Perioperative anaphylactic risk score for risk-oriented premedication.

Basing on the current knowledge, this paper is aimed to review the core characteristics of the most relevant therapeutic agents (steroids and antihistamines), administered to prevent perioperative anaphylaxis. Moreover, the Authors propose the validation of a Global Anaphylactic Risk Score, built up by recording the individual scores related to the most relevant anaphylaxis parameters (i.e. medical history, symptoms and medication for asthma, rhinitis and urticaria etc) and by adding them on all together; the score could be used in the preoperative phase to evaluate the global anaphylactic risk and to prescribe risk-oriented premedication protocols.

Detection of suspected malignant patterns in three-dimensional magnetic resonance breast images.

In this article, a Boolean Neural Network (BNN) is used for the detection of suspected malignant regions in 3D breast magnetic resonance (MR) images. The BNN is characterized by fast learning and classification, guaranteed convergence, and simple, integer weight calculations. The BNN learning algorithm is incremental, which allows the addition and deletion of training patterns without unlearning those already learned. The incremental learning algorithm automatically reduces the training set and trains the network only with those examples estimated to be useful. The architecture is suitable for parallel hardware implementation using available Very Large Scale Integration (VLSI) technology. The BNN was trained by using a set of malignant, benign, and false-positive patterns, extracted by experts, from selected MR studies, by using an incremental learning algorithm. After training, the network was tested by means of a consistency checking test, cross validation techniques, and patterns from actual MR breast images. During the consistency test, the BNN was tested by using the same patterns used for training. The BNN classification accuracy in this case was 99.75%, proving the ability of the BNN to select useful patterns from the training set. Then, a leave one out cross-validation (LOOCV) test was done by using patterns from the training set and the classification accuracy was 90%. Next, an extended training set was created by shifting the original patterns in different directions. A cross-validation test was then performed by dividing the set of patterns into a training and a test set. Classification accuracy was compared to the nearest neighbor classifier. Results showed that the BNN achieved an average of 77% classification accuracy while requiring only 34% of the original training set. On the other hand, the nearest neighbor classifier achieved an accuracy of 57.9% while retaining the whole training set. Another test using actual MR slices different from the training set was done and results compared favorably to a radiologist's findings. Test results show the BNN's capability to detect suspected malignant regions in 3D MR images of the breast. The proposed BNN architecture can save the radiologist a great deal of time browsing MR slices searching for suspected malignancies.