• Korean Journal of Radiology
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• v.20 no.1
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• pp.114-125
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• 2019

Objective: Segmented cardiac cine magnetic resonance imaging (MRI) is the gold standard for cardiac ventricular volumetric assessment. In patients with difficulty in breath-holding or arrhythmia, this technique may generate images with inadequate quality for diagnosis. Real-time cardiac cine MRI has been developed to address this limitation. We aimed to assess the performance of retrospective electrocardiography-gated real-time cine MRI at 3T for left ventricular (LV) volume and mass measurement. Materials and Methods: Fifty-one patients were consecutively enrolled. A series of short-axis cine images covering the entire left ventricle using both segmented and real-time balanced steady-state free precession cardiac cine MRI were obtained. End-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), and LV mass were measured. The agreement and correlation of the parameters were assessed. Additionally, image quality was evaluated using European CMR Registry (Euro-CMR) score and structure visibility rating. Results: In patients without difficulty in breath-holding or arrhythmia, no significant difference was found in Euro-CMR score between the two techniques (0.3 ± 0.7 vs. 0.3 ± 0.5, p > 0.05). Good agreements and correlations were found between the techniques for measuring EDV, ESV, EF, SV, and LV mass. In patients with difficulty in breath-holding or arrhythmia, segmented cine MRI had a significant higher Euro-CMR score (2.3 ± 1.2 vs. 0.4 ± 0.5, p < 0.001). Conclusion: Real-time cine MRI at 3T allowed the assessment of LV volume with high accuracy and showed a significantly better image quality compared to that of segmented cine MRI in patients with difficulty in breath-holding and arrhythmia.

• The Journal of Korean Academy of Sensory Integration
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• v.21 no.3
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• pp.39-53
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• 2023

Objective : The purpose was the effect of sensory integration therapy combined with an eye tracker on the sensory processing and visual perception of children with developmental disabilities. Methods : It was a single-subject study with a multiple baseline design between subjects, and the intervention applied sensory integration therapy combined with an eye tracker. Visual-motor speed and saccadic eye movements were assessed at each session of baseline and intervention periods. As pre- and post-evaluation, sensory profile, Korean-Developmental Test of Visual Perception and Trail Making Test were conducted. The results of each session evaluation and pre- and post-evaluation researched the effectiveness of the intervention through visual analysis and trend line analysis. Results : As a result of the evaluation for each session, the slope of the trend line for all children in visual-motor speed and saccadic eye movement increased sharply during the intervention compared to the baseline. As a result of the pre- and post-evaluation, the sensory processing of movement, body position, and visual changed from more than that of peers to a level similar to that of peers. In visual perception, all children's ability of Visual Closure increased. As a result of Trail Making Test conducted to confirm the improvement of children's visual tracking and visual-motor abilities, all children showed a decrease in performance time after the test compared to before. Conclusion : It was confirmed that sensory integration therapy combined with an eye tracker for developmental disabilities has effect on sensory processing and visual perception. It is expected to play an important role clinically as it can stimulate children's interest and motivation in line with recent technological improvements and the spread of smart devices.

• Korean Journal of Radiology
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• v.21 no.7
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• pp.869-879
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• 2020

Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 x 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.

• Korean Journal of Radiology
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• v.21 no.10
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• pp.1187-1195
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• 2020

Objective: This study aimed to determine the sonographic features suggestive of extrathyroidal extension (ETE) of thyroid cancers. Materials and Methods: We retrospectively reviewed the sonographic images of 1656 consecutive patients who had undergone thyroidectomy in 2017. The diagnostic performance of sonographic features suggestive of ETE was evaluated using operation and histopathologic reports. Sonographic features for gross ETE to the strap muscle and minor ETE were assessed for thyroid cancer abutting the anterolateral thyroid capsule. Sonographic features for tracheal invasion were assessed according to whether the angle between the tumor and the trachea was an acute, right, or obtuse angle. Sonographic features for recurrent laryngeal nerve (RLN) invasion were assessed based on the association between the tumor and tracheoesophageal groove (TEG) as preserved normal tissue, abutting or protruding into the TEG. Results: ETE was observed in 783 patients (47.3%), including 123 patients with gross ETE (7.4% [strap muscle, n = 97; RLN, n = 24; and trachea, n = 14]) and 660 patients with minor ETE (39.9%). Regarding the diagnosis of gross and minor ETE to the strap muscle, sonographic features of replacement of the strap muscle and capsular disruption showed the highest positive predictive value (75.9% and 58.5%, respectively). Thyroid cancer forming an obtuse angle with the trachea had the highest sensitivity for the diagnosis of tracheal invasion (85.7%), and thyroid cancer protrusion into the TEG showed the highest sensitivity for the diagnosis of RLN (83.3%). Conclusion: Sonography is considered beneficial in the diagnosis of ETE to the strap muscle, trachea, and RLN. Assessment of ETE is important for the accurate staging of thyroid cancer, which in turn determines the extent of surgery or whether active surveillance is appropriate or not.

• Korean Journal of Radiology
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• v.21 no.7
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• pp.859-868
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• 2020

Objective: To investigate the value of initial CT quantitative analysis of ground-glass opacity (GGO), consolidation, and total lesion volume and its relationship with clinical features for assessing the severity of coronavirus disease 2019 (COVID-19). Materials and Methods: A total of 84 patients with COVID-19 were retrospectively reviewed from January 23, 2020 to February 19, 2020. Patients were divided into two groups: severe group (n = 23) and non-severe group (n = 61). Clinical symptoms, laboratory data, and CT findings on admission were analyzed. CT quantitative parameters, including GGO, consolidation, total lesion score, percentage GGO, and percentage consolidation (both relative to total lesion volume) were calculated. Relationships between the CT findings and laboratory data were estimated. Finally, a discrimination model was established to assess the severity of COVID-19. Results: Patients in the severe group had higher baseline neutrophil percentage, increased high-sensitivity C-reactive protein (hs-CRP) and procalcitonin levels, and lower baseline lymphocyte count and lymphocyte percentage (p < 0.001). The severe group also had higher GGO score (p < 0.001), consolidation score (p < 0.001), total lesion score (p < 0.001), and percentage consolidation (p = 0.002), but had a lower percentage GGO (p = 0.008). These CT quantitative parameters were significantly correlated with laboratory inflammatory marker levels, including neutrophil percentage, lymphocyte count, lymphocyte percentage, hs-CRP level, and procalcitonin level (p < 0.05). The total lesion score demonstrated the best performance when the data cut-off was 8.2%. Furthermore, the area under the curve, sensitivity, and specificity were 93.8% (confidence interval [CI]: 86.8-100%), 91.3% (CI: 69.6-100%), and 91.8% (CI: 23.0-98.4%), respectively. Conclusion: CT quantitative parameters showed strong correlations with laboratory inflammatory markers, suggesting that CT quantitative analysis might be an effective and important method for assessing the severity of COVID-19, and may provide additional guidance for planning clinical treatment strategies.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.135-144
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• 2024

The objective of this study was to investigate the influence of feeding frequency on a sow's reproductive performance and stress response during gestation. A total of twenty multiparous sows (Yorkshire × Landrace) were used in a completely randomized design based on their parity, body weight (BW), and backfat thickness (BFT), and the sows were allotted to two different feeding systems: 1) once daily feeding (OF) and 2) twice daily feeding (TF) in corn-soybean meal based diets. The gestation diet was formulated to contain 3,265 kcal of metabolizable energy (ME) / kg, 12.90% of crude protein (CP), and 0.75 % of total lysine. The lactation diet was formulated to contain 3,265 kcal of ME / kg, 16.80% of CP, and 1.08% of total lysine and provided ad libitum during lactation. In gestation, sow BFT and BF changes were not affected by feeding frequency, but higher BW and BW gain from day 35 to 90 and day 35 to 110 were observed in OF sow (p < 0.10). In lactation, feeding frequency did not influence on BW, BW gain, BFT, BF changes, average daily feed intake, and wean-to-estrus interval. Also, there were no differences in litter size, litter weight and piglet weight in lactating sows. OF sows had higher (p < 0.05; p < 0.10) protein, solid-not-fat, and total solid concentrations in colostrum compared to TF sows, while OF sows had a lower (p < 0.05) lactose concentration in colostrum compared to TF sows. Sows in OF showed significantly lower average daily water consumption (ADWC) from day 35 to 110 of gestation (p < 0.05). While there were no significant differences in stereotypic behaviors and salivary cortisol levels during gestation between treatments, the OF sows showed less time spending on the activity at day 105 (p < 0.05). In conclusion, reduced feeding frequency increased BW gain during gestation, decreased activation time, and changed the colostrum composition. This information may contribute to the understanding of the physiological and behavioral change of gestating sows by manipulating feeding frequency.

• Therapeutic Science for Rehabilitation
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• v.13 no.2
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• pp.69-84
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• 2024

Objective : This study aimed to categorize the lifestyles of older adults into two types - healthy and unhealthy, and use machine learning to identify the personal values that influence these lifestyles. Methods : This cross-sectional study targeting middle-aged and older adults (55 years and above) living in local communities in South Korea. Data were collected from 300 participants through online surveys. Lifestyle types were dichotomized by the Yonsei Lifestyle Profile (YLP)-Active, Balanced, Connected, and Diverse (ABCD) responses using latent profile analysis. Personal value information was collected using YLP-Values (YLP-V) and analyzed using machine learning to identify the relative importance of personal values on lifestyle types. Results : The lifestyle of older adults was categorized into healthy (48.87%) and unhealthy (51.13%). These two types showed the most significant difference in social relationship characteristics. Among the machine learning models used in this study, the support vector machine showed the highest classification performance, achieving 96% accuracy and 95% area under the receiver operating characteristic (ROC) curve. The model indicated that individuals who prioritized a healthy diet, sought health information, and engaged in hobbies or cultural activities were more likely to have a healthy lifestyle. Conclusion : This study suggests the need to encourage the expansion of social networks among older adults. Furthermore, it highlights the necessity to comprehensively intervene in individuals' perceptions and values that primarily influence lifestyle adherence.

• Therapeutic Science for Rehabilitation
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• v.13 no.2
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• pp.85-94
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• 2024

Objective : This study aimed to determine the impact of an instrumental daily living activity program for adults with developmental disabilities on their daily living activities and quality of life (QOL). Methods : This study used a single-group pre-test-post-test experimental design. Participants comprised 17 adults with developmental disabilities from the Welfare Center for the Disabled. This program consisted of 10 sessions, including pre- and post-assessments, pertaining to training and education on grocery shopping, meal preparation and cleaning, home management, safety, and emergency management. The evaluation tools used in this study were the Quality of Life Questionnaire, Korean version of the Life Space Assessment (K-LSA), and Korean version of the Instrumental Activities of Daily Living (K-IADL). Results : There was a statistically significant difference in the QOL and K-IADL scores before and after the instrumental activities of daily living occupational therapy program, and there was no statistically significant difference in the K-LSA scores. Conclusion : This study confirmed that implementing an IADL program for adults with developmental disabilities had a positive impact on performance and QOL. For adults with developmental disabilities to participate in daily life and society, interventions for work participation, including training in activities of daily living, must be expanded.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.67-84
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• 2023

In some areas of the Republic of Korea, the designation and management of conservation areas do not adequately reflect regional characteristics and often impose behavioral regulations without considering the local context. One prominent example is the Busan EDC area. As a result, conflicts may arise, including large-scale civil complaints, regarding the conservation and utilization of these areas. Therefore, for the efficient designation and management of protected areas, it is necessary to consider various ecosystem factors, changes in land use, and regional characteristics. In this study, we specifically focused on the Busan EDC area and applied machine learning techniques to analyze the habitat of regional species. Additionally, we employed Explainable Artificial Intelligence techniques to interpret the results of our analysis. To analyze the regional characteristics of the waterfront area in the Busan EDC district and the habitat of migratory birds, we used bird observations as dependent variables, distinguishing between presence and absence. The independent variables were constructed using land cover, elevation, slope, bridges, and river depth data. We utilized the XGBoost (eXtreme Gradient Boosting) model, known for its excellent performance in various fields, to predict the habitat probabilities of 11 bird species. Furthermore, we employed the SHapley Additive exPlanations technique, one of the representative methodologies of XAI, to analyze the relative importance and impact of the variables used in the model. The analysis results showed that in the EDC business district, as one moves closer to the river from the waterfront, the likelihood of bird habitat increases based on the overlapping habitat probabilities of the analyzed bird species. By synthesizing the major variables influencing the habitat of each species, key variables such as rivers, rice fields, fields, pastures, inland wetlands, tidal flats, orchards, cultivated lands, cliffs & rocks, elevation, lakes, and deciduous forests were identified as areas that can serve as habitats, shelters, resting places, and feeding grounds for birds. On the other hand, artificial structures such as bridges, railways, and other public facilities were found to have a negative impact on bird habitat. The development of a management plan for conservation areas based on the objective analysis presented in this study is expected to be extensively utilized in the future. It will provide diverse evidential materials for establishing effective conservation area management strategies.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.43-54
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• 2024

This study employs text mining techniques to compare user responses across various Over-The-Top (OTT) service platforms. The primary objective of the research is to understand user satisfaction with OTT service platforms and contribute to the formulation of more effective review strategies. The key questions addressed in this study involve identifying prominent topics and keywords in user reviews of different OTT services and comprehending platform-specific user reactions. TF-IDF is utilized to extract significant words from positive and negative reviews, while BERTopic, an advanced topic modeling technique, is employed for a more nuanced and comprehensive analysis of intricate user reviews. The results from TF-IDF analysis reveal that positive app reviews exhibit a high frequency of content-related words, whereas negative reviews display a high frequency of words associated with potential issues during app usage. Through the utilization of BERTopic, we were able to extract keywords related to content diversity, app performance components, payment, and compatibility, by associating them with content attributes. This enabled us to verify that the distinguishing attributes of the platforms vary among themselves. The findings of this study offer significant insights into user behavior and preferences, which OTT service providers can leverage to improve user experience and satisfaction. We also anticipate that researchers exploring deep learning models will find our study results valuable for conducting analyses on user review text data.