• The Journal of Korean Academy of Sensory Integration
• /
• v.20 no.1
• /
• pp.26-38
• /
• 2022

Objective : The aim of this study was to determine the priority competency by analyzing the importance, performance, and educational requirements of occupational therapists to develop a competency in performing sensory integration intervention. Methods : Occupational therapists were surveyed by gender, age, educational background, work location, occupational therapy experience, and sensory integration therapy experience. The difference was investigated through the importance-performance analysis of competency, and the priority of the competency was investigated using the Borich demand analysis method. Results : The therapists recognized professional competency as the most important, whereas performance was the least important. In all sub-competencies, the importance was high, but the performance was low. As a result, the education requirement was highest for professional competency. The importance-performance analysis revealed that professional competency was the area requiring the most urgent improvement. As a result of the Borich demand analysis, statistically significant differences between the importance of all competencies and the actual performance. The most significant difference was found in the professional competency group. Conclusion : The occupational therapists in this study who performed sensory integration interventions recognized professional competency as the most important but most lacking in actual clinical practice. The results of this study will be used as guidelines for developing competency-based sensory integrated intervention curricula.

• Journal of Internet Computing and Services
• /
• v.23 no.3
• /
• pp.35-45
• /
• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• Journal of Korea Entertainment Industry Association
• /
• v.14 no.8
• /
• pp.397-405
• /
• 2020

The purpose of study was to compare respiratory function and quadriceps muscle activity in stroke patients by applying inspiratory muscle training combined with whole body vibration. In addition, the purpose of study is to present an exercise method for improving the respiratory function of stroke patients and the function of the lower limb muscles of stroke patients. Totally, 21 patients with Stroke patients were randomly assigned to two groups through clinical sampling. 11 patients who applied whole body vibration combined with respiratory exercise were randomly assigned to Experiment Group I, and 10 patients who applied placebo exercise combined with breathing exercise were randomly assigned to Experiment Group II. And for 5 weeks, 4 days/week, 1 time/day, 4 sets/1 time intervention program was implemented. Before intervention, the respiratory function was measured with a maximum inspiratory pressure meter, the lower extremity muscle activity was measured using the surface EMG, and the balance ability was measured using a bug balance test. And after 5 weeks, the post-test was re-measured and analyzed in the same way as the pre-test. In the comparison of changes within the group of experimental group I, there were significant differences in the activity and balance of the respiratory muscle strength, the biceps femoris, and the anterior tibialis muscle (p<.05). In the comparison of the changes in the experimental group I, there was a significant difference in respiratory strength and balance (p<.05). In the comparison of changes between groups, there was a significant difference in the activity of the biceps femoris and anterior tibialis (p<.01). In the future, research on protocols for respiratory exercise and whole body vibration to improve neuromuscular function is considered to be necessary.

• Tuberculosis and Respiratory Diseases
• /
• v.86 no.3
• /
• pp.203-215
• /
• 2023

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R² and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R² value was 0.27 and in set II, LightGBM was the best model with the highest R² value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R² value of 0.56 and the lowest MSE of 174.07. Conclusion: The LightGBM model showed the best performance in predicting postoperative lung function.

• Korean Journal of Radiology
• /
• v.22 no.5
• /
• pp.706-713
• /
• 2021

Objective: To evaluate the impact of surgical simulation training using a three-dimensional (3D)-printed model of tetralogy of Fallot (TOF) on surgical skill development. Materials and Methods: A life-size congenital heart disease model was printed using a Stratasys Object500 Connex2 printer from preoperative electrocardiography-gated CT scans of a 6-month-old patient with TOF with complex pulmonary stenosis. Eleven cardiothoracic surgeons independently evaluated the suitability of four 3D-printed models using composite Tango 27, 40, 50, and 60 in terms of palpation, resistance, extensibility, gap, cut-through ability, and reusability of. Among these, Tango 27 was selected as the final model. Six attendees (two junior cardiothoracic surgery residents, two senior residents, and two clinical fellows) independently performed simulation surgeries three times each. Surgical proficiency was evaluated by an experienced cardiothoracic surgeon on a 1-10 scale for each of the 10 surgical procedures. The times required for each surgical procedure were also measured. Results: In the simulation surgeries, six surgeons required a median of 34.4 (range 32.5-43.5) and 21.4 (17.9-192.7) minutes to apply the ventricular septal defect (VSD) and right ventricular outflow tract (RVOT) patches, respectively, on their first simulation surgery. These times had significantly reduced to 17.3 (16.2-29.5) and 13.6 (10.3-30.0) minutes, respectively, in the third simulation surgery (p = 0.03 and p = 0.01, respectively). The decreases in the median patch appliance time among the six surgeons were 16.2 (range 13.6-17.7) and 8.0 (1.8-170.3) minutes for the VSD and RVOT patches, respectively. Summing the scores for the 10 procedures showed that the attendees scored an average of 28.58 ± 7.89 points on the first simulation surgery and improved their average score to 67.33 ± 15.10 on the third simulation surgery (p = 0.008). Conclusion: Inexperienced cardiothoracic surgeons improved their performance in terms of surgical proficiency and operation time during the experience of three simulation surgeries using a 3D-printed TOF model using Tango 27 composite.

• Journal of the Korean Society of Radiology
• /
• v.18 no.1
• /
• pp.53-63
• /
• 2024

The purpose of this study was to analyze radiological technologists' (RT) task perceptions of intravenous injection behavior of contrast medium and use it as basic data for future workforce response plans. We surveyed a total of 172 RT using questionnaire terms consisting of demographic characteristics, job priorities, and RT' task perceptions of intravenous injection behavior. Statistical analysis was performed using descriptive statistics, frequency analysis, independent samples T-test, and ANOVA analysis. As a result, first, current clinical RT were highly aware of the need for intravenous injection behavior as a response to the future workforce of them, and the workload burden resulting from this was evaluated as low. Second, the fear of intravenous injection behavior was found to be significant, so it is judged to be useful to perform them as selective job actions rather than all RT' task. Third, the need for training courses and certification for RT' intravenous injection behavior is being raised, and additional specific research on this is required. Last, RT' positive perception of intravenous injection behavior could be expected as a foundation for improving national medical services, strengthening RT expertise, and expanding tasks.

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.37 no.6
• /
• pp.172-177
• /
• 2023

In order to deduce the pathogenic factor(PF) diagnosis logic of underlying in pattern identification of Korean medicine, 2,072 cases of DSOM(Diagnosis System of Oriental Medicine) data from May 2005 to April 2022 were collected and analyzed by means of decision tree model(DTM). The entire data were divided into training data and validation data at a ratio of 7:3. The CHAID algorithm was used for analysis of DTM, and then validity was tested by applying the validation data. The decision rules of items and pathways determined from the diagnosis data of Qi Deficiency, Blood Deficiency, Yin Deficiency and Yang Deficiency Pathogenic Factor of DSOM were as follows. Qi Deficiency PF had 7 decision rules and used 5 questions: Q124, Q116a, Q119, Q119a, Q55. The primary indicators(PI) were 'lack of energy' and 'weary of talking'. Blood deficiency PF had 7 decision rules and used 6 questions: Q113, Q84, Q85, Q114, Q129, Q130. The PI were 'numbness in the limbs', 'dizziness when standing up', and 'frequent cramps'. Yin deficiency PF had 3 decision rules and used 2 questions: Q144 and Q56. The PI were 'subjective heat sensation from the afternoon to night' and 'heat sensation in the limbs'. Yang deficiency PF had 3 decision rules and used 3 questions: Q55, Q10, and Q102. The PI were 'sweating even with small movements' and 'lack of energy'. Conclusively, these rules and symptom information to decide the Qi·Blood·Yin·Yang Deficiency PF would be helpful for Korean medicine diagnostics.

• Korean Journal of Radiology
• /
• v.24 no.8
• /
• pp.772-783
• /
• 2023

Objective: Imaging-based survival stratification of patients with gliomas is important for their management, and the 2021 WHO classification system must be clinically tested. The aim of this study was to compare integrative imaging- and pathology-based methods for survival stratification of patients with diffuse glioma. Materials and Methods: This study included diffuse glioma cases from The Cancer Genome Atlas (training set: 141 patients) and Asan Medical Center (validation set: 131 patients). Two neuroradiologists analyzed presurgical CT and MRI to assign gliomas to five imaging-based risk subgroups (1 to 5) according to well-known imaging phenotypes (e.g., T2/FLAIR mismatch) and recategorized them into three imaging-based risk groups, according to the 2021 WHO classification: group 1 (corresponding to risk subgroup 1, indicating oligodendroglioma, isocitrate dehydrogenase [IDH]-mutant, and 1p19q-codeleted), group 2 (risk subgroups 2 and 3, indicating astrocytoma, IDH-mutant), and group 3 (risk subgroups 4 and 5, indicating glioblastoma, IDHwt). The progression-free survival (PFS) and overall survival (OS) were estimated for each imaging risk group, subgroup, and pathological diagnosis. Time-dependent area-under-the receiver operating characteristic analysis (AUC) was used to compare the performance between imaging-based and pathology-based survival model. Results: Both OS and PFS were stratified according to the five imaging-based risk subgroups (P < 0.001) and three imaging-based risk groups (P < 0.001). The three imaging-based groups showed high performance in predicting PFS at one-year (AUC, 0.787) and five-years (AUC, 0.823), which was similar to that of the pathology-based prediction of PFS (AUC of 0.785 and 0.837). Combined with clinical predictors, the performance of the imaging-based survival model for 1- and 3-year PFS (AUC 0.813 and 0.921) was similar to that of the pathology-based survival model (AUC 0.839 and 0.889). Conclusion: Imaging-based survival stratification according to the 2021 WHO classification demonstrated a performance similar to that of pathology-based survival stratification, especially in predicting PFS.

• Clinical Endoscopy
• /
• v.55 no.2
• /
• pp.215-225
• /
• 2022

Background/Aims: The Thai Association for Gastrointestinal Endoscopy published recommendations on safe endoscopy during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to assess the practicality and applicability of the recommendations and the perceptions of endoscopy personnel on them. Methods: A validated questionnaire was sent to 1290 endoscopy personnel globally. Of these, the data of all 330 responders (25.6%) from 15 countries, related to the current recommendations on proper personal protective equipment (PPE), case selection, scope cleaning, and safety perception, were analyzed. Ordinal logistic regression was used to determine the relationships between the variables. Results: Despite an overwhelming agreement with the recommendations on PPE (94.5%) and case selection (95.5%), their practicality and applicability on PPE recommendations and case selection were significantly lower (p=0.001, p=0.047, p<0.001, and p=0.032, respectively). Factors that were associated with lower sense of safety in endoscopy units were younger age (p=0.004), less working experience (p=0.008), in-training status (p=0.04), and higher national prevalence of COVID-19 (p=0.003). High prevalent countries also had more difficulty implementing the guidelines (p<0.001) and they considered the PPE recommendations less practical and showed lower agreement with them (p<0.001 and p=0.008, respectively). A higher number of in-hospital COVID-19 patients was associated with less agreement with PPE recommendations (p=0.039). Conclusions: Using appropriate PPE and case selection in endoscopic practice during a pandemic remains a challenge. Resource availability and local prevalence are critical factors influencing the adoption of the current guidelines.

• Journal of Trauma and Injury
• /
• v.37 no.3
• /
• pp.201-208
• /
• 2024

Purpose: The number of elderly patients with trauma is increasing; therefore, precise models are necessary to estimate the mortality risk of elderly patients with trauma for informed clinical decision-making. This study aimed to develop machine learning based predictive models that predict 30-day mortality in severely injured elderly patients with trauma and to compare the predictive performance of various machine learning models. Methods: This study targeted patients aged ≥65 years with an Injury Severity Score of ≥15 who visited the regional trauma center at Chungbuk National University Hospital between 2016 and 2022. Four machine learning models-logistic regression, decision tree, random forest, and eXtreme Gradient Boosting (XGBoost)-were developed to predict 30-day mortality. The models' performance was compared using metrics such as area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, specificity, F1 score, as well as Shapley Additive Explanations (SHAP) values and learning curves. Results: The performance evaluation of the machine learning models for predicting mortality in severely injured elderly patients with trauma showed AUC values for logistic regression, decision tree, random forest, and XGBoost of 0.938, 0.863, 0.919, and 0.934, respectively. Among the four models, XGBoost demonstrated superior accuracy, precision, recall, specificity, and F1 score of 0.91, 0.72, 0.86, 0.92, and 0.78, respectively. Analysis of important features of XGBoost using SHAP revealed associations such as a high Glasgow Coma Scale negatively impacting mortality probability, while higher counts of transfused red blood cells were positively correlated with mortality probability. The learning curves indicated increased generalization and robustness as training examples increased. Conclusions: We showed that machine learning models, especially XGBoost, can be used to predict 30-day mortality in severely injured elderly patients with trauma. Prognostic tools utilizing these models are helpful for physicians to evaluate the risk of mortality in elderly patients with severe trauma.