± 0.06.Conclusions: An algorithm was developed to automatically diagnose TMJ-anterior disc displacement using MRI. Through a multistage approach, this algorithm facilitated the improvement of results and demonstrated high accuracy from more complex inputs. Furthermore, existing radiological knowledge was applied and validated.","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"488-496"},"PeriodicalIF":2.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141723233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0