{"title":"A Comparison of Smartphone-assisted and Computer Software Assisted Tracing with the Conventional Manual Method","authors":"Siddhartha Kastury, Pavan Kancherla, Sateesh Kumar Reddy, Srikrishna Chalasani","doi":"10.1177/03015742231214376","DOIUrl":"https://doi.org/10.1177/03015742231214376","url":null,"abstract":"","PeriodicalId":31847,"journal":{"name":"Journal of Indian Orthodontic Society","volume":"59 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139801115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-06DOI: 10.1177/03015742241226517
P. Chitra, Neeraj Kumar Dudy, Shubhnita Verma, Gyanda Mishra
Aim: To compare current DSLR and mirrorless camera systems for ease of use, efficiency, and cost in routine dental photography. Methods: Two currently available camera systems (DSLR + macro lens + ring flash/external flash and mirrorless camera + macro lens + ring flash/external flash) were compared and assessed for ease of use, sensor sizes, features, quality of imaging, battery capability, and costs. Results: Mirrorless cameras were smaller and lighter by 16% as compared to DSLRs. Superior Digic X image processors in mirrorless give better image quality compared to DSLRs with older Digic 8 processors. Focus points on the mirrorless are greater at 651 as compared to just 9 on DSLRs. Battery capacity with DSLRs is better at 600–800 shots per charge as compared to 250–300 shots per charge with mirrorless cameras. Overall, the mirrorless camera was priced 16% higher than DSLR cameras. Conclusion: There is a technological shift toward mirrorless camera systems across manufacturers. In the medium to long term, mirrorless technology will replace current DSLR systems making it imperative for dentists to understand and adapt to using mirrorless cameras in imaging.
{"title":"A Comparative Analysis of DSLR and Mirrorless Cameras for Dental Photography","authors":"P. Chitra, Neeraj Kumar Dudy, Shubhnita Verma, Gyanda Mishra","doi":"10.1177/03015742241226517","DOIUrl":"https://doi.org/10.1177/03015742241226517","url":null,"abstract":"Aim: To compare current DSLR and mirrorless camera systems for ease of use, efficiency, and cost in routine dental photography. Methods: Two currently available camera systems (DSLR + macro lens + ring flash/external flash and mirrorless camera + macro lens + ring flash/external flash) were compared and assessed for ease of use, sensor sizes, features, quality of imaging, battery capability, and costs. Results: Mirrorless cameras were smaller and lighter by 16% as compared to DSLRs. Superior Digic X image processors in mirrorless give better image quality compared to DSLRs with older Digic 8 processors. Focus points on the mirrorless are greater at 651 as compared to just 9 on DSLRs. Battery capacity with DSLRs is better at 600–800 shots per charge as compared to 250–300 shots per charge with mirrorless cameras. Overall, the mirrorless camera was priced 16% higher than DSLR cameras. Conclusion: There is a technological shift toward mirrorless camera systems across manufacturers. In the medium to long term, mirrorless technology will replace current DSLR systems making it imperative for dentists to understand and adapt to using mirrorless cameras in imaging.","PeriodicalId":31847,"journal":{"name":"Journal of Indian Orthodontic Society","volume":"119 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139802034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-06DOI: 10.1177/03015742241226517
P. Chitra, Neeraj Kumar Dudy, Shubhnita Verma, Gyanda Mishra
Aim: To compare current DSLR and mirrorless camera systems for ease of use, efficiency, and cost in routine dental photography. Methods: Two currently available camera systems (DSLR + macro lens + ring flash/external flash and mirrorless camera + macro lens + ring flash/external flash) were compared and assessed for ease of use, sensor sizes, features, quality of imaging, battery capability, and costs. Results: Mirrorless cameras were smaller and lighter by 16% as compared to DSLRs. Superior Digic X image processors in mirrorless give better image quality compared to DSLRs with older Digic 8 processors. Focus points on the mirrorless are greater at 651 as compared to just 9 on DSLRs. Battery capacity with DSLRs is better at 600–800 shots per charge as compared to 250–300 shots per charge with mirrorless cameras. Overall, the mirrorless camera was priced 16% higher than DSLR cameras. Conclusion: There is a technological shift toward mirrorless camera systems across manufacturers. In the medium to long term, mirrorless technology will replace current DSLR systems making it imperative for dentists to understand and adapt to using mirrorless cameras in imaging.
{"title":"A Comparative Analysis of DSLR and Mirrorless Cameras for Dental Photography","authors":"P. Chitra, Neeraj Kumar Dudy, Shubhnita Verma, Gyanda Mishra","doi":"10.1177/03015742241226517","DOIUrl":"https://doi.org/10.1177/03015742241226517","url":null,"abstract":"Aim: To compare current DSLR and mirrorless camera systems for ease of use, efficiency, and cost in routine dental photography. Methods: Two currently available camera systems (DSLR + macro lens + ring flash/external flash and mirrorless camera + macro lens + ring flash/external flash) were compared and assessed for ease of use, sensor sizes, features, quality of imaging, battery capability, and costs. Results: Mirrorless cameras were smaller and lighter by 16% as compared to DSLRs. Superior Digic X image processors in mirrorless give better image quality compared to DSLRs with older Digic 8 processors. Focus points on the mirrorless are greater at 651 as compared to just 9 on DSLRs. Battery capacity with DSLRs is better at 600–800 shots per charge as compared to 250–300 shots per charge with mirrorless cameras. Overall, the mirrorless camera was priced 16% higher than DSLR cameras. Conclusion: There is a technological shift toward mirrorless camera systems across manufacturers. In the medium to long term, mirrorless technology will replace current DSLR systems making it imperative for dentists to understand and adapt to using mirrorless cameras in imaging.","PeriodicalId":31847,"journal":{"name":"Journal of Indian Orthodontic Society","volume":"23 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139861952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-04DOI: 10.1177/03015742241221356
Shweta A. Kolhe, Suchita S. Daokar
Objective: This study aimed to assess the connection between tongue pressure, Modified Mallampati Score (MMS), BMI, and their role in evaluating obstructive sleep apnea (OSA). Materials and Methods: A total of 180 participants were categorized into four groups ( n = 45) based on the MMS. After securing informed consent, demographic data, including age, gender, body height, and weight (used to calculate BMI) were collected. A tongue pressure measurement system, patented as Innovative Australian Patent no. 2021106623 on 24 November 2021, was utilized. Results: The one-way analysis of variance test was employed to compare variations in average BMI and tongue pressure across the groups. The post hoc Tukey test revealed significant differences at p ≤ .05. Tongue pressure significantly varied among the distinct MMS categories ( p = .001), notably with group 4 displaying significantly lower tongue pressure compared to the other three groups. Conclusion: The findings suggest that both tongue pressure and MMS are interconnected factors contributing to OSA, while BMI and tongue pressure operate independently in determining OSA.
{"title":"Clinical Assessment of the Correlation Between Tongue Pressure, Modified Mallampati Score, BMI, and Obstructive Sleep Apnea","authors":"Shweta A. Kolhe, Suchita S. Daokar","doi":"10.1177/03015742241221356","DOIUrl":"https://doi.org/10.1177/03015742241221356","url":null,"abstract":"Objective: This study aimed to assess the connection between tongue pressure, Modified Mallampati Score (MMS), BMI, and their role in evaluating obstructive sleep apnea (OSA). Materials and Methods: A total of 180 participants were categorized into four groups ( n = 45) based on the MMS. After securing informed consent, demographic data, including age, gender, body height, and weight (used to calculate BMI) were collected. A tongue pressure measurement system, patented as Innovative Australian Patent no. 2021106623 on 24 November 2021, was utilized. Results: The one-way analysis of variance test was employed to compare variations in average BMI and tongue pressure across the groups. The post hoc Tukey test revealed significant differences at p ≤ .05. Tongue pressure significantly varied among the distinct MMS categories ( p = .001), notably with group 4 displaying significantly lower tongue pressure compared to the other three groups. Conclusion: The findings suggest that both tongue pressure and MMS are interconnected factors contributing to OSA, while BMI and tongue pressure operate independently in determining OSA.","PeriodicalId":31847,"journal":{"name":"Journal of Indian Orthodontic Society","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139807084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-04DOI: 10.1177/03015742241221356
Shweta A. Kolhe, Suchita S. Daokar
Objective: This study aimed to assess the connection between tongue pressure, Modified Mallampati Score (MMS), BMI, and their role in evaluating obstructive sleep apnea (OSA). Materials and Methods: A total of 180 participants were categorized into four groups ( n = 45) based on the MMS. After securing informed consent, demographic data, including age, gender, body height, and weight (used to calculate BMI) were collected. A tongue pressure measurement system, patented as Innovative Australian Patent no. 2021106623 on 24 November 2021, was utilized. Results: The one-way analysis of variance test was employed to compare variations in average BMI and tongue pressure across the groups. The post hoc Tukey test revealed significant differences at p ≤ .05. Tongue pressure significantly varied among the distinct MMS categories ( p = .001), notably with group 4 displaying significantly lower tongue pressure compared to the other three groups. Conclusion: The findings suggest that both tongue pressure and MMS are interconnected factors contributing to OSA, while BMI and tongue pressure operate independently in determining OSA.
{"title":"Clinical Assessment of the Correlation Between Tongue Pressure, Modified Mallampati Score, BMI, and Obstructive Sleep Apnea","authors":"Shweta A. Kolhe, Suchita S. Daokar","doi":"10.1177/03015742241221356","DOIUrl":"https://doi.org/10.1177/03015742241221356","url":null,"abstract":"Objective: This study aimed to assess the connection between tongue pressure, Modified Mallampati Score (MMS), BMI, and their role in evaluating obstructive sleep apnea (OSA). Materials and Methods: A total of 180 participants were categorized into four groups ( n = 45) based on the MMS. After securing informed consent, demographic data, including age, gender, body height, and weight (used to calculate BMI) were collected. A tongue pressure measurement system, patented as Innovative Australian Patent no. 2021106623 on 24 November 2021, was utilized. Results: The one-way analysis of variance test was employed to compare variations in average BMI and tongue pressure across the groups. The post hoc Tukey test revealed significant differences at p ≤ .05. Tongue pressure significantly varied among the distinct MMS categories ( p = .001), notably with group 4 displaying significantly lower tongue pressure compared to the other three groups. Conclusion: The findings suggest that both tongue pressure and MMS are interconnected factors contributing to OSA, while BMI and tongue pressure operate independently in determining OSA.","PeriodicalId":31847,"journal":{"name":"Journal of Indian Orthodontic Society","volume":"48 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139866840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-03DOI: 10.1177/03015742231219540
Jishnu S., Binnoy Kurian, Tony Michael
AI-based automated cephalometric landmark detection streamlines orthodontic diagnosis and treatment planning, providing accurate, efficient, and reliable results. Benefits include saving time, minimizing subjectivity, improving precision, and facilitating continuous improvement. However, they should complement clinician expertise, ensuring qualified orthodontists make the final diagnosis and treatment plan. To propose a method that automatically detects cephalometric landmarks on the X-ray images and compare these values with the manual annotation method. A dataset of 600 X-ray images, each containing 19 landmarks, was collected. Two orthodontists manually marked the 19 landmarks in 300 cephalograms and their coordinates were automatically extracted. The dataset was cleaned for errors, and a pre-trained CNN model with an EfficientNetB7 backbone was used for landmark detection. The model was trained on 80% of the dataset and tested on the remaining 20%. The two-step method included ROI extraction and landmark detection. The RMSE score was used to evaluate inter-examiner reliability and the R2 score was used to compare manual and automated models. Model landmark locations were compared to the manual method. The mean deviation of the predicted landmarks from the actual landmarks was calculated using RMSE, and the model showed acceptable accuracy compared to manual annotation. EfficientNetB7 was found to have detection accuracies similar to the manual annotation method. For landmarks like Porion, articulare, and soft tissue pogonion, the model outperformed the human annotation method and provides a consistent better result, and for points like Point A, pogonion, gnathion, and menton, the manual methods show more accurate results. The study introduced an automated approach using deep learning to predict landmark locations, and the results demonstrate its accuracy in comparison with the manual annotation method. This approach effectively detects cephalometric landmarks, suggesting its potential for clinical use with orthodontist’s supervision.
基于人工智能的自动头颅测量标志检测简化了正畸诊断和治疗计划，提供准确、高效和可靠的结果。其优点包括节省时间、减少主观性、提高精确度和促进持续改进。然而，它们应该与临床医生的专业知识相辅相成，确保由合格的正畸医生做出最终诊断和治疗计划。提出一种在 X 光图像上自动检测头颅测量地标的方法，并将这些值与手动标注法进行比较。收集了一个包含 600 张 X 光图像的数据集，每张图像包含 19 个地标。两名正畸医生在 300 张头影图上手动标注了 19 个地标，并自动提取了它们的坐标。对数据集进行错误清理后，使用预先训练好的以 EfficientNetB7 为骨干的 CNN 模型进行地标检测。该模型在 80% 的数据集上进行了训练，并在剩余的 20% 数据集上进行了测试。两步法包括 ROI 提取和地标检测。RMSE 分数用于评估检查者之间的可靠性，R2 分数用于比较手动和自动模型。将模型地标位置与手动方法进行比较。使用 RMSE 计算了预测地标与实际地标的平均偏差，与人工标注相比，模型显示出了可接受的准确性。EfficientNetB7 的检测精度与人工标注方法相似。对于 Porion、articulare 和软组织 pogonion 等地标，该模型的表现优于人工标注方法，并提供了一致的较好结果；而对于 A 点、pogonion、gnathion 和 menton 等点，人工方法则显示出更准确的结果。该研究引入了一种利用深度学习预测地标位置的自动化方法，结果表明，与人工标注方法相比，该方法的准确性更高。这种方法能有效地检测出头颌测量的地标，表明它有可能在正畸医生的指导下应用于临床。
{"title":"Automated Cephalometric Landmark Detection: A Novel Software Model Compared with Manual Annotation Method","authors":"Jishnu S., Binnoy Kurian, Tony Michael","doi":"10.1177/03015742231219540","DOIUrl":"https://doi.org/10.1177/03015742231219540","url":null,"abstract":"AI-based automated cephalometric landmark detection streamlines orthodontic diagnosis and treatment planning, providing accurate, efficient, and reliable results. Benefits include saving time, minimizing subjectivity, improving precision, and facilitating continuous improvement. However, they should complement clinician expertise, ensuring qualified orthodontists make the final diagnosis and treatment plan. To propose a method that automatically detects cephalometric landmarks on the X-ray images and compare these values with the manual annotation method. A dataset of 600 X-ray images, each containing 19 landmarks, was collected. Two orthodontists manually marked the 19 landmarks in 300 cephalograms and their coordinates were automatically extracted. The dataset was cleaned for errors, and a pre-trained CNN model with an EfficientNetB7 backbone was used for landmark detection. The model was trained on 80% of the dataset and tested on the remaining 20%. The two-step method included ROI extraction and landmark detection. The RMSE score was used to evaluate inter-examiner reliability and the R2 score was used to compare manual and automated models. Model landmark locations were compared to the manual method. The mean deviation of the predicted landmarks from the actual landmarks was calculated using RMSE, and the model showed acceptable accuracy compared to manual annotation. EfficientNetB7 was found to have detection accuracies similar to the manual annotation method. For landmarks like Porion, articulare, and soft tissue pogonion, the model outperformed the human annotation method and provides a consistent better result, and for points like Point A, pogonion, gnathion, and menton, the manual methods show more accurate results. The study introduced an automated approach using deep learning to predict landmark locations, and the results demonstrate its accuracy in comparison with the manual annotation method. This approach effectively detects cephalometric landmarks, suggesting its potential for clinical use with orthodontist’s supervision.","PeriodicalId":31847,"journal":{"name":"Journal of Indian Orthodontic Society","volume":"18 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139808167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-03DOI: 10.1177/03015742231219540
Jishnu S., Binnoy Kurian, Tony Michael
AI-based automated cephalometric landmark detection streamlines orthodontic diagnosis and treatment planning, providing accurate, efficient, and reliable results. Benefits include saving time, minimizing subjectivity, improving precision, and facilitating continuous improvement. However, they should complement clinician expertise, ensuring qualified orthodontists make the final diagnosis and treatment plan. To propose a method that automatically detects cephalometric landmarks on the X-ray images and compare these values with the manual annotation method. A dataset of 600 X-ray images, each containing 19 landmarks, was collected. Two orthodontists manually marked the 19 landmarks in 300 cephalograms and their coordinates were automatically extracted. The dataset was cleaned for errors, and a pre-trained CNN model with an EfficientNetB7 backbone was used for landmark detection. The model was trained on 80% of the dataset and tested on the remaining 20%. The two-step method included ROI extraction and landmark detection. The RMSE score was used to evaluate inter-examiner reliability and the R2 score was used to compare manual and automated models. Model landmark locations were compared to the manual method. The mean deviation of the predicted landmarks from the actual landmarks was calculated using RMSE, and the model showed acceptable accuracy compared to manual annotation. EfficientNetB7 was found to have detection accuracies similar to the manual annotation method. For landmarks like Porion, articulare, and soft tissue pogonion, the model outperformed the human annotation method and provides a consistent better result, and for points like Point A, pogonion, gnathion, and menton, the manual methods show more accurate results. The study introduced an automated approach using deep learning to predict landmark locations, and the results demonstrate its accuracy in comparison with the manual annotation method. This approach effectively detects cephalometric landmarks, suggesting its potential for clinical use with orthodontist’s supervision.
基于人工智能的自动头颅测量标志检测简化了正畸诊断和治疗计划，提供准确、高效和可靠的结果。其优点包括节省时间、减少主观性、提高精确度和促进持续改进。然而，它们应该与临床医生的专业知识相辅相成，确保由合格的正畸医生做出最终诊断和治疗计划。提出一种在 X 光图像上自动检测头颅测量地标的方法，并将这些值与手动标注法进行比较。收集了一个包含 600 张 X 光图像的数据集，每张图像包含 19 个地标。两名正畸医生在 300 张头影图上手动标注了 19 个地标，并自动提取了它们的坐标。对数据集进行错误清理后，使用预先训练好的以 EfficientNetB7 为骨干的 CNN 模型进行地标检测。该模型在 80% 的数据集上进行了训练，并在剩余的 20% 数据集上进行了测试。两步法包括 ROI 提取和地标检测。RMSE 分数用于评估检查者之间的可靠性，R2 分数用于比较手动和自动模型。将模型地标位置与手动方法进行比较。使用 RMSE 计算了预测地标与实际地标的平均偏差，与人工标注相比，模型显示出了可接受的准确性。EfficientNetB7 的检测精度与人工标注方法相似。对于 Porion、articulare 和软组织 pogonion 等地标，该模型的表现优于人工标注方法，并提供了一致的较好结果；而对于 A 点、pogonion、gnathion 和 menton 等点，人工方法则显示出更准确的结果。该研究引入了一种利用深度学习预测地标位置的自动化方法，结果表明，与人工标注方法相比，该方法的准确性更高。这种方法能有效地检测出头颌测量的地标，表明它有可能在正畸医生的指导下应用于临床。
{"title":"Automated Cephalometric Landmark Detection: A Novel Software Model Compared with Manual Annotation Method","authors":"Jishnu S., Binnoy Kurian, Tony Michael","doi":"10.1177/03015742231219540","DOIUrl":"https://doi.org/10.1177/03015742231219540","url":null,"abstract":"AI-based automated cephalometric landmark detection streamlines orthodontic diagnosis and treatment planning, providing accurate, efficient, and reliable results. Benefits include saving time, minimizing subjectivity, improving precision, and facilitating continuous improvement. However, they should complement clinician expertise, ensuring qualified orthodontists make the final diagnosis and treatment plan. To propose a method that automatically detects cephalometric landmarks on the X-ray images and compare these values with the manual annotation method. A dataset of 600 X-ray images, each containing 19 landmarks, was collected. Two orthodontists manually marked the 19 landmarks in 300 cephalograms and their coordinates were automatically extracted. The dataset was cleaned for errors, and a pre-trained CNN model with an EfficientNetB7 backbone was used for landmark detection. The model was trained on 80% of the dataset and tested on the remaining 20%. The two-step method included ROI extraction and landmark detection. The RMSE score was used to evaluate inter-examiner reliability and the R2 score was used to compare manual and automated models. Model landmark locations were compared to the manual method. The mean deviation of the predicted landmarks from the actual landmarks was calculated using RMSE, and the model showed acceptable accuracy compared to manual annotation. EfficientNetB7 was found to have detection accuracies similar to the manual annotation method. For landmarks like Porion, articulare, and soft tissue pogonion, the model outperformed the human annotation method and provides a consistent better result, and for points like Point A, pogonion, gnathion, and menton, the manual methods show more accurate results. The study introduced an automated approach using deep learning to predict landmark locations, and the results demonstrate its accuracy in comparison with the manual annotation method. This approach effectively detects cephalometric landmarks, suggesting its potential for clinical use with orthodontist’s supervision.","PeriodicalId":31847,"journal":{"name":"Journal of Indian Orthodontic Society","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139868074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-02DOI: 10.1177/03015742231221855
Aravind S. Raju, Sruti Gopinath, Ramyashree N.R., Renji K. Paul, H. Kaushik, S. Kamath
Bracket holding tweezer is used for bonding brackets in orthodontics and mouth mirror for indirect vision for checking the horizontal positioning of the brackets. In this article, bracket positioner has been modified with mouth mirror on the opposite side of the positioner to aid in precise positioning of the brackets both vertically and horizontally and to provide a proper view while bonding.
{"title":"Modified Bracket Holding Plier: BRAC-M","authors":"Aravind S. Raju, Sruti Gopinath, Ramyashree N.R., Renji K. Paul, H. Kaushik, S. Kamath","doi":"10.1177/03015742231221855","DOIUrl":"https://doi.org/10.1177/03015742231221855","url":null,"abstract":"Bracket holding tweezer is used for bonding brackets in orthodontics and mouth mirror for indirect vision for checking the horizontal positioning of the brackets. In this article, bracket positioner has been modified with mouth mirror on the opposite side of the positioner to aid in precise positioning of the brackets both vertically and horizontally and to provide a proper view while bonding.","PeriodicalId":31847,"journal":{"name":"Journal of Indian Orthodontic Society","volume":"82 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139683578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cone-beam computed tomography (CBCT) imaging and computer-aided manufacturing were used to produce stereolithographic trays for indirect-direct bonding. The ability to align teeth considering both the crown and the root decreases the chances for post treatment relapse. Three-dimensional (3D) images for separate brackets in a bracket kit were obtained from CBCT scanning in DICOM format and then converted to stereolithography format using Mimics software. Another CBCT image was obtained for the patients’ dentition. The images were saved in DICOM format and then placed into the Mimics image processing software. The images were enhanced, and the teeth were isolated to gain a clear view of their roots. With both images in the Mimics image-processing software, each bracket was placed on its designated tooth and positioned accurately. After the brackets were placed, a 3D image of a U-shaped stent was added to the project. Then, the image of the stent was placed over the teeth and half of the brackets. In the Mimics software, the teeth and brackets were then subtracted from the image of the tray to have a negative replica. The subtracted image of the tray in stereolithography format was printed with a 3D printer to obtain a 3D printed bracket positioning tray with indentations for bracket seating. This allowed brackets to be seated on the tray and bonded using conventional bonding steps.
锥形束计算机断层扫描（CBCT）成像和计算机辅助制造技术被用于生产用于间接-直接粘接的立体平版托盘。同时考虑牙冠和牙根的牙齿排列能力降低了治疗后复发的几率。通过 CBCT 扫描获得 DICOM 格式的托槽套件中不同托槽的三维（3D）图像，然后使用 Mimics 软件转换为立体光刻格式。另一张 CBCT 图像是为患者的牙齿采集的。图像以 DICOM 格式保存，然后放入 Mimics 图像处理软件。对图像进行了增强处理，并对牙齿进行了分离，以获得清晰的牙根视图。通过 Mimics 图像处理软件中的两幅图像，每个托槽都被放置到指定的牙齿上，并准确定位。支架放置完毕后，U 形支架的 3D 图像被添加到项目中。然后，支架的图像被放置在牙齿和一半支架上。然后在 Mimics 软件中，从托架图像中减去牙齿和托架，得到一个负像。用 3D 打印机将减去的托盘图像以立体光刻格式打印出来，得到一个 3D 打印托架定位托盘，托盘上有用于托架就位的凹痕。这样，托架就可以固定在托盘上，并使用传统的粘接步骤进行粘接。
{"title":"CBCT Imaging for Bracket Positioning with Consideration to Root Axes","authors":"Ajit J Kalia, Ashwith B Hegde, Sayali Bobade, Azmat Azha Khan","doi":"10.1177/03015742231222684","DOIUrl":"https://doi.org/10.1177/03015742231222684","url":null,"abstract":"Cone-beam computed tomography (CBCT) imaging and computer-aided manufacturing were used to produce stereolithographic trays for indirect-direct bonding. The ability to align teeth considering both the crown and the root decreases the chances for post treatment relapse. Three-dimensional (3D) images for separate brackets in a bracket kit were obtained from CBCT scanning in DICOM format and then converted to stereolithography format using Mimics software. Another CBCT image was obtained for the patients’ dentition. The images were saved in DICOM format and then placed into the Mimics image processing software. The images were enhanced, and the teeth were isolated to gain a clear view of their roots. With both images in the Mimics image-processing software, each bracket was placed on its designated tooth and positioned accurately. After the brackets were placed, a 3D image of a U-shaped stent was added to the project. Then, the image of the stent was placed over the teeth and half of the brackets. In the Mimics software, the teeth and brackets were then subtracted from the image of the tray to have a negative replica. The subtracted image of the tray in stereolithography format was printed with a 3D printer to obtain a 3D printed bracket positioning tray with indentations for bracket seating. This allowed brackets to be seated on the tray and bonded using conventional bonding steps.","PeriodicalId":31847,"journal":{"name":"Journal of Indian Orthodontic Society","volume":"109 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139615321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-18DOI: 10.1177/03015742231221864
Akshay Sunil Chaugule, Kunal Bhaskar Patil, S. Nagmode, H. Aphale, Vivek Jayaram Shinde, S. P. Surana
Introduction: Assessment of growth is crucial in the diagnosis and treatment planning in orthodontics. Skeletal development can be assessed by using hand-wrist radiographs and lateral cephalograms. The advantage of the panoramic radiograph over hand wrist radiograph is that patient exposure is reduced following the ALARA principle. Aim: The aim of this study was to investigate the correlation between third molar calcification stages and skeletal maturity using the CVM stages in North Maharashtrian population. Methods: Dental panoramic and lateral cephalograms of subjects ranged in age from 9 to 20 years were selected. Demirjians method was used to assess the dental maturation stages of third molars on both the sides. Hassel and Farman classification was used for classifying into cervical vertebral maturation indicator stages. The collected data were statistically analyzed. Result: There was a statistically significant correlation between cervical maturation stages and third molar calcification stages in North Maharashtrian population.
{"title":"To Evaluate Correlation Between Cervical Vertebral Maturation Stages and Third Molar Maturation Stages in North Maharashtrian Population","authors":"Akshay Sunil Chaugule, Kunal Bhaskar Patil, S. Nagmode, H. Aphale, Vivek Jayaram Shinde, S. P. Surana","doi":"10.1177/03015742231221864","DOIUrl":"https://doi.org/10.1177/03015742231221864","url":null,"abstract":"Introduction: Assessment of growth is crucial in the diagnosis and treatment planning in orthodontics. Skeletal development can be assessed by using hand-wrist radiographs and lateral cephalograms. The advantage of the panoramic radiograph over hand wrist radiograph is that patient exposure is reduced following the ALARA principle. Aim: The aim of this study was to investigate the correlation between third molar calcification stages and skeletal maturity using the CVM stages in North Maharashtrian population. Methods: Dental panoramic and lateral cephalograms of subjects ranged in age from 9 to 20 years were selected. Demirjians method was used to assess the dental maturation stages of third molars on both the sides. Hassel and Farman classification was used for classifying into cervical vertebral maturation indicator stages. The collected data were statistically analyzed. Result: There was a statistically significant correlation between cervical maturation stages and third molar calcification stages in North Maharashtrian population.","PeriodicalId":31847,"journal":{"name":"Journal of Indian Orthodontic Society","volume":"9 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139525854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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