The cost of diagnostic errors has been high in the developed world economics according to a number of recent studies and continues to rise. Up till now, a common process of performing image diagnostics for a growing number of conditions has been examination by a single human specialist (i.e., single-channel recognition and classification decision system). Such a system has natural limitations of unmitigated error that can be detected only much later in the treatment cycle, as well as resource intensity and poor ability to scale to the rising demand. At the same time Machine Intelligence (ML, AI) systems, specifically those including deep neural network and large visual domain models have made significant progress in the field of general image recognition, in many instances achieving the level of an average human and in a growing number of cases, a human specialist in the effectiveness of image recognition tasks. The objectives of the AI in Medicine (AIM) program were set to leverage the opportunities and advantages of the rapidly evolving Artificial Intelligence technology to achieve real and measurable gains in public healthcare, in quality, access, public confidence and cost efficiency. The proposal for a collaborative AI-human image diagnostics system falls directly into the scope of this program.
Introduction: It is universally accepted that the posteroanterior skull radiograph shows a lower degree of distortion than other radiographic images, so that measurements on it are considered reliable. Objective: To determine the percentage of distortion in the different facial regions of the postero-anterior skull radiograph. Methods: Thirty human skulls with their jaws were divided by three horizontal and four vertical planes into fifteen quadrants; there were ten in the skull and five in the jaw. On each of them a steel wire was placed in vertical and horizontal positions and their length (actual measurement) was measured. Each set was X-rayed in posteroanterior projection and the length of the wires was measured in the image (radiographic measurement). Results: It was not possible to measure in the lateral quadrants of the skull. The horizontal measurement in the right and left lower intermediate quadrants of the skull and in the intermediate and lateral quadrants of both sides of the mandible is not reliable; in the median quadrant of the mandible it is minimized; in the right and left upper intermediate and median quadrants of the skull and in the median of the mandible it is magnified. Vertical measurements in all quadrants are reliable; in the right and left upper intermediate and left upper and middle quadrants of the skull and in the right and left middle and lateral quadrants of the mandible it is magnified; in the lower intermediate and upper and lower middle quadrants of the skull and median of the mandible it is minimized. The least distortion for both measurements occurs in the upper median quadrant of the skull. Percentages of distortion are reported for each quadrant. Conclusions: Distortion is present in the posteroanterior skull radiograph and varies from one region of the face to another.
Aiming at the problem of road network multi-scale matching, a multi-scale road matching method under the constraint of road mesh of small-scale data has been proposed. First, two road meshes with different scale data are constructed; Secondly, under the constraint of the small-scale road mesh, the composite mesh composed of several road meshes in the large-scale road is extracted, and the mesh matching with the small-scale road mesh is completed; Then, many-to-many matching of road meshes with different scales is realized; finally, the matching relationship between composite mesh and small-scale road mesh is transformed into the matching between multi-scale road mesh boundary roads and internal roads, and the matching of the whole road network is completed. The experimental results show that this method can better realize the matching of multi-scale road network.
Over the last two decades, governance for global health has garnered more attention from policymakers, decision-makers, and scholars from several disciplines. The health sector has also become more dynamic and complicated as a result of several factors that have influenced organizational development. The issue of sustainability is clearly raised with specific emphasis and urgency in the context of the global healthcare system. Some countries have been altering their healthcare systems to improve healthcare performance. University hospitals as the main providers of high-quality healthcare services in China, have an irreplaceable role in promoting the construction of healthy China. This study strategic triangle as an analytical framework to identify the key factors that influence university hospital in China and better comprehend how public value is conceptualized and implemented in practice. The study was conducted by qualitative method, five university hospitals designated as “Grade A tertiary hospitals” and semi-structed interviews were carried out with 33 participants, including experts, university hospital leadership level, and basic level. The study revealed that there are eight (8) major factors influencing the development of university hospitals in China. University hospital administrators must be prepared to assess and respond to factors that enhance or hinder implementation continuously and methodically. These insights can be used to improve early preparedness, but additional study in this area is required to better understand the driving factors, action models, and techniques for achieving sustainable development in university hospitals.
Online community facilitates firm-consumer and consumer-consumer interactions for value co-creation. This study explores the relationship between social capital of online community users and community value co-creation in the context of the Xiaomi community. In the study, the forms of value co-creation are differentiated into two forms: initiated value co-creation and participatory value co-creation, and the effects of different types of online community users’ social capital on the forms of value co-creation in which they participate are empirically examined, and the results find that: structural capital has a significant positive effect on initiated value co-creation, while the effect on participatory value co-creation is insignificant; cognitive capital has a significant positive effect on both initiated value co-creation and participatory value co-creation; and cognitive capital has a significant positive effect on both initiated value co-creation and participatory value co-creation. In this context, the present study contributes to a deeper comprehension of the interplay between social capital and models of value co-creation.
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