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Artificial Intelligence - PLOS

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Last Updated: 10 September 2022

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Artificial intelligence and cloud based platform for fully automated PCI guidance from coronary angiography-study protocol

Regardless of the methods used for diagnosis and treatment of such patients in the daily medical routine, Ischemic heart disease and others represent a significant burden on the healthcare systems. For releasing an extensive paper describing and supporting the correct PCI strategy pick, we suggest that we use multiple artificial intelligence based models to produce three-dimensional coronary anatomy reconstruction and analysis function-based PCI FFR computation. In a pilot clinical trial involving subjects prospectively to compare the artificial intelligence services and results against annotations and invasive measurements, the machine intelligence algorithms and cloud-based PCI selection workflow will be tested and confirmed.

Source link: https://doi.org/10.1371/journal.pone.0274296


Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis

The history: Artificial intelligence algorithms have been used to diagnose temporomandibular disorders. Resultantly, the AI models' results can vary. This paper sought to synthesize the latest evidence on the use of AI technologies for diagnosis of various TMD subtypes of TMD subtypes, assess the quality of these studies, and determine the diagnostic reliability of existing AI models. At least one subtype of TMD and those that assessed the effectiveness of AI algorithms were included in a research that used AI algorithms to analyze the results of AI algorithms. We refused to publish research on orofacial pain that were not directly related to the TMD, such as studies on atypical facial pain and neuropathic pain, editorials, book chapters, and excerpts from chapters with incomplete empirical evidence. Conclusions: Many AI algorithms for diagnosing TMDs may have additional clinical experience to improve diagnostic accuracy.

Source link: https://doi.org/10.1371/journal.pone.0272715


Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network

This paper sought to identify the technology frontiers of artificial intelligence-assisted pathology in this context based on a patent citation network. Methods: Patents related to artificial intelligence-assisted pathology were searched and collected from the Derwent Innovation Index, which were imported into Derwent Data Analyzer for authority monitoring and importation into the freely available computer program Ucinet 6 for creating the patent citation network. According to the citation relationship, the patent citation network according to the citation link could indicate the technology advancement context in the field of artificial intelligence-assisted pathology. Patent citations were extracted from the newly collected patent data, selected highly cited patents to produce a co-occurrence matrix, and developed a patent citation network based on each period's co-occurrence matrix. Text clustering is unsupervised learning technique, which is a common text mining technique, where similar documents are grouped into clusters. According to co-word examination of the patent title and abstract in this field, the text clustering technique was used to identify the technology frontiers based on the patent citation network. The findings of this research gave an overview of the features of study status and growth trends in the field of artificial intelligence-assisted pathology, which may help readers develop innovative ideas and uncover new technological advancements, as well as key policy indicators.

Source link: https://doi.org/10.1371/journal.pone.0273355


Determinants of coronavirus disease 2019 infection by artificial intelligence technology: A study of 28 countries

Objectives: Since 2020, the coronavirus disease 2019 pandemic has impacted countries around the world, and an increasing number of people are being affected. The aim of this study was to use big data and artificial intelligence technologies to identify key factors associated with the coronavirus disease 2019 outbreak. Methods: This study obtained data from "Imperial College London YouGov Covid-19 Behaviour Tracker Open Data Hub" (which included a total of 291,780 questionnaire responses from 28 countries. The results of each module are reported as the area under the receiver operating characteristics curve, as shown. Results: The RF had the highest results, according to this report, the area under the receiver operating characteristics curve of the four machine learning algorithms was all > 0. 95, and the prediction modules were all > 0. 95, with the receiver operating characteristics curve determined by the four machine learning techniques being the highest value.

Source link: https://doi.org/10.1371/journal.pone.0272546


Artificial intelligence and machine learning in mobile apps for mental health: A scoping review

Mental health problems may have a major negative effect on health and healthcare services. Artificial intelligence is being used in mobile health applications, and there is a need for an overview of the state of the literature on these applications. The aim of this scoping report is to give an overview of the latest research findings and knowledge gaps regarding the use of artificial intelligence in mobile health applications for mental health. PubMed was continually searching for randomised controlled trials and cohort studies that have been published in English since 2014 to assess whether artificial intelligence- or machine learning-enabled mobile apps for mental health care. Overall, the findings showed the usefulness of using artificial intelligence to support mental health apps, but the early phases of the study's findings and limitations highlighted the need for more research into machine learning-enabled mental health services, as well as better evidence of their effectiveness. One potential solution to long wait times and a lack of mental health services is to install mobile health services. With this report, we wanted to give an overview of smartphone health apps that are using artificial intelligence to provide some sort of mental health assistance and help identify areas where further research is required. We found 17 studies that tested an AI mental health app. The details we have gathered here can help guide future research and AI mental health applications.

Source link: https://doi.org/10.1371/journal.pdig.0000079

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions