Kidney targeting and puncturing during percutaneous nephrolithotomy: recent advances and future perspectives

Background and Purpose: Precise needle puncture of the kidney is a challenging and essential step for successful percutaneous nephrolithotomy (PCNL). Many devices and surgical techniques have been developed to easily achieve suitable renal access. This article presents a critical review to address the methodologies and techniques for conducting kidney targeting and the puncture step during PCNL. Based on this study, research paths are also provided for PCNL procedure improvement. Methods: Most relevant works concerning PCNL puncture were identified by a search of Medline/PubMed, ISI Web of Science, and Scopus databases from 2007 to December 2012. Two authors independently reviewed the studies. Results: A total of 911 abstracts and 346 full-text articles were assessed and discussed; 52 were included in this review as a summary of the main contributions to kidney targeting and puncturing. Conclusions: Multiple paths and technologic advances have been proposed in the field of urology and minimally invasive surgery to improve PCNL puncture. The most relevant contributions, however, have been provided by the applicationofmedical imaging guidance, newsurgical tools,motion tracking systems, robotics, andimage processing and computer graphics. Despite the multiple research paths for PCNL puncture guidance, no widely acceptable solution has yet been reached, and it remains an active and challenging research field. Future developments should focus on real-time methods, robust and accurate algorithms, and radiation free imaging techniques

Kidney targeting and puncturing during percutaneous nephrolithotomy:recent advances and future perspectives

Precise needle puncture of the kidney is a challenging and essential step for successful percutaneous nephrolithotomy (PCNL). Many devices and surgical techniques have been developed to easily achieve suitable renal access. This article presents a critical review to address the methodologies and techniques for conducting kidney targeting and the puncture step during PCNL. Based on this study, research paths are also provided for PCNL procedure improvement. Methods: Most relevant works concerning PCNL puncture were identified by a search of Medline/PubMed, ISI Web of Science, and Scopus databases from 2007 to December 2012. Two authors independently reviewed the studies.

Preliminary clinical trial in percutaneous nephrolithotomy using a real-time navigation system for percutaneous kidney access

Background: Precise needle puncture of renal calyces is a challenging and essential step for successful percutaneous nephrolithotomy. This work tests and evaluates, through a clinical trial, a real-time navigation system to plan and guide percutaneous kidney puncture. Methods: A novel system, entitled i3DPuncture, was developed to aid surgeons in establishing the desired puncture site and the best virtual puncture trajectory, by gathering and processing data from a tracked needle with optical passive markers. In order to navigate and superimpose the needle to a preoperative volume, the patient, 3D image data and tracker system were previously registered intraoperatively using seven points that were strategically chosen based on rigid bone structures and nearby kidney area. In addition, relevant anatomical structures for surgical navigation were automatically segmented using a multi-organ segmentation algorithm that clusters volumes based on statistical properties and minimum description length criterion. For each cluster, a rendering transfer function enhanced the visualization of different organs and surrounding tissues. Results: One puncture attempt was sufficient to achieve a successful kidney puncture. The puncture took 265 seconds, and 32 seconds were necessary to plan the puncture trajectory. The virtual puncture path was followed correctively until the needle tip reached the desired kidney calyceal. Conclusions: This new solution provided spatial information regarding the needle inside the body and the possibility to visualize surrounding organs. It may offer a promising and innovative solution for percutaneous punctures.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Survival Analysis Employed in Predicting Corporate Failure: A Forecasting Model Proposal

In face of the current economic and financial environment, predicting corporate bankruptcy is arguably a phenomenon of increasing interest to investors, creditors, borrowing firms, and governments alike. Within the strand of literature focused on bankruptcy forecasting we can find diverse types of research employing a wide variety of techniques, but only a few researchers have used survival analysis for the examination of this issue. We propose a model for the prediction of corporate bankruptcy based on survival analysis, a technique which stands on its own merits. In this research, the hazard rate is the probability of ‘‘bankruptcy’’ as of time t, conditional upon having survived until time t. Many hazard models are applied in a context where the running of time naturally affects the hazard rate. The model employed in this paper uses the time of survival or the hazard risk as dependent variable, considering the unsuccessful companies as censured observations.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.