Pub Date : 2025-02-04DOI: 10.1016/j.oooo.2024.11.051
Dr. Brandon Koroni , Dr. André Mol , Dr. Angela Broome , Dr. Pei Feng Lim
Objective
To quantify the amount of distortion of the mandibular condyle in panoramic radiographs when comparing standard and temporomandibular joint (TMJ) programs and different patient positions.
Study Design
Five dry cadaveric mandibles were scanned using standard panoramic and TMJ modes. Markers were placed on the medial (M), lateral (L), anterior (A), posterior (P), and superior (S) aspects of the condyles. The mandibles were imaged with the AT ProVecta 3D Prime in an ideal position, right and left turn as well as chin-up and chin-down. A cone beam computed tomography scan of each mandible was taken to establish true relationships between the markers. Reproducibility was tested by reacquiring the images of one mandible and re-measuring the markers of another.
Results
Distortion was present in all images, regardless of the program and the position of the mandible. Distortion was greatest between the medial and lateral poles in the vertical dimension. Most differences between panoramic and TMJ modes were not statistically significant. Reproducibility of the method was high.
Conclusion
The AT ProVecta 3D Prime produces distortion of the condyle that impacts the radiographic appearance in panoramic radiographs. The TMJ program did not meaningfully mitigate the distortion seen with the standard panoramic program.
{"title":"Quantifying condylar distortion in panoramic radiography","authors":"Dr. Brandon Koroni , Dr. André Mol , Dr. Angela Broome , Dr. Pei Feng Lim","doi":"10.1016/j.oooo.2024.11.051","DOIUrl":"10.1016/j.oooo.2024.11.051","url":null,"abstract":"<div><h3>Objective</h3><div>To quantify the amount of distortion of the mandibular condyle in panoramic radiographs when comparing standard and temporomandibular joint (TMJ) programs and different patient positions.</div></div><div><h3>Study Design</h3><div>Five dry cadaveric mandibles were scanned using standard panoramic and TMJ modes. Markers were placed on the medial (M), lateral (L), anterior (A), posterior (P), and superior (S) aspects of the condyles. The mandibles were imaged with the AT ProVecta 3D Prime in an ideal position, right and left turn as well as chin-up and chin-down. A cone beam computed tomography scan of each mandible was taken to establish true relationships between the markers. Reproducibility was tested by reacquiring the images of one mandible and re-measuring the markers of another.</div></div><div><h3>Results</h3><div>Distortion was present in all images, regardless of the program and the position of the mandible. Distortion was greatest between the medial and lateral poles in the vertical dimension. Most differences between panoramic and TMJ modes were not statistically significant. Reproducibility of the method was high.</div></div><div><h3>Conclusion</h3><div>The AT ProVecta 3D Prime produces distortion of the condyle that impacts the radiographic appearance in panoramic radiographs. The TMJ program did not meaningfully mitigate the distortion seen with the standard panoramic program.</div></div>","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":"139 3","pages":"Page e87"},"PeriodicalIF":2.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173757","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}
Pub Date : 2025-02-04DOI: 10.1016/j.oooo.2024.11.030
Dr. Austin Hughes , Dr. JP Castro , Dr. Sindhura Anamali
Clinical presentation
A 13-year-old male patient was referred to the pathology clinic at The University of Iowa, College of Dentistry to evaluate the first molars for root resorption. The patient has a reported history of Ehlers-Danlos syndrome. The clinical impression, upon examination was that of molar-incisor malformation (MIM). Radiographs were ordered to confirm the diagnosis. Radiographic evaluation of a cone beam computed tomography showed an altered morphology of the maxillary and mandibular right and left first molars wherein the teeth were smaller in size, had smaller and narrow pulp chambers, narrowed less defined canals, thinned enamel and noticeable cervical constriction. The radiographic findings were consistent with a diagnosis of MIM; however, the incisors have no significant morphologic variations in their development.
Differential Diagnosis
Dental anomalies have been reported in cases of Ehlers-Danlos syndrome; however, this particular appearance of the teeth has not been associated with the disorder previously. No other plausible conditions have this appearance localized to the first molars.
Diagnosis and Management
As the etiology of MIM is unclear and could be multifactorial, genetic, and histologic verification were not used in this case. Diagnosis was determined on the basis of medical history and clinical and radiographic examination. The patient had the permanent first molars extracted in spring of 2023 and is under orthodontic treatment to close the remaining spaces orthodontically.
Conclusion
Although MIM has been first described in the literature relatively recently, it is thought to involve epigenetic factors related to brain-related systemic diseases and root development. In this case study the only condition that was reported on was Ehlers-Danlos syndrome, and although there are some dental anomalies that can be contributed to the disorder, MIM does not appear to be one of them. This reflects how little is still known about the etiology of the condition and highlights the importance of continued research on the topic.
{"title":"Molar-incisor malformation: a case report","authors":"Dr. Austin Hughes , Dr. JP Castro , Dr. Sindhura Anamali","doi":"10.1016/j.oooo.2024.11.030","DOIUrl":"10.1016/j.oooo.2024.11.030","url":null,"abstract":"<div><h3>Clinical presentation</h3><div>A 13-year-old male patient was referred to the pathology clinic at The University of Iowa, College of Dentistry to evaluate the first molars for root resorption. The patient has a reported history of Ehlers-Danlos syndrome. The clinical impression, upon examination was that of molar-incisor malformation (MIM). Radiographs were ordered to confirm the diagnosis. Radiographic evaluation of a cone beam computed tomography showed an altered morphology of the maxillary and mandibular right and left first molars wherein the teeth were smaller in size, had smaller and narrow pulp chambers, narrowed less defined canals, thinned enamel and noticeable cervical constriction. The radiographic findings were consistent with a diagnosis of MIM; however, the incisors have no significant morphologic variations in their development.</div></div><div><h3>Differential Diagnosis</h3><div>Dental anomalies have been reported in cases of Ehlers-Danlos syndrome; however, this particular appearance of the teeth has not been associated with the disorder previously. No other plausible conditions have this appearance localized to the first molars.</div></div><div><h3>Diagnosis and Management</h3><div>As the etiology of MIM is unclear and could be multifactorial, genetic, and histologic verification were not used in this case. Diagnosis was determined on the basis of medical history and clinical and radiographic examination. The patient had the permanent first molars extracted in spring of 2023 and is under orthodontic treatment to close the remaining spaces orthodontically.</div></div><div><h3>Conclusion</h3><div>Although MIM has been first described in the literature relatively recently, it is thought to involve epigenetic factors related to brain-related systemic diseases and root development. In this case study the only condition that was reported on was Ehlers-Danlos syndrome, and although there are some dental anomalies that can be contributed to the disorder, MIM does not appear to be one of them. This reflects how little is still known about the etiology of the condition and highlights the importance of continued research on the topic.</div></div>","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":"139 3","pages":"Page e78"},"PeriodicalIF":2.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172697","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}
Pub Date : 2025-02-04DOI: 10.1016/j.oooo.2024.11.068
Dr. Rohan Jagtap , Dr. Prashant Jaju , Dr. Avula Samatha , Dr. Vidhi Shah , Dr. Sana Noor Siddiqui , Dr. Aniket Jadhav
Objective
The purpose of our study is to verify the diagnostic performance of an artificial intelligence (AI) system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on panoramic radiography.
Methodology
Panoramic radiographs were obtained from the EPIC and MiPacs systems of the University of Mississippi Medical Center, spanning from June 2022 to May 2023. A total of one thousand panoramic radiographs of adults were used to identify teeth, caries, implants, restorations, and fixed prostheses. The study included images from 580 patients. The identification and detection of teeth, caries, implants, restorations, and fixed prostheses were then independently determined by 2 oral and maxillofacial radiologists. The convolutional neural network−based architecture was analyzed for detecting panoramic findings. The artificial intelligence system (Velmeni Inc.) was used for analysis to determine whether the panoramic findings could be detected.
Results
The convolutional neural network system successfully detected teeth, caries, implants, restorations, and fixed prostheses on panoramic radiography. The AI system was able to detect findings in 567 out of a total of 580 panoramic radiographs, with a reliability of correctly detecting panoramic findings at 97.75%.
Conclusion
The detection of teeth and periapical pathosis performed by oral radiologists and by AI systems were comparable with each other. AI systems developed on the basis of on deep-learning methods can be useful for detecting teeth, caries, implants, restorations, and fixed prosthesis on panoramic images for clinical applications.
{"title":"Automatic feature segmentation in dental panoramic radiographs","authors":"Dr. Rohan Jagtap , Dr. Prashant Jaju , Dr. Avula Samatha , Dr. Vidhi Shah , Dr. Sana Noor Siddiqui , Dr. Aniket Jadhav","doi":"10.1016/j.oooo.2024.11.068","DOIUrl":"10.1016/j.oooo.2024.11.068","url":null,"abstract":"<div><h3>Objective</h3><div>The purpose of our study is to verify the diagnostic performance of an artificial intelligence (AI) system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on panoramic radiography.</div></div><div><h3>Methodology</h3><div>Panoramic radiographs were obtained from the EPIC and MiPacs systems of the University of Mississippi Medical Center, spanning from June 2022 to May 2023. A total of one thousand panoramic radiographs of adults were used to identify teeth, caries, implants, restorations, and fixed prostheses. The study included images from 580 patients. The identification and detection of teeth, caries, implants, restorations, and fixed prostheses were then independently determined by 2 oral and maxillofacial radiologists. The convolutional neural network−based architecture was analyzed for detecting panoramic findings. The artificial intelligence system (Velmeni Inc.) was used for analysis to determine whether the panoramic findings could be detected.</div></div><div><h3>Results</h3><div>The convolutional neural network system successfully detected teeth, caries, implants, restorations, and fixed prostheses on panoramic radiography. The AI system was able to detect findings in 567 out of a total of 580 panoramic radiographs, with a reliability of correctly detecting panoramic findings at 97.75%.</div></div><div><h3>Conclusion</h3><div>The detection of teeth and periapical pathosis performed by oral radiologists and by AI systems were comparable with each other. AI systems developed on the basis of on deep-learning methods can be useful for detecting teeth, caries, implants, restorations, and fixed prosthesis on panoramic images for clinical applications.</div></div>","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":"139 3","pages":"Pages e93-e94"},"PeriodicalIF":2.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172914","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}
Pub Date : 2025-02-04DOI: 10.1016/j.oooo.2024.11.020
Dr. Ghaidaa Badabaan , Dr. Dena Abderbwih , Dr. Anita Gohel
Objective
Central giant cell granuloma (CGCG) is a benign neoplasm of the jaw that can be locally aggressive. Investigating the detailed prevalence and radiographic features will broaden our understanding of this entity, further helping us narrow the diagnosis in ambiguous cases.
Study Design
This study retrospectively reviewed histologically proven CGCG cases from January 2011 to June 2023. This study was conducted in the Division of Oral and Maxillofacial Radiology, University of Florida College of Dentistry. Patients from both University of Florida Health Science Center and Shands Hospital were included in the data collection. Demographic data, clinical, radiographic features, surgical management and recurrence were evaluated and recorded.
Results
In the time interval, 16 cases with biopsy-proven CGCG cases were included. The mean age was 19.7 years, with a predilection for female (87%). 75% of the cases were in the mandible, with 85% of these cases seen in the posterior region. In total, 88% of the lesions were multilocular and root resorption was noted in 50% of the cases. Expansion and thinning of the cortices was seen in all, with effacement seen in 85% of the cases. Enucleation and curettage were the most commonly reported treatment. Recurrence was seen in one case, whereas a new lesion developed in a different location in one of the cases. There an increased association between the location of the lesion and the locularity. Posterior lesions were more likely to have multilocular appearance.
Conclusion
CGCG is a rare benign neoplasm that is primarily seen in young patients. It is more common in patients under 30 with a female predilection. Posterior lesions were more likely to be multilocular.
{"title":"Prevalence and imaging findings in central giant cell granuloma—retrospective study","authors":"Dr. Ghaidaa Badabaan , Dr. Dena Abderbwih , Dr. Anita Gohel","doi":"10.1016/j.oooo.2024.11.020","DOIUrl":"10.1016/j.oooo.2024.11.020","url":null,"abstract":"<div><h3>Objective</h3><div>Central giant cell granuloma (CGCG) is a benign neoplasm of the jaw that can be locally aggressive. Investigating the detailed prevalence and radiographic features will broaden our understanding of this entity, further helping us narrow the diagnosis in ambiguous cases.</div></div><div><h3>Study Design</h3><div>This study retrospectively reviewed histologically proven CGCG cases from January 2011 to June 2023. This study was conducted in the Division of Oral and Maxillofacial Radiology, University of Florida College of Dentistry. Patients from both University of Florida Health Science Center and Shands Hospital were included in the data collection. Demographic data, clinical, radiographic features, surgical management and recurrence were evaluated and recorded.</div></div><div><h3>Results</h3><div>In the time interval, 16 cases with biopsy-proven CGCG cases were included. The mean age was 19.7 years, with a predilection for female (87%). 75% of the cases were in the mandible, with 85% of these cases seen in the posterior region. In total, 88% of the lesions were multilocular and root resorption was noted in 50% of the cases. Expansion and thinning of the cortices was seen in all, with effacement seen in 85% of the cases. Enucleation and curettage were the most commonly reported treatment. Recurrence was seen in one case, whereas a new lesion developed in a different location in one of the cases. There an increased association between the location of the lesion and the locularity. Posterior lesions were more likely to have multilocular appearance.</div></div><div><h3>Conclusion</h3><div>CGCG is a rare benign neoplasm that is primarily seen in young patients. It is more common in patients under 30 with a female predilection. Posterior lesions were more likely to be multilocular.</div></div>","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":"139 3","pages":"Page e74"},"PeriodicalIF":2.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172940","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}
Pub Date : 2025-02-04DOI: 10.1016/j.oooo.2024.11.067
Dr. Rohan Jagtap , Dr. Aniket Jadhav , Dr. Avula Samatha , Dr. Sana Noor Siddiqui , Dr. Prashant Jaju
Aim
The aim of the study is to verify the success of an artificial intelligence model for the automatic airway segmentation on cone beam computed tomography (CBCT) images.
Materials and Methods
Three hundred CBCT images of adults were used in this study for airway assessment. The algorithm development was carried out using the Mask R-CNN ResNet 101 model. Both manual segmentation and an artificial intelligence (AI) system from Velmeni, Inc., were used for airway analysis. The airway analysis was determined by two oral and maxillofacial radiologists using Anatomage InVivo 3D software. Additionally, the convolutional neural network−based architecture was employed for airway volume detection. A comparison was made between the results obtained from the human observers and the artificial intelligence model.
Results
In evaluating the performance of the AI model for the segmentation of airway analysis, true positive, false positive, and false negative values were found to be 485, 18, and 23, respectively. Sensitivity, precision, and F1 score values were calculated as 0.9332, 0.9615, and 0.9766, respectively. The area under curve value was calculated as 0.8467.
Conclusion
The integration of the AI Mask R-CNN ResNet 101 model for airway analysis holds great promise in the decision support system for diagnostic accuracy, treatment planning, and overall treatment outcomes.
{"title":"Evaluation of artificial intelligence for airway analysis on cone beam computed tomography","authors":"Dr. Rohan Jagtap , Dr. Aniket Jadhav , Dr. Avula Samatha , Dr. Sana Noor Siddiqui , Dr. Prashant Jaju","doi":"10.1016/j.oooo.2024.11.067","DOIUrl":"10.1016/j.oooo.2024.11.067","url":null,"abstract":"<div><h3>Aim</h3><div>The aim of the study is to verify the success of an artificial intelligence model for the automatic airway segmentation on cone beam computed tomography (CBCT) images.</div></div><div><h3>Materials and Methods</h3><div>Three hundred CBCT images of adults were used in this study for airway assessment. The algorithm development was carried out using the Mask R-CNN ResNet 101 model. Both manual segmentation and an artificial intelligence (AI) system from Velmeni, Inc., were used for airway analysis. The airway analysis was determined by two oral and maxillofacial radiologists using Anatomage InVivo 3D software. Additionally, the convolutional neural network−based architecture was employed for airway volume detection. A comparison was made between the results obtained from the human observers and the artificial intelligence model.</div></div><div><h3>Results</h3><div>In evaluating the performance of the AI model for the segmentation of airway analysis, true positive, false positive, and false negative values were found to be 485, 18, and 23, respectively. Sensitivity, precision, and F1 score values were calculated as 0.9332, 0.9615, and 0.9766, respectively. The area under curve value was calculated as 0.8467.</div></div><div><h3>Conclusion</h3><div>The integration of the AI Mask R-CNN ResNet 101 model for airway analysis holds great promise in the decision support system for diagnostic accuracy, treatment planning, and overall treatment outcomes.</div></div>","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":"139 3","pages":"Page e93"},"PeriodicalIF":2.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172975","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}
Pub Date : 2025-02-04DOI: 10.1016/j.oooo.2024.11.064
Dr. Manal Hamdan , Mrs. Jennifer Bjork , Mrs. Reagan Saxe , Ms. Caroline Miller , Ms. Francesca Malensek , Ms. Rakhi Shah
Objective
To use a no-code computer vision platform (LandingLens) to develop, train, and evaluate an artificial intelligence model specifically designed for the detection of dental restorations on panoramic radiographs. No-code computer vision platforms, driven by deep learning neural networks, offer a versatile solution that effectively addresses challenges associated with the need for extensive machine learning expertise, expensive training costs, and operational proficiency.
Study Design
Institutional review board approval was obtained for this study. A convenient sampling method was employed to select one hundred panoramic radiographs from the AxiUm records of the dental school. Exclusion criteria were applied to ensure the selection of diagnostic radiographs. Accurate labeling of dental restorations was performed by calibrated dental faculty and students, with subsequent final review by a radiologist.
The radiographs were randomly split into training (70%), development (20%), and testing (10%) subgroups. The model was trained for 40 epochs using a medium model size. Data augmentation techniques such as horizontal flip and vertical flip were employed to enhance the training process.
Results
At a confidence threshold of 0.95, the model achieved a sensitivity of 86.64%, specificity of 99.78%, accuracy of 99.63%, and precision of 82.4%. These metrics indicate the model's ability to accurately detect dental restorations on a limited set of panoramic radiographs.
Conclusion
This study highlights the potential of no-code computer vision platforms in radiology. However, further research and validation are required to evaluate performance on larger and more diverse datasets, as well as for other detection tasks. Continued exploration of these platforms can contribute to advancements in dental imaging by democratizing computer vision development.
{"title":"Detection of dental restorations on panoramic radiographs using a no-code computer vision platform","authors":"Dr. Manal Hamdan , Mrs. Jennifer Bjork , Mrs. Reagan Saxe , Ms. Caroline Miller , Ms. Francesca Malensek , Ms. Rakhi Shah","doi":"10.1016/j.oooo.2024.11.064","DOIUrl":"10.1016/j.oooo.2024.11.064","url":null,"abstract":"<div><h3>Objective</h3><div>To use a no-code computer vision platform (LandingLens) to develop, train, and evaluate an artificial intelligence model specifically designed for the detection of dental restorations on panoramic radiographs. No-code computer vision platforms, driven by deep learning neural networks, offer a versatile solution that effectively addresses challenges associated with the need for extensive machine learning expertise, expensive training costs, and operational proficiency.</div></div><div><h3>Study Design</h3><div>Institutional review board approval was obtained for this study. A convenient sampling method was employed to select one hundred panoramic radiographs from the AxiUm records of the dental school. Exclusion criteria were applied to ensure the selection of diagnostic radiographs. Accurate labeling of dental restorations was performed by calibrated dental faculty and students, with subsequent final review by a radiologist.</div><div>The radiographs were randomly split into training (70%), development (20%), and testing (10%) subgroups. The model was trained for 40 epochs using a medium model size. Data augmentation techniques such as horizontal flip and vertical flip were employed to enhance the training process.</div></div><div><h3>Results</h3><div>At a confidence threshold of 0.95, the model achieved a sensitivity of 86.64%, specificity of 99.78%, accuracy of 99.63%, and precision of 82.4%. These metrics indicate the model's ability to accurately detect dental restorations on a limited set of panoramic radiographs.</div></div><div><h3>Conclusion</h3><div>This study highlights the potential of no-code computer vision platforms in radiology. However, further research and validation are required to evaluate performance on larger and more diverse datasets, as well as for other detection tasks. Continued exploration of these platforms can contribute to advancements in dental imaging by democratizing computer vision development.</div></div>","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":"139 3","pages":"Page e92"},"PeriodicalIF":2.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172978","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}
Pub Date : 2025-02-04DOI: 10.1016/j.oooo.2024.11.069
Dr. Ben Bartlett , Dr. Hassem Geha , Dr. Rujuta Katkar
The following examples reflect the importance of periodic imaging, review of radiographs, and knowing when to refer to an oral and maxillofacial radiologist for further evaluation. In the first case, a 26-year-old male dental student obtained a panoramic radiograph in September 2019, stating “I would like to get a pano since I have never had one.” The radiographic findings were reported by a general dentist as “No pathology present. Patient has no missing teeth.” Two years later, the patient, now the proud owner of a cone beam computed tomography (CBCT) machine, noticed changes in the left posterior maxillary area and had a CBCT acquired and sent the volume for a report. The area ended up being a plum-sized OKC that required extensive surgical intervention and follow-up. In the second case, A 79-year-old female patient complained of pain on chewing from a crown that was seated in November 2021, but clinical pain originated from #31. The patient developed a large fluctuant swelling in late December 2021/early January 2022 that had cervical lymph node involvement. #31 displayed bone loss on the PA and had 10 mm + pocketing, class III mobility, and heavy occlusal contact with #2. #31 was extracted on January 16, 2022 and a bovine graft was placed. Her discomfort somewhat resolved, but chewing discomfort returned 1/30/22. A PA taken February 4, 2022, revealed that bone loss has extended to #30. These periapical radiographs were never sent for review until February 2022, when a CBCT volume was acquired and sent for review. The findings were highly suggestive of an aggressive process such as gingival carcinoma invading the bone. The patient was the referring doctor's mother, and no follow-up information was provided due to the sensitive nature of the case.
{"title":"The importance of having conventional 2-dimensional radiographs reviewed by oral and maxillofacial radiologists","authors":"Dr. Ben Bartlett , Dr. Hassem Geha , Dr. Rujuta Katkar","doi":"10.1016/j.oooo.2024.11.069","DOIUrl":"10.1016/j.oooo.2024.11.069","url":null,"abstract":"<div><div>The following examples reflect the importance of periodic imaging, review of radiographs, and knowing when to refer to an oral and maxillofacial radiologist for further evaluation. In the first case, a 26-year-old male dental student obtained a panoramic radiograph in September 2019, stating “I would like to get a pano since I have never had one.” The radiographic findings were reported by a general dentist as “No pathology present. Patient has no missing teeth.” Two years later, the patient, now the proud owner of a cone beam computed tomography (CBCT) machine, noticed changes in the left posterior maxillary area and had a CBCT acquired and sent the volume for a report. The area ended up being a plum-sized OKC that required extensive surgical intervention and follow-up. In the second case, A 79-year-old female patient complained of pain on chewing from a crown that was seated in November 2021, but clinical pain originated from #31. The patient developed a large fluctuant swelling in late December 2021/early January 2022 that had cervical lymph node involvement. #31 displayed bone loss on the PA and had 10 mm + pocketing, class III mobility, and heavy occlusal contact with #2. #31 was extracted on January 16, 2022 and a bovine graft was placed. Her discomfort somewhat resolved, but chewing discomfort returned 1/30/22. A PA taken February 4, 2022, revealed that bone loss has extended to #30. These periapical radiographs were never sent for review until February 2022, when a CBCT volume was acquired and sent for review. The findings were highly suggestive of an aggressive process such as gingival carcinoma invading the bone. The patient was the referring doctor's mother, and no follow-up information was provided due to the sensitive nature of the case.</div></div>","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":"139 3","pages":"Page e94"},"PeriodicalIF":2.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173225","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}
Pub Date : 2025-02-04DOI: 10.1016/j.oooo.2024.11.044
Dr. Srinitha Singanamala , Dr. Suresh Mukherji , Dr. Mehrnaz Tahmasbi , Dr. Madhu Nair
Clinical Presentation
Venolymphatic malformation in the buccal space is relatively rare. A 27-year-old male with a soft, compressible mass in the right posterior maxilla causing facial asymmetry and gingival bleeding underwent pre- and post-contrast magnetic resonance imaging (MRI), revealing a heterogeneous but predominantly cystic lesion involving the right buccal space, extending superficially as also deeply to involve the buccinator and the anterior masseter, and inferiorly to the buccal space and inferolateral aspect of the right pterygomaxillary fissure. No osteolysis was identified. Slightly increased signal on T1 is noted from proteinaceous material or blood products as some areas show fluid levels. It is more conspicuous on the contrast-enhanced T1-weighted images with fat suppression. It appears to have primarily high T2 signal. There are focal areas of decreased T1 and T2 signal that are non-enhancing and may represent calcified phleboliths. No evidence of dilated vascular structures is identified suggesting a high-flow lesion.
Differential Diagnosis
Includes capillary, venous, lymphatic, and arteriovenous vascular lesions, hemangioma. For phleboliths: calcified lymph nodes, atherosclerosis, sialoliths, cysticercosis, miliary skin osteomas etc.
Diagnosis and Management
The diagnosis was confirmed via clinical evaluation, MRI, and aspiration. Findings were consistent with a low-flow, venolymphatic malformation involving the right buccal space and right pterygomaxillary fissure. The lesion was embolized and resected.
Conclusion
Although diagnosis of most cases of venolymphatic malformation may not be difficult, it can be challenging if superimposed with trauma, hemorrhage, or infection. Risk of severe hemorrhage exists if extraction in the region is performed. Dynamic contrast-enhanced MRI has greater specificity of diagnosis in that it can differentiate between low-and high-flow lesions. Management of vascular malformations is based on the lesion's vascular anatomy, anatomical location, and involvement with surrounding structures. Non-invasive intervention includes endovascular embolization, sclerotherapy and laser therapy.
{"title":"Venolymphatic malformation in the buccal space: magnetic resonance imaging features","authors":"Dr. Srinitha Singanamala , Dr. Suresh Mukherji , Dr. Mehrnaz Tahmasbi , Dr. Madhu Nair","doi":"10.1016/j.oooo.2024.11.044","DOIUrl":"10.1016/j.oooo.2024.11.044","url":null,"abstract":"<div><h3>Clinical Presentation</h3><div>Venolymphatic malformation in the buccal space is relatively rare. A 27-year-old male with a soft, compressible mass in the right posterior maxilla causing facial asymmetry and gingival bleeding underwent pre- and post-contrast magnetic resonance imaging (MRI), revealing a heterogeneous but predominantly cystic lesion involving the right buccal space, extending superficially as also deeply to involve the buccinator and the anterior masseter, and inferiorly to the buccal space and inferolateral aspect of the right pterygomaxillary fissure. No osteolysis was identified. Slightly increased signal on T1 is noted from proteinaceous material or blood products as some areas show fluid levels. It is more conspicuous on the contrast-enhanced T1-weighted images with fat suppression. It appears to have primarily high T2 signal. There are focal areas of decreased T1 and T2 signal that are non-enhancing and may represent calcified phleboliths. No evidence of dilated vascular structures is identified suggesting a high-flow lesion.</div></div><div><h3>Differential Diagnosis</h3><div>Includes capillary, venous, lymphatic, and arteriovenous vascular lesions, hemangioma. For phleboliths: calcified lymph nodes, atherosclerosis, sialoliths, cysticercosis, miliary skin osteomas etc.</div></div><div><h3>Diagnosis and Management</h3><div>The diagnosis was confirmed via clinical evaluation, MRI, and aspiration. Findings were consistent with a low-flow, venolymphatic malformation involving the right buccal space and right pterygomaxillary fissure. The lesion was embolized and resected.</div></div><div><h3>Conclusion</h3><div>Although diagnosis of most cases of venolymphatic malformation may not be difficult, it can be challenging if superimposed with trauma, hemorrhage, or infection. Risk of severe hemorrhage exists if extraction in the region is performed. Dynamic contrast-enhanced MRI has greater specificity of diagnosis in that it can differentiate between low-and high-flow lesions. Management of vascular malformations is based on the lesion's vascular anatomy, anatomical location, and involvement with surrounding structures. Non-invasive intervention includes endovascular embolization, sclerotherapy and laser therapy.</div></div>","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":"139 3","pages":"Page e84"},"PeriodicalIF":2.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173676","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}
Pub Date : 2025-02-04DOI: 10.1016/j.oooo.2024.11.037
Dr. Hoda Rahimi , Dr. Zahra Mohammadi , Dr. Shahryar Shahab , Dr. Mohammad Javad Kharazifard , Dr. Ali Kavosi , Dr. Zeinab Azizi
Objective
Considering the genetic and physiological status of different geographical areas, different age estimation methods may produce different results. This study aimed to compare the 2 dental age (DA) estimation methods of Cameriere and Demirjian among 6- to 14-year-old children in Tehran in 2017-2018.
Study Design
This cross-sectional analytical study was conducted on 306 panoramic images from 153 girls and 153 boys. The DA of participants was estimated by Cameriere's and Demirjian's methods. The data were statistically analyzed by the paired sample t-test, repeated measures analysis of variance, and the independent t test. Finally, a formula suitable for Iranian society was developed on the basis of the results of regression analysis.
Results
The mean age estimation error was +0.89 years for Demirjian's method (+0.86 in boys and +0.93 in girls) and −0.20 years for Cameriere's method (−0.20 in boys and −0.10 in girls). There was a significant difference between the DA calculated by Cameriere's and Demirjian's methods and the chronological age. There was no significant difference between Cameriere's and Demirjian's methods in this regard. The formula developed in this study could estimate the age of participants with an accuracy of above +0.008 (+0.009 in boys and +0.006 in girls). However, the results indicated no significant difference between the proposed formula and Cameriere's method in the accuracy of age estimation.
Conclusion
The accuracy of Cameriere's method was greater than that of Demirjian's method, but the formula proposed for Iranian society was more accurate than both of them. The Cameriere method underestimated and the Demirjian method overestimated the age.
{"title":"Dental age assessment using Demirjian and Cameriere's methods in an Iranian population","authors":"Dr. Hoda Rahimi , Dr. Zahra Mohammadi , Dr. Shahryar Shahab , Dr. Mohammad Javad Kharazifard , Dr. Ali Kavosi , Dr. Zeinab Azizi","doi":"10.1016/j.oooo.2024.11.037","DOIUrl":"10.1016/j.oooo.2024.11.037","url":null,"abstract":"<div><h3>Objective</h3><div>Considering the genetic and physiological status of different geographical areas, different age estimation methods may produce different results. This study aimed to compare the 2 dental age (DA) estimation methods of Cameriere and Demirjian among 6- to 14-year-old children in Tehran in 2017-2018.</div></div><div><h3>Study Design</h3><div>This cross-sectional analytical study was conducted on 306 panoramic images from 153 girls and 153 boys. The DA of participants was estimated by Cameriere's and Demirjian's methods. The data were statistically analyzed by the paired sample <em>t</em>-test, repeated measures analysis of variance, and the independent <em>t</em> test. Finally, a formula suitable for Iranian society was developed on the basis of the results of regression analysis.</div></div><div><h3>Results</h3><div>The mean age estimation error was +0.89 years for Demirjian's method (+0.86 in boys and +0.93 in girls) and −0.20 years for Cameriere's method (−0.20 in boys and −0.10 in girls). There was a significant difference between the DA calculated by Cameriere's and Demirjian's methods and the chronological age. There was no significant difference between Cameriere's and Demirjian's methods in this regard. The formula developed in this study could estimate the age of participants with an accuracy of above +0.008 (+0.009 in boys and +0.006 in girls). However, the results indicated no significant difference between the proposed formula and Cameriere's method in the accuracy of age estimation.</div></div><div><h3>Conclusion</h3><div>The accuracy of Cameriere's method was greater than that of Demirjian's method, but the formula proposed for Iranian society was more accurate than both of them. The Cameriere method underestimated and the Demirjian method overestimated the age.</div></div>","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":"139 3","pages":"Page e81"},"PeriodicalIF":2.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173750","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}
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