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	<title>AI in surgery &#8211; BIOENGINEER.ORG</title>
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	<title>AI in surgery &#8211; BIOENGINEER.ORG</title>
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		<title>AI Model Predicts Urosepsis Post-Surgery</title>
		<link>https://bioengineer.org/ai-model-predicts-urosepsis-post-surgery/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Tue, 21 Oct 2025 10:49:20 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[AI in surgery]]></category>
		<category><![CDATA[CT radiomics]]></category>
		<category><![CDATA[CT radiomics integration]]></category>
		<category><![CDATA[interpretable AI in healthcare]]></category>
		<category><![CDATA[interpretable machine learning]]></category>
		<category><![CDATA[machine learning for postoperative complications]]></category>
		<category><![CDATA[postoperative complications]]></category>
		<category><![CDATA[predictive analytics in urology]]></category>
		<category><![CDATA[urosepsis prediction]]></category>
		<category><![CDATA[urosepsis risk prediction]]></category>
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					<description><![CDATA[In a groundbreaking advancement at the intersection of medical imaging and artificial intelligence, researchers have unveiled an interpretable machine learning model that leverages computed tomography (CT) radiomic features alongside clinical data to predict the onset of urosepsis in patients undergoing percutaneous nephrolithotomy (PCNL). Urosepsis, a severe and potentially fatal systemic infection resulting from urinary tract [&#8230;]]]></description>
		
		
		
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