• Korean Journal of Radiology
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• v.22 no.6
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• pp.912-921
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• 2021

Objective: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists. Materials and Methods: This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured. Results: A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001). Conclusion: DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool.

• Korean Journal of Radiology
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• v.24 no.3
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• pp.259-270
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• 2023

Objective: It is unknown whether artificial intelligence-based computer-aided detection (AI-CAD) can enhance the accuracy of chest radiograph (CR) interpretation in real-world clinical practice. We aimed to compare the accuracy of CR interpretation assisted by AI-CAD to that of conventional interpretation in patients who presented to the emergency department (ED) with acute respiratory symptoms using a pragmatic randomized controlled trial. Materials and Methods: Patients who underwent CRs for acute respiratory symptoms at the ED of a tertiary referral institution were randomly assigned to intervention group (with assistance from an AI-CAD for CR interpretation) or control group (without AI assistance). Using a commercial AI-CAD system (Lunit INSIGHT CXR, version 2.0.2.0; Lunit Inc.). Other clinical practices were consistent with standard procedures. Sensitivity and false-positive rates of CR interpretation by duty trainee radiologists for identifying acute thoracic diseases were the primary and secondary outcomes, respectively. The reference standards for acute thoracic disease were established based on a review of the patient's medical record at least 30 days after the ED visit. Results: We randomly assigned 3576 participants to either the intervention group (1761 participants; mean age ± standard deviation, 65 ± 17 years; 978 males; acute thoracic disease in 472 participants) or the control group (1815 participants; 64 ± 17 years; 988 males; acute thoracic disease in 491 participants). The sensitivity (67.2% [317/472] in the intervention group vs. 66.0% [324/491] in the control group; odds ratio, 1.02 [95% confidence interval, 0.70-1.49]; P = 0.917) and false-positive rate (19.3% [249/1289] vs. 18.5% [245/1324]; odds ratio, 1.00 [95% confidence interval, 0.79-1.26]; P = 0.985) of CR interpretation by duty radiologists were not associated with the use of AI-CAD. Conclusion: AI-CAD did not improve the sensitivity and false-positive rate of CR interpretation for diagnosing acute thoracic disease in patients with acute respiratory symptoms who presented to the ED.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.29-34
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• 2019

If it takes much time to calculate the fitness of the solution in genetic algorithms, it is essential to create a metamodel. Much research has been completed to improve the performance of metamodels. In this study, we tried to get a better performance of metamotel using discrete Walsh transform in discrete domain. We transforms the basis of the solution and creates a metamodel using the transformed solution. We experimented with NK-landscape, a representative function of the pseudo-boolean function, and provided empirical evidence on the performance of the proposed model. When we performed the genetic algorithm using the proposed model, we confirmed that the genetic algorithm found a better solution. In particular, our metamodel showed better performance than that using the radial basis function network that modified the similarity function for the discrete domain.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.240-252
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• 2023

Purpose To assess the effect of deep learning image reconstruction (DLIR) for head CT in pediatric patients. Materials and Methods We collected 126 pediatric head CT images, which were reconstructed using filtered back projection, iterative reconstruction using adaptive statistical iterative reconstruction (ASiR)-V, and all three levels of DLIR (TrueFidelity; GE Healthcare). Each image set group was divided into four subgroups according to the patients' ages. Clinical and dose-related data were reviewed. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and qualitative parameters, including noise, gray matter-white matter (GM-WM) differentiation, sharpness, artifact, acceptability, and unfamiliar texture change were evaluated and compared. Results The SNR and CNR of each level in each age group increased among strength levels of DLIR. High-level DLIR showed a significantly improved SNR and CNR (p < 0.05). Sequential reduction of noise, improvement of GM-WM differentiation, and improvement of sharpness was noted among strength levels of DLIR. Those of high-level DLIR showed a similar value as that with ASiR-V. Artifact and acceptability did not show a significant difference among the adapted levels of DLIR. Conclusion Adaptation of high-level DLIR for the pediatric head CT can significantly reduce image noise. Modification is needed while processing artifacts.

• Journal of Korean Public Health Nursing
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• v.19 no.1
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• pp.74-94
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• 2005

The purpose of this study was to compare the effect of education between CAI(Computer Assisted Instruction) and lectures for smoking cessation among male students who attended vocational high schools. Conducted from February 24th to April 26th, 2003, the study design was quasi-experimental with nonequivalent control group pretest-posttest design. The study subjects were 60 male students in K vocational high school in Daegu city, who were present smokers and had more than 7.0 ppm concentration level of carbon monoxide. Thirty students were randomly chosen as the experimental group which applied CAI education method for smoking cessation. The other 30 students served as the control group which received lecture education method of 40 minutes on four consecutive days. CAI education for smoking cessation was composed of ready-made individual learning contents, counseling by using cyber-communication, writing a letter to stop smoking, and writing a written agreement for smoking cessation. Lecture education for smoking cessation was composed of a ready-prepared lecture for the group, writing a letter to stop smoking, and writing a written agreement for smoking cessation. To measure smoking related knowledge, Jeong Ree Roh(1996)'s smoking related knowledge scale$(Cronbach's\;{\alpha}=0.84)$ was modified and used by the researcher. To measure smoking related attitude, Jeong Ree Roh(1996)'s smoking related attitude scale$(Cronbach's\;{\alpha}=0.91)$ was modified and used by the researcher. Smoking related knowledge scale's Cronbach's $\alpha$ was 0.83 in the pilot study and 0.93 in this study. Smoking related attitude scale's Cronbach's a was 0.80 in the pilot study and 0.98 in this study. To determine the smoking amount, the number of cigarettes smoked per day was checked. The concentration level of CO in the exhaled breath was measured (Micro CO Cat. No. MCO2, UK). Data was analyzed by $x^2-test$, t-test, repeated measures ANOVA. simple main effects, and time contrast test with SPSS/Win 11.0 program. The results of this study were as follows: 1. The first hypothesis. that 'Smoking-related knowledge score in the experimental group by using CAI education for smoking cessation will be higher than that in the control group by using lecture education for smoking cessation', was not supported. 2. The second hypothesis, that 'Smoking-related attitude in the experimental group by using CAI education for smoking cessation will be higher than that in the control group by using lecture education for smoking cessation'. was supported(F=6490.79. p=0.000). 3. The third hypothesis. that 'Smoking amount in the experimental group by using CAI education for smoking cessation will be less than that in the control group by using lecture education for smoking cessation'. was supported. 1) The third-1st sub-hypothesis. that 'The number of cigarettes smoked per day in the experimental group by using CAI education for smoking cessation will be less than that in the control group by using lecture education for smoking cessation'. was supported(F=134.19. p=0.000). 2) The third-2nd sub-hypothesis. that 'The concentration level of CO by ppm per one exhaled breath in the experimental group by using CAI education for smoking cessation will be lower than that in the control group by using lecture education for smoking cessation"' was supported(F=268.55. p=0.000). From the above results. CAI education can be an effective intervention to improve smoking-related knowledge and attitude. and to reduce the number of cigarettes smoked per day and the concentration level of CO by ppm per one exhaled breath. Lecture education can be effective to improve smoking-related knowledge. In the future, when CAI education and lecture education for smoking cessation are applied on the school nursing field. the students can gain a comprehensive understanding of smoking cessation, changes in smoking-related knowledge. smoking-related attitude and reducing smoking amount. Furthermore, CAI education for smoking cessation could be developed as an individual self initiative program and could give a guideline to apply CAI education for smoking cessation in other field.

• Korean Journal of Radiology
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• v.22 no.1
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• pp.131-138
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• 2021

Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). Materials and Methods: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. Results: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). Conclusion: DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

• Health Policy and Management
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• v.24 no.2
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• pp.164-171
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• 2014

Background: Safety of drugs has become a major issue in public healthcare. Spontaneous reporting of adverse drug reaction (ADR) is the cornerstone in management of drug safety. We aimed to investigate the awareness and knowledge of spontaneous ADR reporting in general public of Korea. Methods: A total of 1,500 study subjects aged 19-69 years were interviewed with a questionnaire for their awareness and knowledge related to spontaneous ADR reporting. Computer assisted telephone interview was performed from 27th February 2013 to 4th March 2013. Target population was selected with quota sampling, using age, sex, and residence area. Healthcare professionals such as physicians, pharmacists, and nurses were excluded. The survey questions included awareness of spontaneous ADR reporting, opinions on ways to activate ADR reporting, and sociodemographic characteristics. Results: Overall awareness of spontaneous ADR reporting system was 8.3% (${\pm}2.53%$) among general population of Korea. Major source from which people got the information regarding ADR reporting was television/radio (69.9%), followed by internet (19.3%), and poster/brochure (6.1%). Awareness level differed between age groups (p<0.0001) and education levels (p<0.0001). Upon learning about the ADR reporting system, 88.5% of study subjects agreed on the necessity of ADR reporting system, while 46.6% thought promotion through internet and mass media as an effective way to activate ADR reporting. Conclusion: The overall awareness of spontaneous ADR reporting should be enhanced in order to establish a firm national system for drug safety. Adequate promotions should be performed targeting lower awareness groups, as well as various publicity activities via effective channels for the general population.

• Journal of The Korean Association of Information Education
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• v.1 no.1
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• pp.92-101
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• 1997

There has been increasing demand of hypermedia as computing and educational environment change. Most authoring tools developed recently have various built-in functions., but they are not enough to create hypermedia program or easy to learn. Even though teachers might try to develop hypermedia program with existing authoring tools, they usually come to face with the difficulties of creating new nodes, adding new data, or keeping user's activity records. Therefore, this study investigated to present a prototype of Educational Hypermedia Editor that can be conveniently used in hypermedia programs. According to the theories of courseware design, instructional design, learning theory, and hypertext and hyperemia, design strategies for the study were selected. Based on the analysis of the characteristics of domestic authoring tools as well as foreign authoring tools, the Educational Hypermedia Editor that has authoring and executing mode was designed and developed. Because the Educational Hypermedia Editor was designed for information retrieval CAI(Computer Assisted Instruction) materials, it is expected that the Educational Hypermedia Editor will be extensively used in the urea of social science classes, investigation studies, and information processing studies.

• Journal of KIISE:Software and Applications
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• v.37 no.2
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• pp.129-136
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• 2010

In this paper, we present an Example-based Dialogue System for English conversation tutoring. It aims to provide intelligent one-to-one English conversation tutoring instead of old fashioned language education with static multimedia materials. This system can understand poor expressions of students and it enables green hands to engage in a dialogue in spite of their poor linguistic ability, which gives students interesting motivation to learn a foreign language. And this system also has educational functionalities to improve the linguistic ability. To achieve these goals, we have developed a statistical natural language understanding module for understanding poor expressions and an example-based dialogue manager with high domain scalability and several effective tutoring methods.

• Progress in Medical Physics
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• v.30 no.4
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• pp.94-103
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• 2019

Purpose: To evaluate the clinical feasibility of knowledge-based planning (KBP) for volumetric-modulated arc radiotherapy (VMAT) in spine stereotactic body radiotherapy (SBRT). Methods: Forty-eight VMAT plans for spine SBRT was studied. Two planning target volumes (PTVs) were defined for simultaneous integrated boost: PTV for boost (PTV-B: 27 Gy/3fractions) and PTV elective (PTV-E: 24 Gy/3fractions). The expert VMAT plans were manually generated by experienced planners. Twenty-six plans were used to train the KBP model using Varian RapidPlan. With the trained KBP model each KBP plan was automatically generated by an individual with little experience and compared with the expert plan (closed-loop validation). Twenty-two plans that had not been used for KBP model training were also compared with the KBP results (open-loop validation). Results: Although the minimal dose of PTV-B and PTV-E was lower and the maximal dose was higher than those of the expert plan, the difference was no larger than 0.7 Gy. In the closed-loop validation, D_1.2cc, D_0.35cc, and D_mean of the spinal cord was decreased by 0.9 Gy, 0.6 Gy, and 0.9 Gy, respectively, in the KBP plans (P<0.05). In the open-loop validation, only D_mean of the spinal cord was significantly decreased, by 0.5 Gy (P<0.05). Conclusions: The dose coverage and uniformity for PTV was slightly worse in the KBP for spine SBRT while the dose to the spinal cord was reduced, but the differences were small. Thus, inexperienced planners could easily generate a clinically feasible plan for spine SBRT by using KBP.