Background As a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly. Objective The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications. Methods The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software. Results The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019. Conclusions This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

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ObjectiveThe purpose of this work was to analyze and compare the movement kinematics of sit-to-stand (STS) and back-to-sit (BTS) transfers between frail aged adults and young subjects, as well as to determine the relationship between kinematic changes and functional capacities.MethodsWe analyzed the Timed Up and Go (TUG) movements by using a 3D movement analysis system for real-time balance assessment in frail elderly. Ten frail aged adults (frail group [FG]) and ten young subjects (young group [YG]) performed the TUG. Seven spatiotemporal parameters were extracted and compared between the two groups. Moreover, these parameters were plotted with TUG test duration.ResultsThe experiments revealed that there were significant differences between FG and YG in trunk angle during both STS and BTS, and in TUG duration. The trunk angle of the young subjects was more than two times higher than that of the FG. As expected, the TUG duration was higher in the FG than in YG. Trunk angles during both transfers were the most different parameters between the groups. However, the BTS trunk angle and STS ratio were more linked to functional capacities.ConclusionThere was a relationship between kinematic changes, representing the motor planning strategies, and physical frailty in these aged adults. These changes should be taken into account in clinical practice.

The visualization of the coronary vasculature is of utmost importance in interventional cardiology. Intravascular surgical robots assist the practitioners to perform the complex procedure while protecting them from the tremendous occupational hazards. Robotic surgical simulation aims to provide support for the learners in both efficiency and convenience. The blood vessels especially the coronary arteries with rich details are the key part of the anatomic scenario of the virtual training system. The variations in diameters and directions make the segmentation of the coronary arteries a difficult work. In this paper, a robust and semi-automatic approach for the segmentation of the coronary arteries is developed. The approach is based on the multi-scale tubular enhancement and an improved geodesic active contours model. The demonstrated approach firstly enhances the tubular objects by computing their "vesselness". Next the edge potential maps are calculated based on the enhanced information. Meanwhile, the initial contours are generated by a modified fast marching method. Then the actual wave fronts evolution extracts the details of the coronary arteries. Finally the visualization model is organized based on the segmentation results by the marching cubes method. This approach has been proved successful for the visualization of the coronary arteries based on the CTA information.

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