Methods: 3D biomodels were printed with flexible material (elastomer) using angiotomographic DICOM acquired images and compared to 3D digital subtraction angiography (DSA) images.
Results: 3D biomodels represented the aneurysm angioarchitecture exactly, especially the neck and domus features.
Conclusion: Elastomers 3D biomodels proved to be a trustworthy representation of the angiotomographic images and could be used to help surgical planning in IA treatment.
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http://dx.doi.org/10.1590/0004-282X20160113 | DOI Listing |
Health Informatics J
December 2024
The University of Queensland, Brisbane, QLD, Australia.
Objective: This study aimed to assess the practicality and trustworthiness of explainable artificial intelligence (XAI) methods used for explaining clinical predictive models.
Methods: Two popular XAIs used for explaining clinical predictive models were evaluated based on their ability to generate domain-appropriate representations, impact clinical workflow, and consistency. Explanations were benchmarked against true clinical deterioration triggers recorded in the data system and agreement was quantified.
Trials
November 2024
Global Health Leadership Initiative, Yale School of Public Health, 60 College Street, New Haven, CT, 06510, USA.
Background: Among the most powerful barriers to broader inclusion of diverse participants in clinical trials are social determinants of health, trustworthiness of health care providers and research institutions, and competing pressures on potential participants. Nevertheless, current tools to assess organizational capabilities for clinical trial diversity focus primarily on trial infrastructure, rely solely on quantitative self-reported data, and lack meaningful assessment of capabilities related to community engagement.
Methods: The Equitable Breakthroughs in Medicine (EQBMED) initiative developed a holistic, collaborative, site-driven formative model and accompanying assessment to catalog sites' current capabilities and identify opportunities for growth in both conducting industry-sponsored clinical trials and enriching diversity of those trials.
Persistence images, derived from topological data analysis, emerge as a powerful tool for visualizing and mitigating biases in radiological data interpretation and AI model development. This technique transforms complex topological features into stable, interpretable representations, offering unique insights into medical imaging data structure. By providing intuitive visualizations, persistence images enable the identification of subtle structural differences and potential biases in data acquisition, interpretation, and AI model training.
View Article and Find Full Text PDFSensors (Basel)
October 2024
Saudi Electronic University, Prince Muhammad Ibn Salman Rd., Ar Rabi, Ryiadh 11673, Saudi Arabia.
There have recently been rapid developments in smart healthcare systems, such as precision diagnosis, smart diet management, and drug discovery. These systems require the integration of the Internet of Things (IoT) for data acquisition, Digital Twins (DT) for data representation into a digital replica and Artificial Intelligence (AI) for decision-making. DT is a digital copy or replica of physical entities (e.
View Article and Find Full Text PDFFront Artif Intell
October 2024
Institute for Medical Education, University of Bern, Bern, Switzerland.
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