Occasionally, surgeons do need various types of information to be available rapidly, efficiently and safely during surgical procedures. Meanwhile, they need to free up hands throughout the surgery to necessarily access the mouse to control any application in the sterility mode. In addition, they are required to record audio as well as video files, and enter and save some data. This is an attempt to develop a comprehensive operating room information system called "Medinav" to tackle all mentioned issues. An integrated and comprehensive operating room information system is introduced to be compatible with Health Level 7 (HL7) and digital imaging and communications in medicine (DICOM). DICOM is a standard for handling, storing, printing, and transmitting information in medical imaging. Besides, a natural user interface (NUI) is designed specifically for operating rooms where touch-less interactions with finger and hand tracking are in use. Further, the system could both record procedural data automatically, and view acquired information from multiple perspectives graphically. A prototype system is tested in a live operating room environment at an Iranian teaching hospital. There are also contextual interviews and usability satisfaction questionnaires conducted with the "MediNav" system to investigate how useful the proposed system could be. The results reveal that integration of these systems into a complete solution is the key to not only stream up data and workflow but maximize surgical team usefulness as well. It is now possible to comprehensively collect and visualize medical information, and access a management tool with a touch-less NUI in a rather quick, practical, and harmless manner.

In this paper, a fuzzy expert system based on adaptive neuro-fuzzy inference system (ANFIS) is introduced to assess the mortality after coronary bypass surgery. In preprocessing phase, the attributes were reduced using a univariant analysis in order to make the classifier system more effective. Prognostic factors with a p-value of less than 0.05 in chi-square or t-student analysis were given to inputs ANFIS classifier. The correct diagnosis performance of the proposed fuzzy system was calculated in 824 samples. To demonstrate the usefulness of the proposed system, the study compared the performance of fuzzy system based on ANFIS method through the binary logistic regression with the same attributes. The experimental results showed that the fuzzy model (accuracy: 96.4%; sensitivity: 66.6%; specificity: 97.2%; and area under receiver operating characteristic curve: 0.82) consistently outperformed the logistic regression (accuracy: 89.4%; sensitivity: 47.6%; specificity: 89.4%; and area under receiver operating characteristic curve: 0.62). The obtained classification accuracy of fuzzy expert system was very promising with regard to the traditional statistical methods to predict mortality after coronary bypass surgery such as binary logistic regression model.

Cardiac events could be taken into account as the leading causes of death throughout the globe. Such events also trigger an undesirable increase in what treatment procedures cost. Despite the giant leaps in technological development in heart surgery, coronary surgery still carries the high risk of the mortality. Besides, there is still a long way ahead to accurately predict and assess the mortality risk. This study is an attempt to develop an expert system for the risk assessment of mortality following the cardiac surgery. The developed system involves three main steps. In the first step, a filtering feature selection method is applied to select the best features. In the second step, an ad hoc data-driven method is utilized to generate the preliminary fuzzy inference system. Finally, a hybrid optimization method is presented to select the optimum subset of the rules. The study relies on 1,811 samples to evaluate the diagnosis performance of the proposed system. The obtained classification accuracy is very promising with regard to other benchmark classification methods including binary logistic regression (LR) and multilayer perceptron neural network (MLP) with the same attributes. The developed system leads to 100% sensitivity and 84.7% specificity, while LR and MLP methods statistically come up with lower figures (65, 78.6 and 65%, 75.8%), respectively. Now, a fuzzy supportive tool can be potentially taken as an alternative for the current mortality risk assessment system that are applied in coronary surgeries, and are chiefly based on crisp database.