• The Journal of Korean Physical Therapy
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• v.19 no.3
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• pp.41-46
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• 2007

Purpose: This study was to investigate how dose the radiography findings are to magnetic resonance (MR) image findings in the L5-S1 instability patients. The subjects of this study were comprised of eleven males and fifteen females, who had Lumbago and agreed with this research. Methods: Radiography and MR images of Lumbar spine were acquired respectively from subjects in conditions of maximum flexion and extension. The horizontal and angular displacements in lumabosacral spine radiography were used to assess the instability of lumbar spine. MR images were also used to evaluate the intervertebral disc abnormalities and change of bone marrow. Results: The results are as follows. 1. In the case of flexion transitional displacement proposed by Dupuis et al, the specificity and negative predictive value were good accuracy ($0.7{\sim}0.8$), and the negative predictive value was in average. In the case of extension displacement, the negative predictive value was about average ($0.6{\sim}0.7$), but the sensitivity, specificity and positive predictive value were below the poor (<0.6). On the other side, the specificity was about average but other things were below in the case of angular displacement. 2. In the case of flexion transitional displacement proposed by Dupuis et al., compared with the intervertebral disc abnormalities, the negative prediction value was excellent, the sensitivity good, and the specificity about average. In the case of extension, the negative prediction value was about average, but the other things were poor. On the other side the specificity and negative predictive value had good accuracy and the sensitivity and positive prediction value were below average in the case of angular displacement. Conclusion: The above results show that the radiography finding is sufficiently helpful to find the lumbar spine instability as an economic point of view.

• Journal of Korean Neurosurgical Society
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• v.64 no.3
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• pp.418-426
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• 2021

Objective : A role of diffusion-weighted imaging (DWI) in the diagnosis of cerebral venous thrombosis (CVT) is not well-understood. This study evaluates the effectiveness of DWI in the diagnosis of CVT. Methods : Literature search was conducted in electronic databases for the identification of studies which reported the outcomes of patients subjected to DWI for CVT diagnosis. Random-effects meta-analyses were performed to achieve overall estimates of important diagnostic efficiency indices including hyperintense signal rate, the sensitivity and specificity of DWI in diagnosing CVT, and the apparent diffusion coefficient (ADC) of DWI signal areas and surrounding tissue. Results : Nineteen studies (443 patients with 856 CVTs; age 40 years [95% confidence interval (CI), 33 to 43]; 28% males [95% CI, 18 to 38]; symptom onset to DWI time 4.6 days [95% CI, 2.3 to 6.9]) were included. Hyperintense signals on DWI were detected in 40% (95% CI, 26 to 55) of the cases. The sensitivity of DWI for detecting CVT was 22% (95% CI, 11 to 34) but specificity was 98% (95% CI, 95 to 100). ADC values were quite heterogenous in DWI signal areas. However, generally the ADC values were lower in DWI signal areas than in surrounding normal areas (mean difference-0.33×10-3 ㎟/s [95% CI, -0.44 to -0.23]; p<0.00001). Conclusion : DWI has a low sensitivity in detecting CVT and thus has a high risk of missing many CVT cases. However, because of its high specificity, it may have supporting and exploratory roles in CVT diagnosis.

• Proceedings of the KOR-BRONCHOESO Conference
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• 1993.05a
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• pp.103-103
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• 1993

Fine needle aspiration biopsy(FNAB) is widely used in screening of head and neck(H&N) masses because it has high accuracy and few damage. The 235 cases of FNAB were performed on patient with H&N mass at the Department of Otolaryngology, Hanyang University Hospital during 1 years, from March 1985 to February 1992. The 188 of 235 were proven histologically, clinically. The results obtained were as follows ; 1) FNAB for malignant H&N mass shows sensitivity of 81.5% specificity of 96.8%. 2) FNAB for malignant LN shows sensitivity of 86.0%, specificity of 88.9%. 3) FNAB for salivary gland shows sensitivity of 66.7%, specificity 81.8%. 4) FNAB for soft tissues, bone, and intraoral lesions shows sensitivity of 90%, specificity 95.8%. 5) FNAB for tuberculosis shows false negative of 55.2%. 6) The result of FNAB was not closely related with location or size of neck nodes.

• Clinical Endoscopy
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• v.55 no.2
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• pp.197-207
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• 2022

Background/Aims: Endoscopic visualization of the microscopic anatomy can facilitate the real-time diagnosis of pancreatobiliary disorders and provide guidance for treatment. This study aimed to review the technique, image classification, and diagnostic performance of confocal laser endomicroscopy (CLE). Methods: We conducted a systematic review of CLE in pancreatic and biliary ducts of humans, and have provided a narrative of the technique, image classification, diagnostic performance, ongoing research, and limitations. Results: Probe-based CLE differentiates malignant from benign biliary strictures (sensitivity, ≥89%; specificity, ≥61%). Needle-based CLE differentiates mucinous from non-mucinous pancreatic cysts (sensitivity, 59%; specificity, ≥94%) and identifies dysplasia. Pancreatitis may develop in 2-7% of pancreatic cyst cases. Needle-based CLE has potential applications in adenocarcinoma, neuroendocrine tumors, and pancreatitis (chronic or autoimmune). Costs, catheter lifespan, endoscopist training, and interobserver variability are challenges for routine utilization. Conclusions: CLE reveals microscopic pancreatobiliary system anatomy with adequate specificity and sensitivity. Reducing costs and simplifying image interpretation will promote utilization by advanced endoscopists.

• Journal of Community Nutrition
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• v.2 no.2
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• pp.129-134
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• 2000

The purpose of the present study is to evaluate the validity of the BMI as an indicator of obesity for Koreans. The usefulness of the BMI to represent overweight and obesity was evaluated by measuring the relative validity of sensitivity and specificity, and was compared with the validity of triceps skinfold thickness(mm). To measure the relative validity of the BMI and triceps skinfold thickness, body fat(%) was used as a reference measure of obesity. The study population included 844 participants aged 20-69 years who resided in Kuri City in Kyunggi province. Participants were measured regarding weight, height, triceps skinfold thickness and body fat. The prevalence of obesity for male subjects was 32.0%, 66.2%, and 0.9%, and for female subjects, 17.6%, 56.1%, 12.4% based on the BMI, triceps skinfold thickness, and body fat(%) respectively. The prevalence of obesity was higher based on the BMI or triceps skinfold thickness than body fat measurement. The sensitivity and specificity of the BMI were 33.3% and 67.9% in male subjects and 77.7% and 90.8% in female subjects. Sensitivity of the BMI was lower, and specificity was higher than those of triceps skinfold thickness. In summary, BMI as an indicator of obesity for Korean showed a tendency of overestimation of obesity prevalence. Therefore, there is a need to develop a more reliable obesity index other than the BMI for Koreans.

• Journal of radiological science and technology
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• v.43 no.6
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• pp.461-467
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• 2020

In the field of nuclear medicine, errors are sometimes generated because the assessment of the uniformity of gamma cameras relies on the naked eye of the evaluator. To minimize these errors, we created an artificial intelligence model based on CNN algorithm and wanted to assess its usefulness. We produced 20,000 normal images and partial cold region images using Python, and conducted artificial intelligence training with Resnet18 models. The training results showed that accuracy, specificity and sensitivity were 95.01%, 92.30%, and 97.73%, respectively. According to the results of the evaluation of the confusion matrix of artificial intelligence and expert groups, artificial intelligence was accuracy, specificity and sensitivity of 94.00%, 91.50%, and 96.80%, respectively, and expert groups was accuracy, specificity and sensitivity of 69.00%, 64.00%, and 74.00%, respectively. The results showed that artificial intelligence was better than expert groups. In addition, by checking together with the radiological technologist and AI, errors that may occur during the quality control process can be reduced, providing a better examination environment for patients, providing convenience to radiologists, and improving work efficiency.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.11
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• pp.4467-4474
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• 2014

Background: We aimed to comprehensively review the evidence for using sputum DNA to detect non-small cell lung cancer (NSCLC). Materials and Methods: We searched PubMed, Science Direct, Web of Science, Chinese Biological Medicine (CBM), Chinese National Knowledge Infrastructure (CNKI), Wanfang, Vip Databases and Google Scholar from 2003 to 2013. The meta-analysis was carried out using a random-effect model with sensitivity, specificity, diagnostic odd ratios (DOR), summary receiver operating characteristic curves (ROC curves), area under the curve (AUC), and 95% confidence intervals (CI) as effect measurements. Results: There were twenty-two studies meeting the inclusion criteria for the meta-analysis. Combined sensitivity and specificity were 0.62 (95%CI: 0.59-0.65) and 0.73 (95%CI: 0.70-0.75), respectively. The DOR was 10.3 (95%CI: 5.88-18.1) and the AUC was 0.78. Conclusions: The overall accuracy of the test was currently not strong enough for the detection of NSCLC for clinical application. Dscovery and evaluation of additional biomarkers with improved sensitivity and specificity from studies rated high quality deserve further attention.

• Physical Therapy Korea
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• v.23 no.4
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• pp.63-70
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• 2016

Background: The Pediatric Balance Scale (PBS) and the Fullerton Advanced Balance (FAB) scale were used to assess balance function in patients with balance problem. These multidimensional clinical balance scales provide information about potential risk factors for falls. Objects: The purpose of this study was to investigate and compare the predictive properties of the PBS and FAB scales relative to fall risk in children with cerebral palsy (CP) using a receiver operating characteristic analysis. Methods: In total, 49 children with CP (boy=21, girl=28) who were diagnosed with level 1 or 2 according to the Gross Motor Function Classification System participated in this study. The PBS and FAB were performed, and verified cut-off score, sensitivity, specificity, and the area of under the curve (AUC). Results: In this study, the PBS scale was as a predictive measure of fall risk, but the FAB was not significant in children with CP. A cut-off score of 45.5 points provided optimal sensitivity of .90 and specificity of .69 on the PBS, and a cut-off score of 21.5 points provided optimal sensitivity of .90 and specificity of .62 on the FAB. Both scales showed moderately accurate of AUC with .79 and .76, respectively. Conclusion: The PBS is a useful screening tool for predicting fall risk in children with cerebral palsy, and those who score 45.5 or lower indicate a high risk for falls and are in need of balance intervention.

• Journal of Preventive Medicine and Public Health
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• v.42 no.4
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• pp.223-230
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• 2009

Objectives : This study was conducted to validate self-reported smoking among high school students using urinary cotinine. Methods : A self report of smoking behavior was collected together with urine sample for cotinine analysis from 130 male and female students in two vocational high school students in November, 2007. Validity and agreement between self-reported smoking and urinary cotinine was analyzed with STATA 9.0 for different definitions of current smokers, and frequent and daily smokers. Urinary cotinine concentration was measured by the DRI Cotinine Assay for urine (Microgenics Corp., Fremont, CA) on Toshiba 200FR. The cut-off point of urinary cotinine was 50 ng/dl. Results : The concentrations of urinary cotinine were significantly different according to the frequency and amount of smoking. Sensitivity and specificity was 90.9% and 91.8% respectively, and the Cohen s kappa value was 0.787 among the current smokers who smoked at least one day during one month preceding the survey. The comparable high sensitivity, specificity, and kappa value were shown also among the other definitions of current smokers, that is, subjective smokers, and weekly smokers. Conclusions : The results showed the high validity of self-reported smoking among high school students. However, due to the small sample size and limitation of the participants, it is cautious to generalize the results to overall high school students.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.187-195
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• 2022

Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs(PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model. Results: The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively. Conclusion: The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the "gold standard" for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.