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| http://dx.doi.org/10.1073/pnas.1917490116 | DOI Listing |
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Recent advances in the tools and techniques for AI-aided diagnosis of atrial fibrillation.
Biophys Rev (Melville)
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Department of Electrical and Electronic Engineering, Dhaka University of Engineering & Technology, Gazipur 1707, Bangladesh.
Atrial fibrillation (AF) is recognized as a developing global epidemic responsible for a significant burden of morbidity and mortality. To counter this public health crisis, the advancement of artificial intelligence (AI)-aided tools and methodologies for the effective detection and monitoring of AF is becoming increasingly apparent. A unified strategy from the international research community is essential to develop effective intelligent tools and technologies to support the health professionals for effective surveillance and defense against AF.
View Article and Find Full Text PDFThe Venus score for the assessment of the quality and trustworthiness of biomedical datasets.
BioData Min
January 2025
Fondazione Bruno Kessler, Trento, Italy.
Biomedical datasets are the mainstays of computational biology and health informatics projects, and can be found on multiple data platforms online or obtained from wet-lab biologists and physicians. The quality and the trustworthiness of these datasets, however, can sometimes be poor, producing bad results in turn, which can harm patients and data subjects. To address this problem, policy-makers, researchers, and consortia have proposed diverse regulations, guidelines, and scores to assess the quality and increase the reliability of datasets.
View Article and Find Full Text PDFLocal interpretable model-agnostic explanation approach for medical imaging analysis: A systematic literature review.
Comput Biol Med
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Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia; Center for Research in Data Science (CeRDaS), Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia. Electronic address:
Background: The interpretability and explainability of machine learning (ML) and artificial intelligence systems are critical for generating trust in their outcomes in fields such as medicine and healthcare. Errors generated by these systems, such as inaccurate diagnoses or treatments, can have serious and even life-threatening effects on patients. Explainable Artificial Intelligence (XAI) is emerging as an increasingly significant area of research nowadays, focusing on the black-box aspect of sophisticated and difficult-to-interpret ML algorithms.
View Article and Find Full Text PDFDynamic and quantitative assessment of quercetin for cardiac oxidative stress injury prevention using sensitive cardiomyocyte based biosensing.
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Diabetes and Obesity Department, Tongde Hospital of Zhejiang Province, Hangzhou, China; Integrated Chinese and Western Medicine Department, Center for General Practice Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China. Electronic address:
Myocardial infarction is a leading cause of morbidity and mortality associated with cardiovascular diseases worldwide. Although novel medications and treatments greatly alleviate patient suffering, challenges related to prognostic limit the recovery of cardiac function. Currently, treatment with monomeric compounds displays promise in prognostic interventions for cardiac diseases.
View Article and Find Full Text PDFBPEN: Brain Posterior Evidential Network for trustworthy brain imaging analysis.
Neural Netw
March 2025
Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, 15260, PA, USA. Electronic address:
The application of deep learning techniques to analyze brain functional magnetic resonance imaging (fMRI) data has led to significant advancements in identifying prospective biomarkers associated with various clinical phenotypes and neurological conditions. Despite these achievements, the aspect of prediction uncertainty has been relatively underexplored in brain fMRI data analysis. Accurate uncertainty estimation is essential for trustworthy learning, given the challenges associated with brain fMRI data acquisition and the potential diagnostic implications for patients.
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