Evaluation of various nephrometric scoring system for nephrolithiasis to predict stone free status after percutaneous nephrolithotomy: A comparative study.

BACKGROUND: The CROES Nephrolithometry nomogram, S.T.O.N.E. Nephrolithometry Score and Guy's stone score were introduced for stratification of kidney stones disease on the basis of quantitative stone burden and its distribution. Till date there has been very limited data on head to head comparison of the existing scoring systems. Comparison and analyses among the scoring system helps in further refinement of these systems along with development of new more effective and broadly acceptable nomogram. OBJECTIVE: Predictability of the stone-free status (SFS) and post-operative complication after PCNL by various scoring systems (The CROES nomogram, S.T.O.N.E. nephrolithometry score and Guy's stone score). MATERIALS AND METHODS: Total 100 adult patients underwent PCNL after considering inclusion and exclusion criteria. All patients underwent Preoperative NCCT scan, investigations of blood (Hb%, PCV, bleeding and coagulation profile, urea, and creatinine), and urine (RE/ME and C/S), Postoperative X ray KUB/NCCT. RESULTS: ROC curves were developed for each scoring system to determine the accuracy to predict stone free status. We found CS had significantly higher AUC than other scoring systems [p-value for CS vs GSS = 0.0091 & CS vs SS = 0.000]. So CS has higher accuracy to predict stone free status. None of the scoring system had shown significantly higher AUC than other scoring system in predicting complication. CONCLUSION: CROES Nephrolithometry nomogram is most accurate to predict preoperative stone-free rate. All scoring systems can equally predict perioperative complications and other variables.

Augmenting Neuromuscular Disease Detection Using Optimally Parameterized Weighted Visibility Graph.

In this contribution, we propose a novel neuromuscular disease detection framework employing weighted visibility graph (WVG) aided analysis of electromyography signals. WVG converts a time series into an undirected graph, while preserving the signal properties. However, conventional WVG is sensitive to noise and has high computational complexity which is problematic for lengthy and noisy time series analysis. To address this issue in this article, we investigate the performance of WVG by varying two important parameters, namely penetrable distance and scale factor, both of which have shown promising results by eliminating the problem of signal adulteration and decreasing the computational complexity, respectively. We also aim to unfold the combined effect of these two aforesaid parameters on the WVG performance to discriminate between myopathy, amyotrophic lateral sclerosis (ALS) and healthy EMG signals. Using graph theory based features we demonstrated that the discriminating capability between the three classes increased significantly with the increase in both penetrable distance and scale factor values. Three binary (healthy vs. myopathy, myopathy vs. ALS and healthy vs. ALS) and one multiclass problems (healthy vs. myopathy vs. ALS) have been addressed in this study and for each problem, we obtained optimum parameter values determined on the basis of F-value computed using one way analysis of variance (ANOVA) test. Using optimal parameter values, we obtained mean accuracy of 98.57%, 98.09% and 99.45%, respectively for three binary and 99.05% for the multi-class classification problem. Additionally, the computational time was reduced by 96% with optimally selected WVG parameters compared to traditional WVG.

Neuromuscular Disease Detection Employing Deep Feature Extraction from Cross Spectrum Images of Electromyography Signals.

In this paper, a deep learning framework for detection and classification of EMG signals for diagnosis of neuromuscular disorders is proposed employing cross wavelet transform. Cross wavelet transform which is a modification of continuous wavelet transform is an important tool to analyze any non-stationary signal in time scale and in time-frequency frame. To this end, EMG signals of healthy, myopathy and Amyotrophic lateral sclerosis disorders were procured from an online existing database. A healthy EMG signal was chosen as reference and cross wavelet transform of the rest of the healthy as well as the disease EMG signals was done with the reference. From the resulting cross wavelet spectrum images of EMG signals, a convolution neural network (CNN) based automated deep feature extraction technique was implemented. The extracted deep features were further subjected to feature ranking employing one way analysis of variance (ANOVA) test. The extracted deep features with high degree of statistical significance were fed to several benchmark machine learning classifiers for the purpose of discrimination of EMG signals. Two binary classification problems are addressed in this paper and it has been observed that the highest mean classification accuracy of 100% is achieved using the statistically significant extracted deep features. The proposed method can be implemented for real-time detection of neuromuscular disorders.