Standard Deviation Area Research Articles

Meshing Centroidal Voronoi Tessellation with spatial statistics to optimize waste management regions

Regionalization is of great practical importance, being applied in fields ranging from air space to water distribution. Canada has one of the highest waste generation rates in the world, disposing an average of 694.4 kg/cap in 2018. Waste management costed Canadians about CAD $3.3 billion in 2016, with collection and transportation making up 41.3% of this expenditure. Regionalized waste management systems are becoming popular in Canada and around the world; and design and optimization of regions is vital. Different spatial statistics are used to optimize 3 sub-regions in Saskatchewan (Regina, Leinan, and Beatty). Centroidal Voronoi Tessellation is generally the most efficient method. When the ratio of populated places to road length is low (0.010), ‘median center’ better optimized populated places in each sub-region. Computational demand was highest in Beatty (19.0 iterations), followed by Regina (9.67 iterations) and Leinan (5.33 iterations) indicating a trade-off between the number of sub-regions, computational efficiency, and optimized percent standard deviation. A strong linear relationship is observed in some cases between percent SD of optimized parameters and standard deviation of area, indicating the importance of relative size in optimization. In Regina, Median center (rank = 2) was ranked higher than mean center (rank = 3), while the opposite was true in Beatty. Feature distribution within regions and different spatial statistics in the algorithm should be explored. This work is of practical importance in waste management and other fields where region development is important.

The Elbow UCL Injury Prognosis Score

Objectives: No model exists to predict which patients with elbow ulnar collateral ligament (UCL) injuries will successfully return to play (RTP) with non-operative treatment. The reported rates for successful RTP after non-operative management of UCL injuries are limited and vary widely. Furthermore, patient and UCL injury characteristics that influence failure of non-operative treatment have not been established. We sought to identify which factors were most significant in predicting which patients would have successful RTP after non-operative management of UCL injury. In doing so, we created the Elbow UCL Injury Prognosis Score to identify and weigh factors that predict failure of non-operative treatment. The parameters include: age, position, competition level, moving valgus stress test (MVST), 5th finger sensation, UCL tenderness, and MRI findings including tear type, location, and chronicity features. This score will predict which patients will succeed non-operative management, avoiding unnecessary surgery while simultaneously identify patients for whom non-operative management would be delaying the inevitable need for surgical intervention. Methods: 205 patients were evaluated by the senior author for a UCL injury. Physical exam findings, imaging characteristics and patient demographics were retrospectively collected and entered into a HIPAA-compliant REDCap database after IRB approval of the study design. Of these patients, 138 underwent a trial of non-operative treatment and were either cleared for RTP (n= 55) or eventually underwent surgical intervention (n= 83) after a minimum one-month trial of non-operative management. Sixty-seven patients decided to have surgery immediately without trialing non-operative management and were excluded from the scoring system. The Elbow UCL Injury Prognosis Score was developed and validated using stepwise feature selection by Akaike information criterion (AIC). Three-fold cross validation was performed using these ten variables as predictors. The mean and standard deviation of area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value were evaluated. Results: The area under curve for non-operative failure predicting non-operative management failure was 0.79 (0.054), with sensitivity=0.85 (0.088), specificity=0.72 (0.032), positive predictive value=0.83 (0.023), negative predictive value=0.77 (0.091) [mean (standard deviation (SD))]. Patients were predicted to succeed non-operative management if they scored less than 10, while for a score greater than or equal to 10 patients would likely fail non-operative management and require surgical intervention for their UCL injury (Table 1). Conclusion: The Elbow UCL Injury Prognosis Score is a statistically rigorous, powerful tool for predicting which patients will most likely RTP with non-operative treatment. We hope this score can provide surgeons with a useful tool to enhance and enable shared decision making with patients when faced with UCL injuries at all competitive levels. [Table: see text]

Machine Learning for the Prediction of New-Onset Diabetes Mellitus during 5-Year Follow-up in Non-Diabetic Patients with Cardiovascular Risks.

PurposeMany studies have proposed predictive models for type 2 diabetes mellitus (T2DM). However, these predictive models have several limitations, such as user convenience and reproducibility. The purpose of this study was to develop a T2DM predictive model using electronic medical records (EMRs) and machine learning and to compare the performance of this model with traditional statistical methods.Materials and MethodsIn this study, a total of available 8454 patients who had no history of diabetes and were treated at the cardiovascular center of Korea University Guro Hospital were enrolled. All subjects completed 5 years of follow up. The prevalence of T2DM during follow up was 4.78% (404/8454). A total of 28 variables were extracted from the EMRs. In order to verify the cross-validation test according to the prediction model, logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and K-nearest neighbor (KNN) algorithm models were generated. The LR model was considered as the existing statistical analysis method.ResultsAll predictive models maintained a change within the standard deviation of area under the curve (AUC) <0.01 in the analysis after a 10-fold cross-validation test. Among all predictive models, the LR learning model showed the highest prediction performance, with an AUC of 0.78. However, compared to the LR model, the LDA, QDA, and KNN models did not show a statistically significant difference.ConclusionWe successfully developed and verified a T2DM prediction system using machine learning and an EMR database, and it predicted the 5-year occurrence of T2DM similarly to with a traditional prediction model. In further study, it is necessary to apply and verify the prediction model through clinical research.

Design of a practical model-observer-based image quality assessment method for x-ray computed tomography imaging systems.

The use of a channelization mechanism on model observers not only makes mimicking human visual behavior possible, but also reduces the amount of image data needed to estimate the model observer parameters. The channelized Hotelling observer (CHO) and channelized scanning linear observer (CSLO) have recently been used to assess CT image quality for detection tasks and combined detection/estimation tasks, respectively. Although the use of channels substantially reduces the amount of data required to compute image quality, the number of scans required for CT imaging is still not practical for routine use. It is our desire to further reduce the number of scans required to make CHO or CSLO an image quality tool for routine and frequent system validations and evaluations. This work explores different data-reduction schemes and designs an approach that requires only a few CT scans. Three different kinds of approaches are included in this study: a conventional CHO/CSLO technique with a large sample size, a conventional CHO/CSLO technique with fewer samples, and an approach that we will show requires fewer samples to mimic conventional performance with a large sample size. The mean value and standard deviation of areas under ROC/EROC curve were estimated using the well-validated shuffle approach. The results indicate that an 80% data reduction can be achieved without loss of accuracy. This substantial data reduction is a step toward a practical tool for routine-task-based QA/QC CT system assessment.

Morphometric analysis of the intimal reaction after stent implantation in iliac arteries submitted to angioplasty in pigs

To compare through digital morphometry, the intimal thickening of the common ilíac arteries (CIA) in pigs, submitted to angioplasty and angioplasty followed by stent implantation. Angioplasty was performed in 10 pigs in both CIA, followed by a stent implantation in the left CIA. After four weeks, the aorto-iliac segment was dissected. Histologic slices where divided in three groups: left CIA in the area of the proximal (group 1) and distal (group 2) implantation site of the stent and the region of angioplasty in the right CIA (group 3). The histological slices were digitalized to be analyzed by morphometry with calculation of the luminal, intimal and media layers areas. Descriptive statistics analysis was performed through average and standard deviation of areas on each group, using ANOVA, with LSD Post-Hoc test (p<0.05). A significant statistic difference was observed when the median area of intimal layer of the groups 1 and 2 where compared with group 3 and in the media layer area of the groups 1 and 2 when compared with group 3. No difference was observed in luminal areas among the three groups. Angioplasty followed by stent insertion produces an intimal thickening larger than that observed after simple angioplasty. However, the area of the media layer is smaller in the "angioplasty plus stent" group and there is no significant change in the luminal area among the three groups.

A New Method of Grading Malignancy of Prostate Carcinoma Using Quantitative Microscopic Nuclear Features

A morphometrical assessment of nuclear features and a DNA study were performed on prostate tissue specimens from 33 patients with prostate carcinoma using an image analysing computer. Six nuclear geometric variables were measured and their mean, standard deviation (SD) and standard error (SE) were calculated for each case. The data on nuclear DNA content obtained by static cytometry were processed using an algorithm which provided a DNA grade of malignancy (DNA MG). Using the stepwise multiple regression, we found a significant correlation (p less than 0.01) between the DNA MG, chosen as the dependent variable in the statistical model, and the following nuclear features in decreasing order of importance: area SD, convex perimeter SE, and the mean of maximum diameter. From the correlation coefficients of the variables an equation was built up which provided a geometric nuclear grade of malignancy (GNMG) on a morphometrical basis more closely related to the clinical stage of the tumour (r = 0.75) than the visually assessed histological grade (r = 0.68) based on the Gleason score. This new method of grading malignancy allows an objective and quantitative evaluation to be made of the biological behaviour of the tumour, as measured by the patient's clinical stage.