Abstract
Digital images allow for the objective evaluation of facial appearance and abnormalities as well as treatment outcomes and stability. With the advancement of technology, manual clinical ...measurements can be replaced with fully automatic photographic assessments. However, obtaining millimetric measurements on photographs does not provide clinicians with their actual value due to different image magnification ratios. A deep learning tool was developed to estimate linear measurements on images with unknown magnification using the iris diameter. A framework was designed to segment the eyes’ iris and calculate the horizontal visible iris diameter (HVID) in pixels. A constant value of 12.2 mm was assigned as the HVID value in all the photographs. A vertical and a horizontal distance were measured in pixels on photographs of 94 subjects and were estimated in millimeters by calculating the magnification ratio using HVID. Manual measurement of the distances was conducted on the subjects and the actual and estimated amounts were compared using Bland–Altman analysis. The obtained error was calculated as mean absolute percentage error (MAPE) of 2.9% and 4.3% in horizontal and vertical measurements. Our study shows that due to the consistent size and narrow range of HVID values, the iris diameter can be used as a reliable scale to calibrate the magnification of the images to obtain precise measurements in further research.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Introduction: Crossbite is a common malocclusion with a 7-23% prevalence rate. Treatment is based on the expansion of the mid-palatal suture (MPS) with Rapid Palatal Expansion(RPE) followed by a ...retention period to reach new bone maturation, enough to maintain the results stable. This systematic review was conducted to evaluate the effectiveness of low-level laser therapy (LLLT) in increasing bone formation in MPS. Methods: This article was written by the PRISMA checklist. Electronically, 3 databases, namely PubMed, Scopus, and Embase, were searched with the keywords selected based on PICO. Time (2010-2021) and language restrictions were performed. Results: 528 articles, out of which 374 studies were screened, were found, and 9 full-text articles were subsequently included considering these inclusion criteria: randomized clinical trial (RCT) that examines the efficacy of LLLT in rapid palatal expansion (RPE), age under 15 years, non-surgical RPE with a tooth-supported appliance, and low-intensity laser application. Finally, 4 articles were appraised by Cochrane version 5.2.0 with 7 domains. 3 of 4 articles showed LLLT has a significant impact on bone formation. One of them showed no significant difference in pain perception and bone density between the laser and non-laser groups. Conclusion: While many studies have assessed the effect of LLLT on bone formation in animal models, high-quality clinical trials are missing in this regard. The available clinical trials suggest a positive effect of LLLT on sutural bone formation after RPE.
Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and implantological purposes. A systematic ...electronic search was conducted on four databases (Medline via PubMed, Google Scholar, Scopus, and Embase) and a repository (ArXiv) for publications after 2010, without any limitation on language. In the present review, we included studies that reported deep learning models' performance on periodontal or oral implantological tasks. Given the heterogeneities in the included studies, no meta‐analysis was performed. The risk of bias was assessed using the QUADAS‐2 tool. We included 47 studies: focusing on imaging data (n = 20) and non‐imaging data in periodontology (n = 12), or dental implantology (n = 15). The detection of periodontitis and gingivitis or periodontal bone loss, the classification of dental implant systems, or the prediction of treatment outcomes in periodontology and implantology were major use cases. The performance of the models was generally high. However, it varied given the employed methods (which includes various types of convolutional neural networks (CNN) and multi‐layered perceptron (MLP)), the variety in specific modeling tasks, as well as the chosen and reported outcomes, outcome measures and outcome level. Only a few studies (n = 7) showed a low risk of bias across all assessed domains. A growing number of studies evaluated DL for periodontal or implantological objectives. Heterogeneity in study design, poor reporting and a high risk of bias severely limit the comparability of studies and the robustness of the overall evidence.
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CMK, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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