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	<title>AI in medical imaging &#8211; BIOENGINEER.ORG</title>
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		<title>Improving Pediatric Iodinated Contrast Delivery Worldwide</title>
		<link>https://bioengineer.org/improving-pediatric-iodinated-contrast-delivery-worldwide/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Wed, 29 Oct 2025 20:02:03 +0000</pubDate>
				<category><![CDATA[Cancer]]></category>
		<category><![CDATA[AI in medical imaging]]></category>
		<category><![CDATA[Healthcare Standardization]]></category>
		<category><![CDATA[International Healthcare Disparities]]></category>
		<category><![CDATA[Iodinated Contrast Media]]></category>
		<category><![CDATA[pediatric radiology]]></category>
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					<description><![CDATA[In a groundbreaking exploration of pediatric radiology, researchers led by Rawashdeh et al. have delved into the crucial issue of variability in iodinated contrast media delivery across a plethora of international healthcare settings. As medical imaging technologies continue to evolve and become critical in diagnosing and treating ailments in children, the standardization of contrast media [&#8230;]]]></description>
		
		
		
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		<title>Comparative Analysis of Liver Vessel Segmentation Algorithms</title>
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		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Wed, 27 Aug 2025 08:55:04 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[3D Medical Imaging]]></category>
		<category><![CDATA[AI in medical imaging]]></category>
		<category><![CDATA[Algorithm Performance Metrics]]></category>
		<category><![CDATA[CT-Based Preoperative Planning]]></category>
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		<category><![CDATA[Liver Vessel Segmentation]]></category>
		<category><![CDATA[Liver Vessel Segmentation Algorithms]]></category>
		<category><![CDATA[Medical Image Analysis]]></category>
		<category><![CDATA[Multidisciplinary Collaboration in Healthcare]]></category>
		<category><![CDATA[Segmentation Algorithms Comparison]]></category>
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					<description><![CDATA[In the rapidly evolving fields of medical imaging and preoperative planning, a groundbreaking study has emerged that significantly enhances our understanding of segmentation algorithms used for generating CT-based meshes of liver vessels. Conducted by a team of scholars including Pedraja, Seiffert, and Corpas-del Moral, their research delves deeply into the comparative effectiveness of various segmentation [&#8230;]]]></description>
		
		
		
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		<title>Building Generalist Radiology Models with Massive 2D/3D Data</title>
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		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Sat, 23 Aug 2025 22:30:00 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[2D/3D medical data integration]]></category>
		<category><![CDATA[AI in medical imaging]]></category>
		<category><![CDATA[generalist radiology models]]></category>
		<category><![CDATA[multi-modal learning in radiology]]></category>
		<category><![CDATA[web-scale healthcare datasets]]></category>
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					<description><![CDATA[In an era where artificial intelligence continues to revolutionize medicine, a groundbreaking development is reshaping the landscape of radiology. Researchers led by Wu, Zhang, and Zhang unveil a pioneering approach to constructing a generalist foundation model that seamlessly integrates both two-dimensional (2D) and three-dimensional (3D) medical imaging data on an unprecedented scale. This model is [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">257219</post-id>	</item>
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		<title>AI Predicts Lung Nodule Infiltration Pre-Surgery</title>
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		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Wed, 16 Apr 2025 14:26:26 +0000</pubDate>
				<category><![CDATA[Cancer]]></category>
		<category><![CDATA[AI in medical imaging]]></category>
		<category><![CDATA[CT radiomics]]></category>
		<category><![CDATA[Neural network applications]]></category>
		<category><![CDATA[Preoperative surgical planning]]></category>
		<category><![CDATA[Pulmonary ground-glass nodules]]></category>
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					<description><![CDATA[In the rapidly evolving domain of oncology and medical imaging, the precision of preoperative assessments stands as a critical determinant of successful patient outcomes. A recent breakthrough study published in BMC Cancer introduces an innovative approach that synergizes computed tomography (CT) based radiomics with advanced neural network architectures to predict the infiltration status of pulmonary [&#8230;]]]></description>
		
		
		
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