A low-cost unity-based virtual training simulator for laparoscopic partial nephrectomy using HTC Vive.

Laparoscopic education and surgery assessments increase the success rates and lower the risks during actual surgeries. Hospital residents need a secure setting, and trainees require a safe and controlled environment with cost-effective resources where they may hone their laparoscopic abilities. Thus, we have modeled and developed a surgical simulator to provide the initial training in Laparoscopic Partial Nephrectomy (LPN-a procedure to treat kidney cancer or renal masses). To achieve this, we created a virtual simulator using an open-source game engine that can be used with a commercially available, reasonably priced virtual reality (VR) device providing visual and haptic feedback. In this study, the proposed simulator's design is presented, costs are contrasted, and the simulator's performance is assessed using face and content validity measures. CPU- and GPU-based computers can run the novel simulation with a soft body deformation based on simplex meshes. With a reasonable trade-off between price and performance, the HTC Vive's controlled soft body effect, physics-based deformation, and haptic rendering offer the advantages of an excellent surgical simulator. The trials show that the medical volunteers who performed the initial LPN procedures for newbie surgeons received positive feedback.

Analysis Of Consultation Length In Khyber Pakhtunkhwa, Pakistan.

BACKGROUND: Consultation length is considered as direct measure of quality healthcare service and patient satisfaction. We analysed data collected from five different hospitals to inference the effects of sub-factors on consultation length. These factors have positive contribution in predicting the behaviour of consultation length. METHODS: We performed cross-sectional study on first hand data collected from 386 participants using snow ball sampling method. The survey instrument was questionnaire and face to face interviews. We considered null hypothesis (H0=0) as means are equal against alternative hypothesis (H1 ≠ 0) for factors of time consumed by overall consultation, patient's history, physical examination, and prescription writing. Data was also analysed by nonparametric univariate tests and multiple linear regression model. RESULTS: Mean of consultation length is 22.466 minutes [CI: 21.420-23.512 and α=0.01]. Null hypothesis (H0=0) was rejected in favour of alternative hypothesis (H1≠0) by all factors due to sufficient evidence in data except prescription writing which failed to reject H0. CONCLUSIONS: We found factors had high spread in mean values and rejected null hypothesis indicating the duration of health workforces' consultation is varying in different setups. Multiple factors contributed in formation of consultation length of doctors. Similar studies related to conservation of variation in consultation length must consider these factors. Eventually, such studies reporting this variation and its factors will add up in its efficacy and provisioning of appropriate consultation time totting up in patient's satisfaction positively.

Dysregulated Alanine as a Potential Predictive Marker of Glioma-An Insight from Untargeted HRMAS-NMR and Machine Learning Data.

Metabolic alterations play a crucial role in glioma development and progression and can be detected even before the appearance of the fatal phenotype. We have compared the circulating metabolic fingerprints of glioma patients versus healthy controls, for the first time, in a quest to identify a panel of small, dysregulated metabolites with potential to serve as a predictive and/or diagnostic marker in the clinical settings. High-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS-NMR) was used for untargeted metabolomics and data acquisition followed by a machine learning (ML) approach for the analyses of large metabolic datasets. Cross-validation of ML predicted NMR spectral features was done by statistical methods (Wilcoxon-test) using JMP-pro16 software. Alanine was identified as the most critical metabolite with potential to detect glioma with precision of 1.0, recall of 0.96, and F1 measure of 0.98. The top 10 metabolites identified for glioma detection included alanine, glutamine, valine, methionine, N-acetylaspartate (NAA), γ-aminobutyric acid (GABA), serine, α-glucose, lactate, and arginine. We achieved 100% accuracy for the detection of glioma using ML algorithms, extra tree classifier, and random forest, and 98% accuracy with logistic regression. Classification of glioma in low and high grades was done with 86% accuracy using logistic regression model, and with 83% and 79% accuracy using extra tree classifier and random forest, respectively. The predictive accuracy of our ML model is superior to any of the previously reported algorithms, used in tissue- or liquid biopsy-based metabolic studies. The identified top metabolites can be targeted to develop early diagnostic methods as well as to plan personalized treatment strategies.

Cleft prediction before birth using deep neural network.

In developing countries like Pakistan, cleft surgery is expensive for families, and the child also experiences much pain. In this article, we propose a machine learning-based solution to avoid cleft in the mother's womb. The possibility of cleft lip and palate in embryos can be predicted before birth by using the proposed solution. We collected 1000 pregnant female samples from three different hospitals in Lahore, Punjab. A questionnaire has been designed to obtain a variety of data, such as gender, parenting, family history of cleft, the order of birth, the number of children, midwives counseling, miscarriage history, parent smoking, and physician visits. Different cleaning, scaling, and feature selection methods have been applied to the data collected. After selecting the best features from the cleft data, various machine learning algorithms were used, including random forest, k-nearest neighbor, decision tree, support vector machine, and multilayer perceptron. In our implementation, multilayer perceptron is a deep neural network, which yields excellent results for the cleft dataset compared to the other methods. We achieved 92.6% accuracy on test data based on the multilayer perceptron model. Our promising results of predictions would help to fight future clefts for children who would have cleft.

Important citation identification by exploiting content and section-wise in-text citation count.

A citation is deemed as a potential parameter to determine linkage between research articles. The parameter has extensively been employed to form multifarious academic aspects like calculating the impact factor of journals, h-Index of researchers, allocate different research grants, find the latest research trends, etc. The current state-of-the-art contends that all citations are not of equal importance. Based on this argument, the current trend in citation classification community categorizes citations into important and non-important reasons. The community has proposed different approaches to extract important citations such as citation count, context-based, metadata, and textual based approaches. The contemporary state-of-the-art in citation classification community ignores significantly potential features that can play a vital role in citation classification. This research presents a novel approach for binary citation classification by exploiting section-wise in-text citation frequencies, similarity score, and overall citation count-based features. The study also introduces machine learning algorithms based novel approach for assigning appropriate weights to the logical sections of research papers. The weights are allocated to the citations with respect to their sections. To perform the classification, we used three classification techniques, Support Vector Machine, Kernel Linear Regression, and Random Forest. The experiment was performed on two annotated benchmark datasets that contain 465 and 311 citation pairs of research articles respectively. The results revealed that the proposed approach attained an improved value of precision (i.e., 0.84 vs 0.72) from contemporary state-of-the-art approach.