Purpose: Dehydration is a paediatric medical emergency but there is no single standard parameter to evaluate it at the emergency department. Our aim was to evaluate the reliability and validity of capillary refilling time as a triage parameter to assess dehydration in children. Methods: This was a prospective pilot cohort study of children who presented to two paediatric emergency departments in Italy, with symptoms of dehydration. Reliability was assessed by comparing the triage nurse's measurements with those obtained by the physician. Validity was demonstrated by using 6 parameters suggestive of dehydration. Comparison between refilling time (RT) and a validated Clinical Dehydration Score (CDS) was also considered. The scale's discriminative ability was evaluated for the outcome of starting intravenous rehydration therapy by using a receiver operating characteristic (ROC) curve. Results: Participants were 242 children. All nurses found easy to elicit the RT after being trained. Interobserver reliability was fair, with a Cohen's kappa of 0.56 (95% confidence interval [CI], 0.41 to 0.70). There was a significant correlation between RT and weight loss percentage (r-squared=-0.27; 95% CI, -0.47 to -0.04). The scale's discriminative ability yielded an area under the ROC curve (AUC) of 0.65 (95% CI, 0.57 to 0.73). We found a similarity between RT AUC and CDS-scale AUC matching the two ROC curves. Conclusion: The study showed that RT represents a fast and handy tool to recognize dehydrated children who need a prompt rehydration and may be introduced in the triage line-up.
The Journal of the Institute of Internet, Broadcasting and Communication
/
v.20
no.4
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pp.101-106
/
2020
In the current medical field, ultrasound diagnosis can be said to be the same as a stethoscope in the past. However, due to the nature of ultrasound, it has the disadvantage that the prediction of results is uncertain depending on the skill level of the examiner. Therefore, this paper aims to improve the accuracy of liver lesion detection during ultrasound examination based on deep learning technology to solve this problem. In the proposed paper, we compared the accuracy of lesion classification using a CNN model and an ensemble model. As a result of the experiment, it was confirmed that the classification accuracy in the CNN model averaged 82.33% and the ensemble model averaged 89.9%, about 7% higher. Also, it was confirmed that the ensemble model was 0.97 in the average ROC curve, which is about 0.4 higher than the CNN model.
To evaluate the diagnostic accuracy of two commercial ELISA tests (Allied- and CSL-ELISA) for the diagnosis of Mycobacterium paratuberculosis in cattle, Meta-analysis using English language papers published during 1990-2001 was performed. Diagnostic odds ratios (DOR) were analyzed using regression analysis together with summary receiver operating characteristic (ROC) curves. The difference in diagnostic performance between the two ELISA systems was evaluated by using linear regression. Publication bias was assessed by funnel plot and linear regression. The pooled sensitivity and specificity were 44% (95% CI, 38 to 51) and 98% (95% CI, 96 to 99) for the random-effect model. The DOR between studies was heterogeneous. The area under the fitted ROC curve (AUC) was 0.72 for the unweighted and 0.77 for the weighted model. Maximum joint sensitivity and specificity for the unweighted and weighted model from their summary ROC curve were 70% and 75%, respectively. Based on the fitted model, at a specificity of 95%, sensitivity was estimated to be 52% for the unweighted and 57% for the weighted model. From the final multivariable model study characteristic, the country was the only significant variable with an explained component variance of 13.3%. There were no significant differences in discriminatory power, sensitivity, and specificity between the two ELISA tests. The overall diagnostic accuracy of two commercial ELISA tests was moderate, as judged by the AUC, maximum joint sensitivity and specificity, and estimates from the fitted model and clinical usefulness of the tests for screening program is limited because of low sensitivity and heterogeneous of DOR. It is, therefore, recommended to use ELISA tests as a parallel testing with other diagnostic tests together to increase test sensitivity in the screening program.
Youm, Jung Hyun;Chung, Yoona;Yang, You Jung;Han, Sang Ah;Song, Jeong Yoon
Korean Journal of Clinical Oncology
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v.14
no.2
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pp.135-141
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2018
Purpose: Axillary lymph node dissection (ALND) and sentinel lymph node biopsy (SLNB) are important for staging of patients with node-positive breast cancer. However, these can be avoided in select micrometastatic diseases, preventing postoperative complications. The present study evaluated the ability of axillary lymph node maximum standardized uptake value (SUVmax) on positron emission tomography-computed tomography (PET-CT) to predict axillary metastasis of breast cancer. Methods: The records of invasive breast cancer patients who underwent pretreatment (surgery and/or chemotherapy) PET-CT between January 2006 and December 2014 were reviewed. ALNs were preoperatively evaluated by PET-CT. Lymph nodes were dissected by SLNB or ALND. SUVmax was measured in both the axillary lymph node and primary tumor. Student t-test and chi-square test were used to analyze sensitivity and specificity. Receiver operating characteristic (ROC) and area under the ROC curve (AUC) analyses were performed. Results: SUV-tumor (SUV-T) and SUV-lymph node (SUV-LN) were significantly higher in the triple-negative breast cancer (TNBC) group than in other groups (SUV-T: 5.99, P<0.01; SUV-LN: 1.29, P=0.014). The sensitivity (0.881) and accuracy (0.804) for initial ALN staging were higher in fine needle aspiration+PET-CT than in other methods. For PET-CT alone, the subtype with the highest sensitivity (0.870) and negative predictive value (0.917) was TNBC. The AUC for SUV-LN was greatest in TNBC (0.797). Conclusion: The characteristics of SUV-T and SUV-LN differed according to immunohistochemistry subtype. Compared to other subtypes, the true positivity of axillary metastasis on PET-CT was highest in TNBC. These findings could help tailor management for therapeutic and diagnostic purposes.
This paper proposes a method for detecting malicious domains considering human habitual characteristics by building a Deep Learning model based on LSTM (Long Short-Term Memory). DGA (Domain Generation Algorithm) malicious domains exploit human habitual errors, resulting in severe security threats. The objective is to swiftly and accurately respond to changes in malicious domains and their evasion techniques through typosquatting to minimize security threats. The LSTM-based Deep Learning model automatically analyzes and categorizes generated domains as malicious or benign based on malware-specific features. As a result of evaluating the model's performance based on ROC curve and AUC accuracy, it demonstrated 99.21% superior detection accuracy. Not only can this model detect malicious domains in real-time, but it also holds potential applications across various cyber security domains. This paper proposes and explores a novel approach aimed at safeguarding users and fostering a secure cyber environment against cyber attacks.
Purpose: We investigated whether the diagnostic performance of SPECT scintimammography (SMM) can be improved by adding computer-aided diagnosis (CAD) of ultrasonography (US). Materials and methods: We reviewed breast SPECT SMM images and corresponding US images from 40 patients with breast masses (21 malignant and 19 benign tumors). The quantitative data of SPECT SMM were obtained as the uptake ratio of lesion to contralateral normal breast. The morphologic features of the breast lesions on US were extracted and quantitated using the automated CAD software program. The diagnostic performance of SPECT SMM and CAD of US alone was determined using receiver operating characteristic (ROC) curve analysis. The best discriminating parameter (D-value) combining SPECT SMM and the CAD of US was created. The sensitivity, specificity and accuracy of combined two diagnostic modalities were compared to those of a single one. Results: Both SPECT SMM and CAD of US showed a relatively good diagnostic performance (area under curve = 0.846 and 0.831, respectively). Combining the results of SPECT SMM and CAD of US resulted in improved diagnostic performance (area under curve =0.860), but there was no statistical differerence in sensitivity, specificity and accuracy between the combined method and a single modality. Conclusion: It seems that combining the results of SPECT SMM and CAD of breast US do not significantly improve the diagnostic performance for diagnosis of breast cancer, compared with that of SPECT SMM alone. However, SPECT SMM and CAD of US may complement each other in differential diagnosis of breast cancer.
This study aimed to classify normal and nodule images in thyroid ultrasound images using GLCM and machine learning. The research was conducted on 600 patients who visited S Hospital in Busan and were diagnosed with thyroid nodules using thyroid ultrasound. In the thyroid ultrasound images, the ROI was set to a size of 50x50 pixels, and 21 parameters and 4 angles were used with GLCM to analyze the normal thyroid patterns and thyroid nodule patterns. The analyzed data was used to distinguish between normal and nodule diagnostic results using the SVM model and KNN model in MATLAB. As a result, the accuracy of the thyroid nodule classification rate was 94% for SVM model and 91% for the KNN model. Both models showed an accuracy of over 90%, indicating that the classification rate is excellent when using machine learning for the classification of normal thyroid and thyroid nodules. In the ROC curve, the ROC curve for the SVM model was generally higher compared to the KNN model, indicating that the SVM model has higher within-sample performance than the KNN model. Based on these results, the SVM model showed high accuracy in diagnosing thyroid nodules. This result can be used as basic data for future research as an auxiliary tool for medical diagnosis and is expected to contribute to the qualitative improvement of medical services through machine learning technology.
Background: A perioperative myocardial infarction(PMI) is one of the major complications after CABG. Among diagnostic methods of PMI, CK-MB activity assays have been increasingly replaced by CK-MB mass assays, which have more sensitive, simple measurement. Also, new cardiac-specific and -sensitive marker, cardiac troponin I(cTnl), has been shown to be a marker of myocardial infarction. We report our evaluation of clinical significance of CK-MB mass and cTnl as a marker of PMI after CABG. Material and Method: We studied 32 patients who underwent CABG at Kangdong Sacred Hospital between April 2000 and April 2001. Postoperative serum CK-MB activity level, serum CK-MB mass, cTnl, electrocardiogram, echocardiogram, and clinical data were recorded prospectively The diagnosis of PMI was defined as positive 2 among 3 or all of the following , by a new Q wave on the electrocardiogram, by serum CK-MB activity higher than 200 lU/L within 72 hours after operation, and by new regional wall motion abnormality on the echocardiogram. Result: After CABG, 3 patients had sustained a PMI according to current diagnostic criteria. As serum CK-MB activity time course, a level of CK-MB activity 12 hours after CABG had very linear correlated significance with serum CK-MB mass 24hours(R=0.946) and cTnl 48 hours(R=0.933) after CABG(p=0.000). As we used a receiver operating characteristics curve(ROC curve) for a diagnostic cutoff value in patients with PMI, serum CK-MB mass levels higher than 30.05 ug/L 24 hours after CABG detected the presence of PMI with an area under the ROC curve of 1.0, a sensitivity of 100%, a specificity of 100%, a positive predictive value of 100%, and a negative predictive value of 100%. Also serum cTnl levels higher than 17.15 ug/L 48 hours after CABG detected the presence of PMI with an area under the ROC curve of 0.98, a sensitivity of 100%, a specificity of 96.6%, a positive preclictive value of 75%, and a negative predictive value of 100% Conclusion: We concluded that both the measurement of CK-MB mass and cTnl are the easier, accurate methods as a diagnostic marker of PMT after CABG, also as a proposal of diagnostic cutoff value enables to an early detection of PMI. However, a 1arger number of patient will be needed because of statistic limitation that a small number of participating patients, a small number of PMI.
This study was attempted to investigate the discriminant validity of Korean version of Eating Attitude Test-26(KEAT-26) and to provide the sensitivity, specificity and efficiency according to cutting score, which may be useful to determine the optimal cutoff point on various purposes. The KEAT-26 was administered to 108 female patients with eating disorders, 179 female participants in body slimming center, 120 female athletic college students, 227 female college students, and 183 healthy normal women. Validity was tested by ANOVA and ROC curve analysis. The results revealed that the total score of the KEAT-26 showed a statistically significance between groups and that the score of the KEAT-26 of eating disorders group was significantly higher than that of the other groups in post hoc test. In comparison of the 4 subfactor score of the KEAT-26 between groups, significant differences in main effect within groups were found in all subfactors except factor IV. ROC curve analysis showed 80% of efficiency to discriminate eating disorders group from normal control group using cutoff score on maximum discriminant efficiency and 69% of efficiency to discriminate eating disorders group from high risk groups for eating disorders. Each cutoff score on maximum in efficiency was as follows ; 25 between eating disorders group and participants in body slimming center, 19 between eating disorders group and healthy normal woman, 23 between eating disorders group and athletic college students, 21 between eating disorders group and college students. Using 22(T score 65) of the KEAT-26 as the cutoff score, sensitivity was 54%, specificity was 84%, and overall efficiency was 80%. These results indicate that the KEAT-26 has a good discriminant validity in Korean population and also suggest that the KEAT-26 may be useful assessment tool to screen the disordered eating problems on clinical and epidemiological purposes.
Background: This study used receiver operating characteristic (ROC) curves to screen Surveillance, Epidemiology and End Results (SEER) skin melanoma data to identify and quantify the effects of socioeconomic factors on cause specific survival. Methods: 'SEER cause-specific death classification' used as the outcome variable. The area under the ROC curve was to select best pretreatment predictors for further multivariate analysis with socioeconomic factors. Race and other socioeconomic factors including rural-urban residence, county level % college graduate and county level family income were used as predictors. Univariate and multivariate analyses were performed to identify and quantify the independent socioeconomic predictors. Results: This study included 49,999 parients. The mean follow up time (SD) was 59.4 (17.1) months. SEER staging (ROC area of 0.08) was the most predictive foctor. Race, lower county family income, rural residence, and lower county education attainment were significant univariates, but rural residence was not significant under multivariate analysis. Living in poor neighborhoods was associated with a 2-4% disadvantage in actuarial cause specific survival. Conclusions: Racial and socioeconomic factors have a significant impact on the survival of melanoma patients. This generates the hypothesis that ensuring access to cancer care may eliminate these outcome disparities.
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