Oral Radiology最新文献_第9页

A retrospective comparative fractal and radiomorphometric analysis of the effect of bisphosphonate use pattern and duration on the mandible. 双膦酸盐使用方式和持续时间对下颌骨影响的回顾性比较分形和放射形态分析。

IF 1.6 3区医学

Oral Radiology Pub Date : 2025-04-01 Epub Date: 2025-01-08 DOI: 10.1007/s11282-024-00801-2

Nida Geçkil, Katibe Tuğçe Temur

{"title":"A retrospective comparative fractal and radiomorphometric analysis of the effect of bisphosphonate use pattern and duration on the mandible.","authors":"Nida Geçkil, Katibe Tuğçe Temur","doi":"10.1007/s11282-024-00801-2","DOIUrl":"10.1007/s11282-024-00801-2","url":null,"abstract":"Aims: The aim of this study was to investigate the effect of two different bisphosphonate types on bone using dental panoramic radiographs (DPRs) and to compare these findings with a healthy cohort.Study design: Panoramic dental radiographs of bisphosphonate users (30) and healthy individuals (30) were retrospectively evaluated for the study. Regarding FA, standardized 50 × 50 pixel regions of interest (ROI) were identified for each patient. Moreover, the assessment encompassed Mandibular Cortical Width (MCW), Panoramic Mandibular Index (PMI), and Mandibular Cortical Index (MCI). A significance level of p < 0.05 was deemed to be statistically significant.Results: The case group showed significantly higher MCW and PMI measurements than the control group (p < 0.001). Right and left MCI measurements differed depending on the type of drug used (p = 0.008 and p = 0.019, respectively). No discernible correlation was found between the time elapsed since the last dose and any measurement values (p > 0.05).Conclusion: This study showed that bisphosphonate type and duration of drug use have a significant effect on changes in cortical bone structure. The persistence of these effects, unaffected by the time since the previous dose, suggests that bisphosphonates have a long-lasting effect on bone.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"276-284"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142959217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 deep learning-based pipeline architecture for pulp stone detection on panoramic radiographs. 一种新的基于深度学习的管道结构用于全景x光片上的牙髓结石检测。

IF 1.6 3区医学

Oral Radiology Pub Date : 2025-04-01 Epub Date: 2025-01-14 DOI: 10.1007/s11282-025-00804-7

Ceyda Gürhan, Hasan Yiğit, Selim Yılmaz, Cihat Çetinkaya

{"title":"A novel deep learning-based pipeline architecture for pulp stone detection on panoramic radiographs.","authors":"Ceyda Gürhan, Hasan Yiğit, Selim Yılmaz, Cihat Çetinkaya","doi":"10.1007/s11282-025-00804-7","DOIUrl":"10.1007/s11282-025-00804-7","url":null,"abstract":"Objectives: Pulp stones are ectopic calcifications located in pulp tissue. The aim of this study is to introduce a novel method for detecting pulp stones on panoramic radiography images using a deep learning-based two-stage pipeline architecture.Materials and methods: The first stage involved tooth localization with the YOLOv8 model, followed by pulp stone classification using ResNeXt. 375 panoramic images were included in this study, and a comprehensive set of evaluation metrics, including precision, recall, false-negative rate, false-positive rate, accuracy, and F1 score was employed to rigorously assess the performance of the proposed architecture.Results: Despite the limited annotated training data, the proposed method achieved impressive results: an accuracy of 95.4%, precision of 97.1%, recall of 96.1%, false-negative rate of 3.9%, false-positive rate of 6.1%, and a F1 score of 96.6%, outperforming existing approaches in pulp stone detection.Conclusions: Unlike current studies, this approach adopted a more realistic scenario by utilizing a small dataset with few annotated samples, acknowledging the time-consuming and error-prone nature of expert labeling. The proposed system is particularly beneficial for dental students and newly graduated dentists who lack sufficient clinical experience, as it aids in the automatic detection of pulpal calcifications. To the best of our knowledge, this is the first study in the literature that propose a pipeline architecture to address the PS detection tasks on panoramic images.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"285-295"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Diagnostic performance of depth of invasion, thickness, and styloglossus and hyoglossus muscle invasion on magnetic resonance imaging in predicting potential neck lymph node metastasis in clinical N0 tongue cancer. 磁共振成像浸润深度、厚度及茎突舌骨和舌水肌浸润对临床N0舌癌颈部淋巴结转移的诊断价值。

IF 1.6 3区医学

Oral Radiology Pub Date : 2025-04-01 Epub Date: 2024-12-20 DOI: 10.1007/s11282-024-00796-w

Chika Yamada, Akira Baba, Satoshi Matsushima, Hideomi Yamauchi, Masato Nagaoka, Tomoya Suzuki, Yuika Kato, Hiroya Ojiri

{"title":"Diagnostic performance of depth of invasion, thickness, and styloglossus and hyoglossus muscle invasion on magnetic resonance imaging in predicting potential neck lymph node metastasis in clinical N0 tongue cancer.","authors":"Chika Yamada, Akira Baba, Satoshi Matsushima, Hideomi Yamauchi, Masato Nagaoka, Tomoya Suzuki, Yuika Kato, Hiroya Ojiri","doi":"10.1007/s11282-024-00796-w","DOIUrl":"10.1007/s11282-024-00796-w","url":null,"abstract":"Objectives: To evaluate previously reported quantitative (tumor thickness 11 mm and depth of invasion [DOI] 7.5 mm) and qualitative (styloglossus/hyoglossus muscle invasion [SHMI]) magnetic resonance imaging (MRI) parameters for predicting occult neck node metastasis in clinical N0 oral tongue squamous cell carcinoma.Methods: This single-center retrospective study included 76 patients. MRI images were independently reviewed by two radiologists for tumor thickness, DOI, and SHMI. Statistical analysis assessed the predictive capability of these parameters for 2-year potential lymph node metastasis.Results: Among the 76 cases, 30.2% developed 2-year potential lymph node metastasis. For tumor thickness ≥ 11 mm, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were 0.46, 0.68, 0.37, 0.75, and 0.61, respectively. DOI ≥ 7.5 mm exhibited a sensitivity, specificity, PPV, NPV, and accuracy of 0.73, 0.59, 0.42, 0.84, and 0.63, respectively. SHMI demonstrated a sensitivity, specificity, PPV, NPV, and accuracy of 0.87, 0.51, 0.46, 0.89, and 0.63, respectively.Conclusion: DOI ≥ 7.5 mm and SHMI demonstrated comparable diagnostic accuracy in predicting neck metastasis, surpassing tumor thickness of > 11 mm. These findings underscore their potential utility in guiding decisions concerning elective neck dissection.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"231-237"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Automated segmentation of dental restorations using deep learning: exploring data augmentation techniques. 使用深度学习的牙修复体自动分割：探索数据增强技术。

IF 1.6 3区医学

Oral Radiology Pub Date : 2025-04-01 Epub Date: 2024-12-09 DOI: 10.1007/s11282-024-00794-y

Berrin Çelik, Muhammed Emin Baslak, Mehmet Zahid Genç, Mahmut Emin Çelik

{"title":"Automated segmentation of dental restorations using deep learning: exploring data augmentation techniques.","authors":"Berrin Çelik, Muhammed Emin Baslak, Mehmet Zahid Genç, Mahmut Emin Çelik","doi":"10.1007/s11282-024-00794-y","DOIUrl":"10.1007/s11282-024-00794-y","url":null,"abstract":"Objectives: Deep learning has revolutionized image analysis for dentistry. Automated segmentation of dental radiographs is of great importance towards digital dentistry. The performance of deep learning models heavily relies on the quality and diversity of the training data. Data augmentation is a widely used technique implemented in machine learning and deep learning to artificially increase the size and diversity of a training dataset by applying various transformations to the original data.Methods: This work aims to automatically segment implants, prostheses, and fillings in panoramic images using 9 different deep learning segmentation models. Later, it explores the effect of data augmentation methods on segmentation performance of the models. Eight different data augmentation techniques are examined. Performance is evaluated by well-accepted metrics such as intersection over union (IoU) and Dice coefficient.Results: While averaging the segmentation results for the three classes, IoU varies between 0.62 and 0.82 while Dice score is between 0.75 and 0.9 among deep learning models used. Augmentation techniques provided performance improvements of up to 3.37%, 5.75% and 8.75% for implant, prosthesis and filling classes, respectively.Conclusions: Findings reveal that choosing optimal augmentation strategies depends on both model architecture and dental structure type.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"207-215"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

The effectiveness of fractal analysis in diagnosing temporomandibular joint disorders: a systematic review of clinical studies. 分形分析诊断颞下颌关节疾病的有效性：临床研究的系统回顾。

IF 1.6 3区医学

Oral Radiology Pub Date : 2025-04-01 Epub Date: 2024-12-09 DOI: 10.1007/s11282-024-00791-1

Sanjana Santhosh Kumar, Ravleen Nagi, Rachel Chacko, Junad Khan

{"title":"The effectiveness of fractal analysis in diagnosing temporomandibular joint disorders: a systematic review of clinical studies.","authors":"Sanjana Santhosh Kumar, Ravleen Nagi, Rachel Chacko, Junad Khan","doi":"10.1007/s11282-024-00791-1","DOIUrl":"10.1007/s11282-024-00791-1","url":null,"abstract":"Objective: This study aims to evaluate the application and effectiveness of fractal analysis (FA) in assessing temporomandibular joint disorders (TMDs) with dental imaging.Methods: This systematic review, conducted in adherence to PRISMA-P and Cochrane Handbook guidelines, involved a comprehensive search of five electronic indexed databases up to September 15, 2024. The thorough search aimed to ensure the inclusion of all relevant studies on dental imaging with fractal dimension (FD) analysis for TMDs. The risk of bias was performed using the revised QUADAS 2 tool.Results: Out of 342 studies retrieved, 15 met the inclusion criteria and were included in the systematic review. These studies comprised 7 retrospective and 8 prospective nonrandomized clinical studies. Various imaging modalities were used including panoramic, CT, CBCT, and MRI. Most studies reported significantly lower FD values in TMD patients than in controls suggesting FD analysis' potential for detecting early TMJ degenerative changes. However, a few studies did not find significant differences or lacked control groups, highlighting the variability in findings across the research. The overall risk of bias was high regarding the applicability of all included studies.Conclusion: The fractal dimension (FD) analysis of dental images shows potential as a valuable tool for detecting early degenerative changes in temporomandibular disorders (TMDs). It could enhance diagnostic efficiency by providing additional insights from routine radiographs. However, the variability in findings and methodologies underscores the need for further research to validate and standardize these techniques.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"153-168"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Fractal dimension, lacunarity, and bone area fraction analysis of peri-implant trabecular bone after prosthodontic loading. 修复加载后种植体周围骨小梁的分形维度、裂隙度和骨面积分数分析。

IF 1.6 3区医学

Oral Radiology Pub Date : 2025-01-01 Epub Date: 2024-11-11 DOI: 10.1007/s11282-024-00784-0

Nesrin Dundar, Elif Aslan, Onur Mutlu

{"title":"Fractal dimension, lacunarity, and bone area fraction analysis of peri-implant trabecular bone after prosthodontic loading.","authors":"Nesrin Dundar, Elif Aslan, Onur Mutlu","doi":"10.1007/s11282-024-00784-0","DOIUrl":"10.1007/s11282-024-00784-0","url":null,"abstract":"Objectives: To assess the structural alterations in peri-implant bone occurring 5 years after prosthodontic loading in panoramic radiography (PR).Methods: PR images of 44 mandibular and 33 maxillary implants along with 42 healthy control teeth taken before and 5 years after prosthodontic loading were included. Two regions-of-interest (ROI) were selected from mesial and distal surrounding bone of each implant and tooth. Then, the selected ROIs were divided to obtain three sub-ROIs (coronal, middle, and apical) on each side. A total of eight ROIs and sub-ROIs from each implant and control tooth were used for the calculations of fractal dimension (FD), lacunarity, and bone area fraction (BA/TA). The paired-sample t test was used to compare measurements before and 5 years after loading (p = 0.05).Results: Overall evaluation of 77 implants showed that FD decreased at the middle and apical peri-implant bone levels 5 years after loading (p < 0.05). In mandibular implants, BA/TA decreased after loading (p < 0.05). While FD decreased at the coronal level (p = 0.022), lacunarity increased at the middle level of mandibular implants (p < 0.05). In maxillary implants, FD decreased at the middle and BA/TA decreased at the coronal level (p < 0.05). On the other hand, BA/TA increased at the apical level of maxillary implants (p = 0.016) after loading. None of the parameters revealed any difference in the control group (p > 0.05).Conclusions: FD and BA/TA can be used to analyze structural changes in peri-implant bone after prosthodontic loading. Additionally, FD, lacunarity and BA/TA may provide useful information about changes occurring at different levels of peri-implant bone.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"120-130"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Correction: Comparing the accuracy of two machine learning models in detection and classification of periapical lesions using periapical radiographs. 更正：比较两种机器学习模型使用根尖周X光片检测和分类根尖周病变的准确性。

IF 1.6 3区医学

Oral Radiology Pub Date : 2025-01-01 DOI: 10.1007/s11282-024-00783-1

Do Hoang Viet, Le Hoang Son, Do Ngoc Tuyen, Tran Manh Tuan, Nguyen Phu Thang, Vo Truong Nhu Ngoc

引用次数: 0

Evaluation of root canal filling length on periapical radiograph using artificial intelligence. 利用人工智能评估根尖周X光片上的根管充填长度。

IF 1.6 3区医学

Oral Radiology Pub Date : 2025-01-01 Epub Date: 2024-10-27 DOI: 10.1007/s11282-024-00781-3

Berrin Çelik, Mehmet Zahid Genç, Mahmut Emin Çelik

{"title":"Evaluation of root canal filling length on periapical radiograph using artificial intelligence.","authors":"Berrin Çelik, Mehmet Zahid Genç, Mahmut Emin Çelik","doi":"10.1007/s11282-024-00781-3","DOIUrl":"10.1007/s11282-024-00781-3","url":null,"abstract":"Objectives: This work proposes a novel method to evaluate root canal filling (RCF) success using artificial intelligence (AI) and image analysis techniques.Methods: 1121 teeth with root canal treatment in 597 periapical radiographs (PARs) were anonymized and manually labeled. First, RCFs were segmented using 5 different state-of-the-art deep learning models based on convolutional neural networks. Their performances were compared based on the intersection over union (IoU), dice score and accuracy. Additionally, fivefold cross validation was applied for the best-performing model and their outputs were later used for further analysis. Secondly, images were processed via a graphical user interface (GUI) that allows dental clinicians to mark the apex of the tooth, which was used to find the distance between the apex of the tooth and the nearest RCF prediction of the deep learning model towards it. The distance can show whether the RCF is normal, short or long.Results: Model performances were evaluated by well-known evaluation metrics for segmentation such as IoU, Dice score and accuracy. CNN-based models can achieve an accuracy of 88%, an IoU of 79% and Dice score of 88% in segmenting root canal fillings.Conclusions: Our study demonstrates that AI-based solutions present accurate and reliable performance for root canal filling evaluation.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"102-110"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Patients' attitudes toward artificial intelligence in dentistry and their trust in dentists. 患者对牙科人工智能的态度及其对牙医的信任。

IF 1.6 3区医学

Oral Radiology Pub Date : 2025-01-01 Epub Date: 2024-10-09 DOI: 10.1007/s11282-024-00775-1

Hasibe Sevilay Bahadir, Neslihan Büşra Keskin, Emine Şebnem Kurşun Çakmak, Gürkan Güneç, Kader Cesur Aydin, Fatih Peker

{"title":"Patients' attitudes toward artificial intelligence in dentistry and their trust in dentists.","authors":"Hasibe Sevilay Bahadir, Neslihan Büşra Keskin, Emine Şebnem Kurşun Çakmak, Gürkan Güneç, Kader Cesur Aydin, Fatih Peker","doi":"10.1007/s11282-024-00775-1","DOIUrl":"10.1007/s11282-024-00775-1","url":null,"abstract":"Objectives: This study intended to evaluate patients' attitudes toward the use of AI in dental radiographic detection of occlusal caries and the impact of AI-based diagnosis on their trust in dentists.Methods: A total of 272 completed questionnaires were included in this study. In the first part of the study, approval was obtained from the patients, and data were collected about their socio-demographic characteristics. In the second part the 11-item Dentist Trust Scale was applied. In the third and fourth parts, there were questions about two clinical scenarios, the patients' knowledge of attitudes toward AI, and how the AI-based diagnosis had affected their trust. Evaluation was performed using a Likert-type scale. Data were analyzed with the Chi-square, one-way ANOVA, and ordinal logistic regression tests (p < 0.05).Results: The patients believed that \"AI is useful\" (3.86 ± 1.03) and were not afraid of the use of AI in dentistry (2.40 ± 1.05). Educational level was considerably related to the patients' attitudes to the use of AI for dental diagnostics (p < 0.05). The patients stated that \"dentists are extremely thorough and careful\" (4.39 ± 0.77).Conclusions: The patients displayed a positive attitude to AI-based diagnosis in the dental field and appear to exhibit trust in dentists. The use of Al in routine clinical practice can provide important benefit to physicians as a clinical decision support system in dentistry and understanding patients' attitudes may allow dentists to shape AI-supported dentistry in the future.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"52-59"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Style harmonization of panoramic radiography using deep learning. 利用深度学习协调全景放射摄影的风格。

IF 1.6 3区医学

Oral Radiology Pub Date : 2025-01-01 Epub Date: 2024-10-29 DOI: 10.1007/s11282-024-00782-2

Hak-Sun Kim, Jaejung Seol, Ji-Yun Lee, Sang-Sun Han, Jaejun Yoo, Chena Lee

{"title":"Style harmonization of panoramic radiography using deep learning.","authors":"Hak-Sun Kim, Jaejung Seol, Ji-Yun Lee, Sang-Sun Han, Jaejun Yoo, Chena Lee","doi":"10.1007/s11282-024-00782-2","DOIUrl":"10.1007/s11282-024-00782-2","url":null,"abstract":"Objectives: This study aimed to harmonize panoramic radiograph images from different equipment in a single institution to display similar styles.Methods: A total of 15,624 panoramic images were acquired using two different equipment: 8079 images from Rayscan Alpha Plus (R-unit) and 7545 images from Pax-i plus (P-unit). Among these, 222 image pairs (444 images) from the same patients comprised the test dataset to harmonize the P-unit images with the R-unit image style using CycleGAN. Objective evaluations included Frechet Inception Distance (FID) and Learned Perceptual Image Patch Similarity (LPIPS) assessments. Additionally, expert evaluation was conducted by two oral and maxillofacial radiologists on transformed P-unit and R-unit images. The statistical analysis of LPIPS employed a Student's t-test.Results: The FID and mean LPIPS values of the transformed P-unit images (7.362, 0.488) were lower than those of the original P-unit images (8.380, 0.519), with a significant difference in LPIPS (p < 0.05). The experts evaluated 43.3-46.7% of the transformed P-unit images as R-unit images, 20.0-28.3% as P-units, and 28.3-33.3% as undetermined images.Conclusions: CycleGAN has the potential to harmonize panoramic radiograph image styles. Enhancement of the model is anticipated for the application of images produced by additional units.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"111-119"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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