• 치위생과학회지
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• 제4권1호
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• pp.33-38
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• 2004

The purpose of this study was to analyzes the Job of Dental Hygienists in Dental (Clinics) Hospitals the Capital region. This study analyzes the degree of job importance and education-training need about and task, task according to work place and work age. The results are as follows : (1) Job importance of dental hygienists were order 'photographing in Dental Radiology', 'Management of Dental clinic', 'Oral prophylaxis', in case education-training need was order 'dental health insurance', 'Oral prophylaxis', 'Management of Dental clinic'. duty more than 5.0 of job importance and education-training need was as 'dental health education', 'Oral prophylaxis', 'preventive dental treatment', 'dental assistance (cooperation)', 'photographing in Dental Radiology', 'dental health insurance', 'Management of Dental clinic', Duty of practice centering in Dental (Clinics) Hospitals except 'Public oral health'. (2) Job importance and education-training need of task increased most of job importance in proportion to education-training need. (3) No significantly between dental hospital hygienist and dental clinic hygienist difference of job importance and education-training need according to work place. but 'Management of Dental clinic' and 'dental health insurance' of dental hospital hygienist lower than dental clinic hygienist. (4) The results job importance compare less 3 years to more 3 years of dental hygienists were perceive significantly 'dental health education', 'Public oral health', 'dental health insurance', 'Management of Dental clinic' the other hand, education-training need was perceive significantly 'preventive dental treatment'.

• Korean Journal of Radiology
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• 제21권8호
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• pp.1007-1017
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• 2020

Objective: The purpose of our study was to investigate the predictive abilities of clinical and computed tomography (CT) features for outcome prediction in patients with coronavirus disease (COVID-19). Materials and Methods: The clinical and CT data of 238 patients with laboratory-confirmed COVID-19 in our two hospitals were retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in the training cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in the validation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, or death. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. A nomogram was constructed based on the combination of clinical and CT features, and its prognostic performance was externally tested in the validation group. The predictive value of the combined model was compared with models built on the clinical and radiological attributes alone. Results: Overall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohort experienced adverse outcomes. Underlying comorbidity (hazard ratio [HR], 3.35; 95% confidence interval [CI], 1.67-6.71; p < 0.001), lymphocyte count (HR, 0.12; 95% CI, 0.04-0.38; p < 0.001) and crazy-paving sign (HR, 2.15; 95% CI, 1.03-4.48; p = 0.042) were the independent factors. The nomogram displayed a concordance index (C-index) of 0.82 (95% CI, 0.76-0.88), and its prognostic value was confirmed in the validation cohort with a C-index of 0.89 (95% CI, 0.82-0.96). The combined model provided the best performance over the clinical or radiological model (p < 0.050). Conclusion: Underlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverse outcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predicting adverse outcomes of patients with COVID-19.

• Imaging Science in Dentistry
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• 제53권1호
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• pp.27-34
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• 2023

Purpose: The aim of this study was to clarify the influence of training with a different kind of lesion on the performance of a target model. Materials and Methods: A total of 310 patients(211 men, 99 women; average age, 47.9±16.1 years) were selected and their panoramic images were used in this study. We created a source model using panoramic radiographs including mandibular radiolucent cyst-like lesions (radicular cyst, dentigerous cyst, odontogenic keratocyst, and ameloblastoma). The model was simulatively transferred and trained on images of Stafne's bone cavity. A learning model was created using a customized DetectNet built in the Digits version 5.0 (NVIDIA, Santa Clara, CA). Two machines(Machines A and B) with identical specifications were used to simulate transfer learning. A source model was created from the data consisting of ameloblastoma, odontogenic keratocyst, dentigerous cyst, and radicular cyst in Machine A. Thereafter, it was transferred to Machine B and trained on additional data of Stafne's bone cavity to create target models. To investigate the effect of the number of cases, we created several target models with different numbers of Stafne's bone cavity cases. Results: When the Stafne's bone cavity data were added to the training, both the detection and classification performances for this pathology improved. Even for lesions other than Stafne's bone cavity, the detection sensitivities tended to increase with the increase in the number of Stafne's bone cavities. Conclusion: This study showed that using different lesions for transfer learning improves the performance of the model.

• 한국방사선학회논문지
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• 제18권3호
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• pp.203-212
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• 2024

동물병원 방사선사를 위한 대학 방사선학과 교육과정을 개발에 기초자료로 사용 하고자 동물병원전문와 방사선학과 재학생들을 대상으로 설문조사를 실시하였다. 동물병원전문가 20명을 대상으로 기본정보와 방사선검사 교육 이수, 방사선검사 실시 경험, 동물케어 및 동물 해부 생리학 교육 이수, 방사선 안전관리 교육 이수, 방사선 생물학 교육 이수에 대해서, 방사선학과 재학생 171명을 대상으로 기본정보와 취업진로 분야, 동물병원 인식 분야, 동물병원 관련 학과 환경에 대해 온라인 설문조사를 실시하였다. 설문조사 결과는 엑셀에 정량적으로 입력 후 SPSS ver. 26.0 을 사용하여 분석하였다. 재학생 의 평균 나이는 22.6세 이었고, 전체 171명 중 남자 92명이었고 여자는 79명이었다. 취업진로 분야에서 전체 대상자의 62.6%가 의료기관 취업전망이 좋다고 응답하였고, 의료기관 외 취업 희망에서는 동물병원이 83명으로 가장 높았다. 동물병원 취업을 희망한다고 응답한 83명 중 64명이 동물을 좋아해서, 47명이 발전 가능성이 높아서 동물병원 취업을 희망하였다. 동물병원 발전 가능성이 있다고 응답한 159명 중 96.2% 반려동물증가로 인한다고 응답하였다. 동물병원 관련 학과 환경에서는 94.7% 관련 기자재가 없다고 응답하였고, 학과에 동물케어 교과목 72.5%, 해부생리 82.5%로 개설이 필요하다고 응답하였다. 76.6% 동물관련 교과목이 개설되면 수강 의사가 있다고 응답하였다. 동물병원전문가 전체 대상자 20명 중에 4명이 동물에 대한 방사선 검사 경험이 없었고, 방사선사 2명, 기타 2명 이었다. 동물방사선 검사에 대한 교육을 받지 않은 자는 7명 이었고, 방사선사 2명은 동물 케어와 동물 해부생리에 대한 교육을 받지 않았다. 본 연구는 향후 동물병원에 취업하는 방사선사를 위한 방사선학과 교육과정 개발에 도움이 될 것으로 사료된다.

• 한국방사선학회논문지
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• 제18권3호
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• pp.293-300
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• 2024

2023년 10월 31일 의료기사 등에 관한 법률의 개정에 따라 방사선사 면허를 취득하기 위해서는 현장실습과목을 필수로 이수하여야 한다. 이에 따라 의료기관 현장실습의 실태를 조사하여 개정된 의료기사법을 알리고, 현장실습의 실효성을 높이기 위한 개선방안을 제안하고자 한다. 2023년 3월부터 4월까지 의료기관에 종사하는 방사선사를 대상으로 설문을 시행하였다. 설문지는 국내 포털사이트인 N사의 폼을 통해 받았으며, 응답자는 120명이었다. 현장실습 학생의 교육을 담당한 경험이 있는 응답자는 68.3%인 82명이었다. 의료기사 등에 관한 법률 개정으로 방사선사면허를 취득하기 위해 현장실습이 의무화된 사실을 알고 있는 응답자는 58%로 나타났다. 의료기사 등에 관한 법률 제9조 무면허자의 업무 금지 등에 따라 대학 등에서 취득하려는 면허에 상응하는 교육과정을 이수하기 위하여 실습 중에 있는 사람의 실습에 필요한 경우는 해당 의료기사 등의 업무를 수행할 수 있다는 사실을 알고 있는 응답자는 50%로 나타났다. 현재 현장 실습 시 어떤 교육을 하는지 묻는 질문에 참관, 환자 안내 및 환자 자세 유지와 이동 외에 방사선을 발생시키는 행위를 하게 한다는 응답자는 6%로 나타났다. 면허 취득을 위한 현장실습이 의무화됨에 따라 앞으로의 교육 방향에 묻는 질문에 77%의 응답자가 현행보다 더 많은 것을 교육할 것이라고 응답하였다. 현장실습의 적절한 전체 시간을 묻는 질문에는 12주 480시간이 35%, 8주 320시간이 33%, 16주 640시간이 27%로 나타났다. 현행 현장실습은 각종 규제에 따라 부실한 교육여건이며, 학생들의 만족도 또한 낮음을 알 수 있다. 그러나 의료기사 등에 관한 법률이 개정됨에 따라 방사선사 면허를 취득하기 위해 현장실습이 의무화되었으며, 현장실습의 교육여건을 개선할 필요가 있다. 이에 따라 원자력안전법과 진단용방사선발생장치의 안전관리에 관한 규칙을 준수하며, 표준화된 실습 목표와 평가 시스템 도입, 수련 병원 지정과 교육 전담 방사선사 지정, 확대된 실습 기간과 모의실습을 도입하여 현장실습 교육의 내실화가 필요하다.

• Korean Journal of Radiology
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• 제22권4호
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• pp.612-623
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• 2021

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988-0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618-0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion: The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

• Korean Journal of Radiology
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• 제24권5호
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• pp.454-464
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• 2023

Objective: We aimed to investigate current expectations and clinical adoption of artificial intelligence (AI) software among neuroradiologists in Korea. Materials and Methods: In April 2022, a 30-item online survey was conducted by neuroradiologists from the Korean Society of Neuroradiology (KSNR) to assess current user experiences, perceptions, attitudes, and future expectations regarding AI for neuro-applications. Respondents with experience in AI software were further investigated in terms of the number and type of software used, period of use, clinical usefulness, and future scope. Results were compared between respondents with and without experience with AI software through multivariable logistic regression and mediation analyses. Results: The survey was completed by 73 respondents, accounting for 21.9% (73/334) of the KSNR members; 72.6% (53/73) were familiar with AI and 58.9% (43/73) had used AI software, with approximately 86% (37/43) using 1-3 AI software programs and 51.2% (22/43) having up to one year of experience with AI software. Among AI software types, brain volumetry software was the most common (62.8% [27/43]). Although 52.1% (38/73) assumed that AI is currently useful in practice, 86.3% (63/73) expected it to be useful for clinical practice within 10 years. The main expected benefits were reducing the time spent on repetitive tasks (91.8% [67/73]) and improving reading accuracy and reducing errors (72.6% [53/73]). Those who experienced AI software were more familiar with AI (adjusted odds ratio, 7.1 [95% confidence interval, 1.81-27.81]; P = 0.005). More than half of the respondents with AI software experience (55.8% [24/43]) agreed that AI should be included in training curriculums, while almost all (95.3% [41/43]) believed that radiologists should coordinate to improve its performance. Conclusion: A majority of respondents experienced AI software and showed a proactive attitude toward adopting AI in clinical practice, suggesting that AI should be incorporated into training and active participation in AI development should be encouraged.

• Korean Journal of Radiology
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• 제22권4호
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• pp.535-546
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• 2021

Objective: To evaluate the feasibility of texture analysis on non-contrast-enhanced T1 maps of cardiac magnetic resonance (CMR) imaging for the diagnosis of myocardial injury in acute myocardial infarction (MI). Materials and Methods: This study included 68 patients (57 males and 11 females; mean age, 55.7 ± 10.5 years) with acute ST-segment-elevation MI who had undergone 3T CMR after a percutaneous coronary intervention. Forty patients of them also underwent a 6-month follow-up CMR. The CMR protocol included T2-weighted imaging, T1 mapping, rest first-pass perfusion, and late gadolinium enhancement. Radiomics features were extracted from the T1 maps using open-source software. Radiomics signatures were constructed with the selected strongest features to evaluate the myocardial injury severity and predict the recovery of left ventricular (LV) longitudinal systolic myocardial contractility. Results: A total of 1088 segments of the acute CMR images were analyzed; 103 (9.5%) segments showed microvascular obstruction (MVO), and 557 (51.2%) segments showed MI. A total of 640 segments were included in the 6-month follow-up analysis, of which 160 (25.0%) segments showed favorable recovery of LV longitudinal systolic myocardial contractility. Combined radiomics signature and T1 values resulted in a higher diagnostic performance for MVO compared to T1 values alone (area under the curve [AUC] in the training set; 0.88, 0.72, p = 0.031: AUC in the test set; 0.86, 0.71, p = 0.002). Combined radiomics signature and T1 values also provided a higher predictive value for LV longitudinal systolic myocardial contractility recovery compared to T1 values (AUC in the training set; 0.76, 0.55, p < 0.001: AUC in the test set; 0.77, 0.60, p < 0.001). Conclusion: The combination of radiomics of non-contrast-enhanced T1 mapping and T1 values could provide higher diagnostic accuracy for MVO. Radiomics also provides incremental value in the prediction of LV longitudinal systolic myocardial contractility at six months.

• 한국방사선학회논문지
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• 제14권7호
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• pp.923-930
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• 2020

2020년도는 COVID-19 위기 상황으로 인하여 불가피하게 전면 비대면 교육을 시행하게 되었다. 연구자는 방사선사면허대비 전국 보건 계열 방사선과 3학년, 방사선학과 4학년 학생들이 면허시험을 앞두고 실시한 대면 교육과 비대면 모의시험(2019년 1.2회, 2020년 1.2회)의 성적을 알아보기 위하여 전국 방사선학과 및 방사선과 48개 대학 중 5개의 대학을 선정하여 대면 교육과 비대면 교육(2019년 1,2회, 2020년 1,2회)의 성적을 1회 모의고사 시험에 대면 교육과 비대면 교육(2019년 1회, 2020년 1회)의 성적을 비교하여, 비모수 검정으로 통계를 분석한 결과, 이론(Z=-2.023, p<0.05), 응용(Z=-2.023, p<0.05), 실기(Z=-2.023, p<0.05) 모두 성적에 차이가 있는 것으로 나타났다. 2회 모의고사 시험에 대면 교육과 비대면 교육 (2019년 2회, 2020년 2회)의 성적을 비교하여, 비모수 검정으로 통계를 분석한 결과, 이론(Z=-2.023, p<0.05), 응용(Z=-2.023, p<0.05), 실기(Z=-1.753, p<0.05) 성적에 차이가 있는 것으로 나타났다. 모의고사 시험의 결과 비대면 교육이 대면교육에 비교해 성적이 저조함에 따라 강의 방법을 달리하거나, 학생들과 소통할 수 있는 다양한 교육 방법을 병행해야 할 것으로 사료된다.

• Korean Journal of Radiology
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• 제21권7호
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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.