3 research outputs found
Children’s perspective on the right of self-determination
No full textThe present study was conducted to determine the thoughts of children, whose lives are governed by decisions taken by adults, on the right to self-determination. The study was conducted as a generic qualitative research, a qualitative research design. The study group included 16 children. The data were collected with semi-structured interviews conducted with children. Before the data collection process, the solo test game was played with children as ice breaker. After the solo test game, the modified version of the Cinderella fairy tale was read by the children and they were asked questions about the fairy tale. The objective of the modified Cinderella tale was to make children imagine a world where the decisions are taken by children. After the fairy tales, the semi-structured interview was conducted. The interviews were conducted upon the approval of the children to record the conversations, and the replies provided by children who did not provide approval for voice recordings were noted by the first author. The findings were analyzed with descriptive analysis. Based on the analysis results, the collected data was grouped in five categories that were organized under two themes. In conclusion, students stated that they were able to decide on daily matters such as selecting clothes, what to eat, however, they were presented with no choices in matters that could affect their lives such as school selection. Children stated that adults do not believe that children can make decisions on issues relevant to children’s lives. © 2017 Published by T& K Academic
Numbering teeth in panoramic images: A novel method based on deep learning and heuristic algorithm
No full textDental problems are one of the most common health problems for people. To detect and analyze these problems, dentists often use panoramic radiographs that show the entire mouth and have low radiation exposure and exposure time. Analyzing these radiographs is a lengthy and tedious process. Recent studies have ensured dental radiologists can perform the analyses faster with various artificial intelligence sup-ports. In this study, the numbering performance of Mask R-CNN and our heuristic algorithm-based method was verified on panoramic dental radiographs according to the Federation Dentaire Internationale (FDI) system. Ground-truth labelling of images required for training the deep learning algorithm was performed by two dental radiologists using the web-based labelling software DentiAssist created by the first author. The dataset was created from 2702 anonymized panoramic radio-graphs. The dataset is divided into 1747, 484, and 471 images, which serve as training, validation, and test sets. The dataset was validated using the k-fold cross-validation method (k = 5). A three-step heuristic algorithm was developed to improve the Mask R-CNN segmentation and numbering results. As far as we know, our study is the first in the literature to use a heuristic method in addition to traditional deep learning algorithms in detection, segmentation and numbering studies in panoramic radiography. The experimental results show that the mAp (@IOU = 0.5), precision, recall and f1 scores are 92.49%, 96.08%, 95.65% and 95.87%, respectively. The results of the learning-based algorithm were improved by more than 4%. In our research, we discovered that heuristic algorithms could improve the accuracy of deep learning-based algorithms. Our research will significantly reduce dental radiologists' workload, speed up diagnostic processes, and improve the accuracy of deep learning systems.(c) 2022 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Scientific and Technological Research Council of Turkey (TUBITAK) as part of the DentiAssist project [2200272]This study is funded by the Scientific and Technological Research Council of Turkey (TUBITAK) as part of the DentiAssist project numbered 2200272
