Oral Radiology最新文献_第2页

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 : 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":"https://doi.org/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":""},"PeriodicalIF":1.6,"publicationDate":"2024-10-09","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

Acceptability of artificial intelligence in dental radiology among patients in India: are we ready for this revolution? 印度患者对人工智能牙科放射学的接受程度：我们准备好迎接这场革命了吗？

IF 1.6 3区医学

Oral Radiology Pub Date : 2024-10-09 DOI: 10.1007/s11282-024-00777-z

Preeti Chawla Arora, Komaldeep Kaur Sandhu, Aman Arora, Ambika Gupta, Mandavi Waghmare, Vasundhara Rampal

{"title":"Acceptability of artificial intelligence in dental radiology among patients in India: are we ready for this revolution?","authors":"Preeti Chawla Arora, Komaldeep Kaur Sandhu, Aman Arora, Ambika Gupta, Mandavi Waghmare, Vasundhara Rampal","doi":"10.1007/s11282-024-00777-z","DOIUrl":"https://doi.org/10.1007/s11282-024-00777-z","url":null,"abstract":"Objective: In recent times, artificial Intelligence (AI) has gained popularity in medical as well as dental radiology. Studies have been conducted among medical and dental students and professionals about the knowledge and understanding towards AI. The aim of this study was to investigate the perceptions and acceptability of AI in dental radiology among a group of Indian patients seeking dental treatment.Methods: A cross-sectional research was planned with a validated questionnaire, containing ten close ended questions amongst 1562 patients. Their sociodemographic characters, opinions and attitudes regarding AI and feasibility of acceptance of AI-based dental radiological diagnosis among patients was evaluated. The study sample was divided in various groups on the basis of their age; group-1(16-30 years), group-2(31-45 years) and group-3(>45 years), educational status and urban/rural background. Statistical analysis was done by Chi-square test with significance value set at p< 0.005.Results-: The participants possessed impressive knowledge about AI. Patients' awareness, attitudes and acceptability towards AI for dental radiographic diagnosis were substantially influenced by age, education level and residential background. Although many of them, especially the urban and more educated participants believed that AI could be more accurate, they preferred the human judgement. Overall, a negative attitude in terms of acceptability of AI in dental radiology was observed in this study.Conclusions: Participants opined that AI should only be used as an auxiliary tool and valued clinical judgment over AI in ambiguous situations. It is recommended that this promising technological advancement can be used for initial screening in dental radiology.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395616","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

Evaluation of fractal analysis and radiomorphometric measurements of mandibular bone structure in bruxism and non-bruxism paediatric patients. 评估磨牙症和非磨牙症儿科患者下颌骨结构的分形分析和放射形态测量。

IF 1.6 3区医学

Oral Radiology Pub Date : 2024-10-08 DOI: 10.1007/s11282-024-00776-0

Aslı Soğukpınar Önsüren, Katibe Tuğçe Temur

{"title":"Evaluation of fractal analysis and radiomorphometric measurements of mandibular bone structure in bruxism and non-bruxism paediatric patients.","authors":"Aslı Soğukpınar Önsüren, Katibe Tuğçe Temur","doi":"10.1007/s11282-024-00776-0","DOIUrl":"https://doi.org/10.1007/s11282-024-00776-0","url":null,"abstract":"Objectives: The goal of this examination was to compare the impact of probable sleep/awake bruxism on the mandibular trabecular bone structure by fractal analysis (FA) with digital panoramic radiograph (DPR) and radiomorphometric measurements in paediatric patients with bruxism.Methods: The examination included 130 participants with 63 patients with probable sleep/awake bruxism and 67 control groups. Bilateral regions of interest (ROI) in three regions were examined as ROI1: mandibular ramus, ROI2: mandibular angulus, ROI3: anterior to the molar teeth. Radiomorphometric measurements were taken of the mandibular cortical width (MCW), panoramic mandibular index (PMI), and mandibular cortical index (MCI). p < 0.05 was approved for statistical significance.Results: The ROI-1, ROI-2, and ROI-3 values were defined to be statistically significantly high in the bruxism group (p < 0.05). No significant difference was found between the groups in the other values (p > 0.05). There was no difference in the age and gender for any of the parameters (p > 0.05).Conclusion: In children and adolescents, the mandibular trabecular bone can be affected by bruxism. FA can be used as an auxiliary method for finding the mandibular trabecular differences of patients with bruxism in paediatric dentistry just as it can for adults.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395617","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

Improved soft-tissue visibility on cone-beam computed tomography with an image-generating artificial intelligence model using a cyclic generative adversarial network. 利用循环生成对抗网络的图像生成人工智能模型提高锥束计算机断层扫描的软组织可见度。

IF 1.6 3区医学

Oral Radiology Pub Date : 2024-10-01 Epub Date: 2024-06-28 DOI: 10.1007/s11282-024-00763-5

Motoki Fukuda, Michihito Nozawa, Hironori Akiyama, Eiichiro Ariji, Yoshiko Ariji

{"title":"Improved soft-tissue visibility on cone-beam computed tomography with an image-generating artificial intelligence model using a cyclic generative adversarial network.","authors":"Motoki Fukuda, Michihito Nozawa, Hironori Akiyama, Eiichiro Ariji, Yoshiko Ariji","doi":"10.1007/s11282-024-00763-5","DOIUrl":"10.1007/s11282-024-00763-5","url":null,"abstract":"Objectives: The objective of this study was to enhance the visibility of soft tissues on cone-beam computed tomography (CBCT) using a CycleGAN network trained on CT images.Methods: Training and evaluation of the CycleGAN were conducted using CT and CBCT images collected from Aichi Gakuin University (α facility) and Osaka Dental University (β facility). Synthesized images (sCBCT) output by the CycleGAN network were evaluated by comparing them with the original images (oCBCT) and CT images, and assessments were made using histogram analysis and human scoring of soft-tissue anatomical structures and cystic lesions.Results: The histogram analysis showed that on sCBCT, soft-tissue anatomical structures showed significant shifts in voxel intensity toward values resembling those on CT, with the mean values for all structures approaching those of CT and the specialists' visibility scores being significantly increased. However, improvement in the visibility of cystic lesions was limited.Conclusions: Image synthesis using CycleGAN significantly improved the visibility of soft tissue on CBCT, with this improvement being particularly notable from the submandibular region to the floor of the mouth. Although the effect on the visibility of cystic lesions was limited, there is potential for further improvement through refinement of the training method.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"508-519"},"PeriodicalIF":1.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141473105","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

Osteosarcoma associated with cemento-osseous dysplasia: co-incidence or two related entities? 伴有骨水泥骨发育不良的骨肉瘤：巧合还是两个相关实体？

IF 1.6 3区医学

Oral Radiology Pub Date : 2024-10-01 Epub Date: 2024-06-09 DOI: 10.1007/s11282-024-00758-2

Suvarna Indermun, Fadi Titinchi, Julandi Alwan, Jean Morkel, Christoffel Johannes Nortje

{"title":"Osteosarcoma associated with cemento-osseous dysplasia: co-incidence or two related entities?","authors":"Suvarna Indermun, Fadi Titinchi, Julandi Alwan, Jean Morkel, Christoffel Johannes Nortje","doi":"10.1007/s11282-024-00758-2","DOIUrl":"10.1007/s11282-024-00758-2","url":null,"abstract":"Background: Osteosarcoma of the jaws is a rare primary malignant tumor of bone. The clinical, radiological and histopathological features of a case associated with cemento-osseous dysplasia is presented.Case report: A 57-year-old mixed-race female presented with a large, progressive, swelling of the right mandible. Radiographic examination revealed two associated lesions. Partially defined irregular radiopacities were noted in the left mandible, extending from the premolar to the molar region. The lesion had a cotton-wool appearance and resembled a fibro-osseous lesion; i.e. cemento-osseous dysplasia. A second large, expansive and irregular, radiopaque lesion was noted on the right angle of the mandible, extending beyond the inferior cortex of the mandible. The internal structure was heterogeneous and resembled irregular bone formation. The classic \"sunburst\" appearance of radiating bony spicules can be seen in the posterior-anterior view and the CBCT 3D reconstruction, indicating the outgrowth of the tumor matrix. Histopathological exam confirmed a final diagnosis of osteosarcoma closely associated with cemento-osseous dysplasia. The patient underwent a fludeoxyglucose-18 (FDG) positron emission tomography (PET) scan which indicated metastasis in the left lung and increased uptake in the right mandible. Chemotherapy was initially administered with a plan to resect the tumor, however, the patient demised as a result of medical complications.Conclusion: The question in the literature remains whether these two entities are coincidentally found or arise from each other. Nevertheless, it is important for clinicians to closely monitor patients with cemento-osseous dysplasia and biopsy any suspicious lesions that may develop into osteosarcoma.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"546-554"},"PeriodicalIF":1.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11379726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141293941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of canalis sinuosus and accessory canal morphology by cone-beam computed tomography. 通过锥形束计算机断层扫描评估鼻窦和附属管形态。

IF 1.6 3区医学

Oral Radiology Pub Date : 2024-10-01 Epub Date: 2024-07-26 DOI: 10.1007/s11282-024-00767-1

Ceren Aktuna Belgin, Gozde Serindere, Zarif Ece Hammudioglu, Merve Kucuk

{"title":"Evaluation of canalis sinuosus and accessory canal morphology by cone-beam computed tomography.","authors":"Ceren Aktuna Belgin, Gozde Serindere, Zarif Ece Hammudioglu, Merve Kucuk","doi":"10.1007/s11282-024-00767-1","DOIUrl":"10.1007/s11282-024-00767-1","url":null,"abstract":"Objective: To evaluate canalis sinuosus (CS) and accessory canalis sinuosus (AC) morphology and their relationship with the impacted canine on cone-beam computed tomography (CBCT) images.Methods: The diameter and location of the CS, its distance from the nasal cavity (NC-CS), its distance from the buccal cortical plate (BC-CS), and its distance from the alveolar ridge crest (AR-CS) were evaluated on 1000 CBCT scans. The prevalence and termination of AC and the presence of impacted canines were also evaluated.Results: CS was detected in 89 (8.9%) of 1000 CBCTs. The mean CS diameter was found as 1.34 ± 0.53 mm. No statistically significant difference was found between gender, age, direction, and CS presence and diameter. CS was most frequently seen in regions 11 (23.6%) and 13 (23.6%). The average NC-CS, BC-CS, and AR-CS length was 6.14, 6.06 and 4.35 mm, respectively. AC was detected in 22 patients (24.71%). There was no statistically significant difference between the presence of AC and gender, age, CS diameter, NC-CS, BC-CS, and AR-CS distance. BC-CS length and AR-CS length were statistically significantly higher in patients with impacted canines.Conclusions: It should be kept in mind that the CS diameter, NC-CS, BC-CS, and AR-CS distance may increase in the presence of an impacted canine and the integrity of the neurovascular structure should be preserved. The fact that the CS is often localized in the palatial region requires a detailed evaluation of the anterior maxillary region with three-dimensional imaging methods.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"530-537"},"PeriodicalIF":1.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141768116","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

Arteriovenous fistula of the mandible: a case report. 下颚动静脉瘘：病例报告。

IF 1.6 3区医学

Oral Radiology Pub Date : 2024-10-01 Epub Date: 2024-06-19 DOI: 10.1007/s11282-024-00762-6

Mattia Todaro, Ermal Pashaj, Alessandro Pedicelli, Andrea Alexandre, Gianmarco Saponaro, Giulio Gasparini, Alessandro Moro

引用次数: 0

Deep learning classification performance for diagnosing condylar osteoarthritis in patients with dentofacial deformities using panoramic temporomandibular joint projection images. 利用颞下颌关节全景投影图像诊断颌面部畸形患者髁状突骨关节炎的深度学习分类性能。

IF 1.6 3区医学

Oral Radiology Pub Date : 2024-10-01 Epub Date: 2024-07-11 DOI: 10.1007/s11282-024-00768-0

Yukiko Iwase, Tomoya Sugiki, Yoshitaka Kise, Masako Nishiyama, Michihito Nozawa, Motoki Fukuda, Yoshiko Ariji, Eiichiro Ariji

{"title":"Deep learning classification performance for diagnosing condylar osteoarthritis in patients with dentofacial deformities using panoramic temporomandibular joint projection images.","authors":"Yukiko Iwase, Tomoya Sugiki, Yoshitaka Kise, Masako Nishiyama, Michihito Nozawa, Motoki Fukuda, Yoshiko Ariji, Eiichiro Ariji","doi":"10.1007/s11282-024-00768-0","DOIUrl":"10.1007/s11282-024-00768-0","url":null,"abstract":"Objective: The present study aimed to assess the consistencies and performances of deep learning (DL) models in the diagnosis of condylar osteoarthritis (OA) among patients with dentofacial deformities using panoramic temporomandibular joint (TMJ) projection images.Methods: A total of 68 TMJs with or without condylar OA in dentofacial deformity patients were tested to verify the consistencies and performances of DL models created using 252 TMJs with or without OA in TMJ disorder and dentofacial deformity patients; these models were used to diagnose OA on conventional panoramic (Con-Pa) images and open (Open-TMJ) and closed (Closed-TMJ) mouth TMJ projection images. The GoogLeNet and VGG-16 networks were used to create the DL models. For comparison, two dental residents with < 1 year of experience interpreting radiographs evaluated the same condyle data that had been used to test the DL models.Results: On Open-TMJ images, the DL models showed moderate to very good consistency, whereas the residents' demonstrated fair consistency on all images. The areas under the curve (AUCs) of both DL models on Con-Pa (0.84 for GoogLeNet and 0.75 for VGG-16) and Open-TMJ images (0.89 for both models) were significantly higher than the residents' AUCs (p < 0.01). The AUCs of the DL models on Open-TMJ images (0.89 for both models) were higher than the AUCs on Closed-TMJ images (0.72 for both models).Conclusions: The DL models created in this study could help residents to interpret Con-Pa and Open-TMJ images in the diagnosis of condylar OA.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"538-545"},"PeriodicalIF":1.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581652","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: Radiologic evaluation of associated symptoms and fractal analysis of unilateral dens invaginatus cases. 更正：相关症状的放射学评估和单侧颅内凹陷病例的骨折分析。

IF 1.6 3区医学

Oral Radiology Pub Date : 2024-10-01 DOI: 10.1007/s11282-024-00760-8

Sema Kaya, Alaettin Koc

引用次数: 0

Comparing the accuracy of two machine learning models in detection and classification of periapical lesions using periapical radiographs. 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 16.4 3区医学

Oral Radiology Pub Date : 2024-10-01 Epub Date: 2024-06-11 DOI: 10.1007/s11282-024-00759-1

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

{"title":"Comparing the accuracy of two machine learning models in detection and classification of periapical lesions using periapical radiographs.","authors":"Do Hoang Viet, Le Hoang Son, Do Ngoc Tuyen, Tran Manh Tuan, Nguyen Phu Thang, Vo Truong Nhu Ngoc","doi":"10.1007/s11282-024-00759-1","DOIUrl":"10.1007/s11282-024-00759-1","url":null,"abstract":"Background: Previous deep learning-based studies were mainly conducted on detecting periapical lesions; limited information in classification, such as the periapical index (PAI) scoring system, is available. The study aimed to apply two deep learning models, Faster R-CNN and YOLOv4, in detecting and classifying periapical lesions using the PAI score from periapical radiographs (PR) in three different regions of the dental arch: anterior teeth, premolars, and molars.Methods: Out of 2658 PR selected for the study, 2122 PR were used for training, 268 PR were used for validation and 268 PR were used for testing. The diagnosis made by experienced dentists was used as the reference diagnosis.Results: The Faster R-CNN and YOLOv4 models obtained great sensitivity, specificity, accuracy, and precision for detecting periapical lesions. No clear difference in the performance of both models among these three regions was found. The true prediction of Faster R-CNN was 89%, 83.01% and 91.84% for PAI 3, PAI 4 and PAI 5 lesions, respectively. The corresponding values of YOLOv4 were 68.06%, 50.94%, and 65.31%.Conclusions: Our study demonstrated the potential of YOLOv4 and Faster R-CNN models for detecting and classifying periapical lesions based on the PAI scoring system using periapical radiographs.","PeriodicalId":56103,"journal":{"name":"Oral Radiology","volume":" ","pages":"493-500"},"PeriodicalIF":16.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141307539","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