• Journal of Dental Rehabilitation and Applied Science
• /
• v.24 no.2
• /
• pp.213-230
• /
• 2008

The aim of this study was to analyze the initial movement and the stress distribution of each tooth and periodontal ligament during the lingual lever-arm retraction of 6 maxillary incisors using FEA. Two kinds of finite element models were produced: 2-properties model (simple model) and 24-properties model (multi model) according to the material property assignment. The subject was an adult male of 23 years old. The DICOM images through the CT of the patient were converted into the 3D image model of a skull using the Mimics (version 10.11, Materialise's interactive Medical Image Control System, Materialise, Belgium). After series of calculating, remeshing, exporting, importing process and volume mesh process was performed, FEA models were produced. FEA models are consisted of maxilla, maxillary central incisor, lateral incisor, canine, periodontal ligaments and lingual traction arm. The boundary conditions fixed the movements of posterior, sagittal and upper part of the model to the directions of X, Y, Z axis respectively. The model was set to be symmetrical to X axis. Through the center of resistance of maxilla complex, a retraction force of 200g was applied horizontally to the occlusal plane. Under this conditions, the initial movements and stress distributions were evaluated by 3D FEA. In the result, the amount of posterior movement was larger in the multi model than in the simple model as well as the amount of vertically rotation. The pattern of the posterior movement in the central incisors and lateral incisors was controlled tipping movement, and the amount was larger than in the canine. But the amount of root movement of the canine was larger than others. The incisor rotated downwardly and the canines upwardly around contact points of lateral incisor and canine in the both models. The values of stress are similar in the both simple and multi model.

• Progress in Medical Physics
• /
• v.27 no.2
• /
• pp.64-71
• /
• 2016

We develop a manufacture procedure for the production of a patient specific customized bolus (PSCB) using a 3D printer (3DP). The dosimetric accuracy of the 3D-PSCB is evaluated for electron beam therapy. In order to cover the required planning target volume (PTV), we select the proper electron beam energy and the field size through initial dose calculation using a treatment planning system. The PSCB is delineated based on the initial dose distribution. The dose calculation is repeated after applying the PSCB. We iteratively fine-tune the PSCB shape until the plan quality is sufficient to meet the required clinical criteria. Then the contour data of the PSCB is transferred to an in-house conversion software through the DICOMRT protocol. This contour data is converted into the 3DP data format, STereoLithography data format and then printed using a 3DP. Two virtual patients, having concave and convex shapes, were generated with a virtual PTV and an organ at risk (OAR). Then, two corresponding electron treatment plans with and without a PSCB were generated to evaluate the dosimetric effect of the PSCB. The dosimetric characteristics and dose volume histograms for the PTV and OAR are compared in both plans. Film dosimetry is performed to verify the dosimetric accuracy of the 3D-PSCB. The calculated planar dose distribution is compared to that measured using film dosimetry taken from the beam central axis. We compare the percent depth dose curve and gamma analysis (the dose difference is 3%, and the distance to agreement is 3 mm) results. No significant difference in the PTV dose is observed in the plan with the PSCB compared to that without the PSCB. The maximum, minimum, and mean doses of the OAR in the plan with the PSCB were significantly reduced by 9.7%, 36.6%, and 28.3%, respectively, compared to those in the plan without the PSCB. By applying the PSCB, the OAR volumes receiving 90% and 80% of the prescribed dose were reduced from $14.40cm^3$ to $0.1cm^3$ and from $42.6cm^3$ to $3.7cm^3$, respectively, in comparison to that without using the PSCB. The gamma pass rates of the concave and convex plans were 95% and 98%, respectively. A new procedure of the fabrication of a PSCB is developed using a 3DP. We confirm the usefulness and dosimetric accuracy of the 3D-PSCB for the clinical use. Thus, rapidly advancing 3DP technology is able to ease and expand clinical implementation of the PSCB.

• Tuberculosis and Respiratory Diseases
• /
• v.45 no.6
• /
• pp.1143-1153
• /
• 1998

Background : Recent outbreaks of pulmonary disease due to drug-resistant strains of Mycobacterium Tuberculosis have resulted in significant morbidity and mortality in patients worldwide. We reviewed our experience to evaluate the effects of pulmonary resection on the management of multidrug-resistant tuberculosis. Method : A retrospective review was performed of 41 patients undergoing pulmonary resection for multidrug-resistant tuberculosis between January 1993 and December 1997. We divided these into 3 groups according to the radiologic findings : (1) patients who have reasonably localized lesion (Localized Lesion Group ; LLG) (2) patients who have cavitary lesions after pulmonary resection on chest roentgenogram (Remained Cavity Group : RCG) (3) patients who have Remained infiltrative lesions postoperatively (Remained infiltrative group : RIG). We evaluated the negative conversion rate after resection and overall response rate of the groups. Then they were compared with the results of the chemotherapy on the multi drug-resistant tuberculosis which has been outcome by Goble et al. Goble et al reported that negative conversion rate was 65% and overall response rate, 56% over a mean period of 5.1 months. Results : Seventy five point six percent were men and 24.4% women with a median age of 31 years (range, 16 to 60 years). Although the patients were treated preoperatively with multidrug regimens in an effort to reduce the mycobacterial burden, 22 of 41 were still sputum culture positive at the time of surgery. 20 of 22 patients(90.9%, p<0.01) responded which is defined as negative sputum cultures within 2 months postoperative. Of 26 patients with the sufficient follow up data, 19 have Remained sputum culture negative for a mean duration of 25.7 months (73.1%, p<0.05). The bulk of the disease was manifest in one lung, but lesser amounts of contralateral disease were demonstrated in 15, consisted of 8 in RIG and 7 in RCG, of 41. 12 of 12 patients (100%, p<0.01) who were sputum positive at the time of surgery in LLG converted successfully. 14 of 15 patients (93.3%, p<0.05) with the follow up have completed treatment and not relapsed for a mean period of 25. 7 months. The mean length of postoperative drug therapy of LLG was 12.2 months. In RIG, postoperative negative conversion rate was 83.3% which was not significant statistically. There was a statistical significance in overall response rate (100%, p<0.05) of RIG for a mean period of 24.4 months with a mean length of postoperative chemotherapy, 11.8 months. In RCG a statistically lower overall response rate (14.3%, p<0.01) has been revealed for a mean duration of follow up, 24.2 months. A negative conversion rate of RCG was 75% which was not significant statistically. Conclusion : Surgery plays an important role in the management of patients with multidrug-resistant Mycobacterium tuberculosis infection. Aggressive pulmonary resection should be performed for resistant Mycobacterium tuberculosis infection to avoid treatment failure or relapse. Especially all cavitary lesions on preoperative chest roentgenogram should be resected completely. If all of them could not be resected perfectly, you should not open the thorax.

• Journal of Intelligence and Information Systems
• /
• v.21 no.2
• /
• pp.69-92
• /
• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.