• Journal of Periodontal and Implant Science
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• 제52권3호
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• pp.220-229
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• 2022

Purpose: The aim of this study was to evaluate and compare the accuracy performance of dental professionals in the classification of different types of dental implant systems (DISs) using panoramic radiographic images with and without the assistance of a deep learning (DL) algorithm. Methods: Using a self-reported questionnaire, the classification accuracy of dental professionals (including 5 board-certified periodontists, 8 periodontology residents, and 31 dentists not specialized in implantology working at 3 dental hospitals) with and without the assistance of an automated DL algorithm were determined and compared. The accuracy, sensitivity, specificity, confusion matrix, receiver operating characteristic (ROC) curves, and area under the ROC curves were calculated to evaluate the classification performance of the DL algorithm and dental professionals. Results: Using the DL algorithm led to a statistically significant improvement in the average classification accuracy of DISs (mean accuracy: 78.88%) compared to that without the assistance of the DL algorithm (mean accuracy: 63.13%, P<0.05). In particular, when assisted by the DL algorithm, board-certified periodontists (mean accuracy: 88.56%) showed higher average accuracy than did the DL algorithm, and dentists not specialized in implantology (mean accuracy: 77.83%) showed the largest improvement, reaching an average accuracy similar to that of the algorithm (mean accuracy: 80.56%). Conclusions: The automated DL algorithm classified DISs with accuracy and performance comparable to those of board-certified periodontists, and it may be useful for dental professionals for the classification of various types of DISs encountered in clinical practice.

• Steel and Composite Structures
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• 제41권6호
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• pp.831-850
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• 2021

Surface subsidence caused by mining subsidence has an impact on neighboring structures and utilities. In other words, subsurface voids created by mining or tunneling activities induce soil movement, exposing buildings to physical and/or functional destruction. Soil-structure is evaluated employing probability distribution laws to account for their uncertainty and complexity to estimate structural vulnerability. In this study, to investigate the displacement field and surface settlement profile caused by mining subsidence, on the basis of a Winklersoil model, analytical equations for the moment-rotation response ofsoil during mining induced ground movements are developed. To define the full static moment-rotation response, an equation for the uplift-yield state is constructed and integrated with equations for the uplift- and yield-only conditions. The constructed model's findings reveal that the inverse of the factor of safety (x) has a considerable influence on the moment-rotation curve. The maximal moment-rotation response of the footing is defined by X = 0:6. Despite the use of Winkler model, the computed moment-rotation response results derived from the literature were analyzed through the ELM-SVM hybrid of Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Also, Monte Carlo simulations are used to apply continuous random parameters to assess the transmission of ground motions to structures. Following the findings of RMSE and R2, the results show that the choice of probabilistic laws of input parameters has a substantial impact on the outcome of analysis performed.

• Journal of Gastric Cancer
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• 제23권3호
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• pp.375-387
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• 2023

Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.

• Korean Journal of Radiology
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• 제24권2호
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• pp.155-165
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• 2023

Objective: Little is known about the effects of using different expert-determined reference standards when evaluating the performance of deep learning-based automatic detection (DLAD) models and their added value to radiologists. We assessed the concordance of expert-determined standards with a clinical gold standard (herein, pathological confirmation) and the effects of different expert-determined reference standards on the estimates of radiologists' diagnostic performance to detect malignant pulmonary nodules on chest radiographs with and without the assistance of a DLAD model. Materials and Methods: This study included chest radiographs from 50 patients with pathologically proven lung cancer and 50 controls. Five expert-determined standards were constructed using the interpretations of 10 experts: individual judgment by the most experienced expert, majority vote, consensus judgments of two and three experts, and a latent class analysis (LCA) model. In separate reader tests, additional 10 radiologists independently interpreted the radiographs and then assisted with the DLAD model. Their diagnostic performance was estimated using the clinical gold standard and various expert-determined standards as the reference standard, and the results were compared using the t test with Bonferroni correction. Results: The LCA model (sensitivity, 72.6%; specificity, 100%) was most similar to the clinical gold standard. When expert-determined standards were used, the sensitivities of radiologists and DLAD model alone were overestimated, and their specificities were underestimated (all p-values < 0.05). DLAD assistance diminished the overestimation of sensitivity but exaggerated the underestimation of specificity (all p-values < 0.001). The DLAD model improved sensitivity and specificity to a greater extent when using the clinical gold standard than when using the expert-determined standards (all p-values < 0.001), except for sensitivity with the LCA model (p = 0.094). Conclusion: The LCA model was most similar to the clinical gold standard for malignant pulmonary nodule detection on chest radiographs. Expert-determined standards caused bias in measuring the diagnostic performance of the artificial intelligence model.

• 디지털융복합연구
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• 제18권6호
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• pp.99-108
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• 2020

본 연구는 초급반 L2 쓰기 수업에서 번역기의 사용 현황을 조사하고, 그들이 응답한 번역기의 필요성, 신뢰도, 한계를 통해 그 타당성을 조명하고자 했다. 대학에서 초급 L2 수업을 수강한 117명의 학생이 설문조사에 참여했고, 그 가운데 11명은 추가적으로 실시된 심층면접에서 응답 내용을 설명했다. 수집된 자료에서 쓰기 수업에 활용된 번역기의 신뢰 정도, 효과, 사용 범위 등에 대한 다양한 관점들이 제시되었다. 응답자의 76.1%가 쓰기 활동에서 나름대로의 방법과 목적을 가지고 번역기를 사용하고 있었다. 그들은 번역기를 통해 부족한 영어 능력을 보완했고, 그 과정에서 수업참여의 동기와 자신감이 고취되었다고 설명했다. 반면에, 학생들은 부정확한 기계번역을 검토와 수정이라는 중요한 학습 과정을 생략한 채 그대로 옮겨 쓰게 된다면 학습 효과도 없을 뿐 아니라 표절행위가 될 수 있음을 지적했다. 그럼에도 불구하고 번역기는 이 시대에 새롭게 등장한 효율적인 학습 도구이며 효과적이고 완성도 있는 글쓰기를 위해 활용될 가치가 있는 것으로 나타났다.

• 한국도서관정보학회지
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• 제40권1호
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• pp.433-449
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• 2009

본 연구는 초 중등학교 교육현장에서 실시되고 있는 도서관활용수업이 어떠한 저해요인에 의해 활성화되지 않는지 원인을 분석하여 해소방안을 제시하기 위한 연구로 첫째, 도서관활용수업방법의 실증적 개선을 위한 평가 분석의 모형을 인간수행공학모형을 기반으로 재구성하였으며 둘째, 도서관활용수업에 관한 기존 연구의 이론적 토대를 기초로 실태를 파악하고 도서관활용수업의 바람직한 수행목표를 도출하였으며 셋째, 실제 연구대상학교를 중심으로 재구성된 인간수행 공학모형을 적용하여 도서관활용수업을 저해하는 요인을 도출하고 분석기법에 따라 수행요인을 분류하여 저해요인의 해소방안을 제시하였다. 연구의 결과 제시된 도서관활용수업 저해요인의 해소방안으로는 도서관활용수업에 대한 명확한 기대치 제공과 학습목표의 정확한 기술, 도서관활용수업 지원체제 구축, 교사들의 외적 동기 유발 시스템 개발, 다양한 도서관활용수업 프로그램 개발, 그리고 학교장과 일선 교사들의 도서관활용수업에 대한 인식전환 등으로 요약할 수 있으며 학교도서관매체센터가 도서관활용수업의 활성화로 인해 학교교육의 핵심적 역할을 수행할 수 있기를 기대한다.

• 한국과학교육학회지
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• 제13권2호
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• pp.230-246
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• 1993

The purpose of this study was to investigate the computer-naive children's preconceptions of computer concept, anxieties for computer, the changes in preconceptions and anxieties by computer literacy teaching, and the effect of CASI(Computer Assisted Science Instruction) on the science achievement. For this study, 42 5th graders were sampled. They were divided into two groups, experimental group(male:10, female:11) and control group(male:12, female:9). Each group was randomly assigned in the elementary school. Preconceptions about computer were examined by individual interview. Computer anxiety score was measured by questionaires. The questionaires developed in this study consisted of total 21 items measured by Chronbach ${\alpha}$ (0.93) and Total Item Correlationtp(p=0.01, r = $0.40{\sim}0.72$). Computer literacy curriculum based on children's preconceptions was developed and then was treated for experimental group as a computer literacy course. Preconceptions of computer, computer anxiety, and CASI achievements were compared between experimental group and control group in pre and post test. The results of this study are as follows; 1) children's preconceptions of computer showed various non-scientific concepts as animism and obvious visiual thinking. 2) children's misconceptions and anxieties about computer did not show significant differences in terms of learning experience of computer. 3) computer literacy had an effect on eliminating children's misconception about computer. 4) computer literacy had an effect on diminishing children's computer anxiety. 5) children's misconceptions and anxieties about computer showed significant inter-correlation. 6) children's misconceptions and anxieties about computer were appeared negative effect on CASI achievements. As the results, children's misconception and anxieties about computer had an effect on CASI acheivements. Therefore before performing CASI, more systematic computer literacy might be taught in formal education.

• Journal of Gastric Cancer
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• 제10권4호
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• pp.241-246
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• 2010

Purpose: Laparoscopy-assisted gastrectomy (LAG) has become a technically feasible and safe procedure for early gastric cancer treatment. LAG is being increasingly performed in many centers; however, there have been few reports regarding LAG at low-volume centers. The aim of this study was to report our early experience with LAG in patients with gastric cancer at a low-volume center. Materials and Methods: The clinicopathologic data and surgical outcomes of 39 patients who underwent LAG for gastric cancer between April 2007 and March 2010 were retrospectively reviewed. Results: The mean age was 68.3 years. Thirty-one patients had medical co-morbidities. The mean patient ASA score was 2.0. Among the 39 patients, 4 patients underwent total gastrectomy and 35 patients underwent distal gastrectomy. The mean blood loss was 145.4 ml and the mean operative time was 259.4 minutes. The mean time-to-first flatus, first oral intake, and the postoperative hospital stay was 2.8, 3.1, and 9.3 days, respectively. The 30-day mortality rate was 0%. Postoperative complications developed in 9 patients, as follows: anastomotic leakage, 1; wound infection, 1; gastric stasis, 2; postoperative ileus, 1; pneumonia, 1; cerebral infarction, 1; chronic renal failure, 1; and postoperative psychosis, 1. Conclusions: LAG is technically feasible and can be performed safely at a low-volume center, but an experienced surgical team and careful patient selection are necessary. Furthermore, for early mastery of the learning curve for LAG, surgeons need education and training in addition to an accumulation of cases.

• 대한방사선기술학회지:방사선기술과학
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• 제28권2호
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• pp.123-128
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• 2005

의료 방사선영상 정보에서 방사선사에게 숙련도와 능력을 토대로 진단 가치가 높은 영상을 얻고자 한다. 그래서 방사선영상학의 더욱더 향상된 지식과 더 많은 학습으로 인한 임상교육이 이루어지고, 각 방사선사의 교육수준을 고려하여 다양한 종류의 교육을 할 필요가 있다. 본 연구에서는 웹 환경에서 멀티미디어 저작도구를 사용한 임상 경추골절 방사선영상 컴퓨터 교육보조(Computer Assisted Instruction) 시스템을 구현하고자 한다. 컴퓨터 교육보조 시스템은 신규 방사선사의 교육프로그램으로써 방사선영상촬영실에서 수행되는 경추골절 방사선영상획득의 전반적인 업무내용을 교육하고자 한다. 제안한 CAI 시스템은 경추골절 환자의 방사선 촬영기술교본의 웹 정보화로 더욱더 신속하고 정확한 영상을 획득 할 수 있으며, 정확한 진단결정에 따른 환자 치료에 크게 도움이 되는 유용한 프로그램으로 기대된다.

• 대한기계학회논문집A
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• 제41권8호
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• pp.729-736
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• 2017

물리모델과 기계학습방법을 이용한 모델지원탐지확률(MAPOD, Model-assisted Probability of Detection) 실험계획법과 운용 중 결함이 발생한 부품을 사용하여 탐지확률을 측정하는 방법을 연구하였다. 검사방법은 와전류탐상검사를 적용하였고 검사대상은 볼트홀 표면에 존재하는 피로균열이다. 모델 지원탐지확률을 이용한 결과 실험요인이 큰 폭으로 감소하였다. 몬테카를로(Monte Carlo) 시뮬레이션을 이용하여 시편 균열길이 측정의 불확실성을 탐지확률에 반영함으로써 사용 중 결함품을 사용하여 비파괴검사정비사의 기량검증을 수행할 수 있었다.