In this article, a GUI system is proposed to support clinical cardiology examinations. The proposed system estimates “pulmonary artery wedge pressure” based on patients' chest radiographs using an explainable regression-based convolutional neural network. The GUI system was validated by performing an effectiveness survey with 23 cardiology physicians with medical licenses. The results indicated that many physicians considered the GUI system to be effective.

Given a network G(V,E), a lightweight method to calculate overlaid origin-destination (O-D) traffic flows on all edges is developed. Each O-D trip shall select the shortest path. While simple implementations for single-source/all-destination and all-pair trips need O(L·n) and O(L·n2) in worst-case time complexity, respectively, our technique is executed with O(m+n) and O(m+n2), where n=|V|, m=|E|, and L represents the maximum arc length. This improvement is achieved by reusing outcomes of priority queue-based algorithms. Using a GIS dataset of a road network in Tokyo, Japan, the effectiveness of our technique is confirmed.