Journal of Indian Orthodontic Society最新文献

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}