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	<title>convolutional neural networks &#8211; BIOENGINEER.ORG</title>
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		<title>AI Advances Brain-Wide Histopathology in Synucleinopathy Models</title>
		<link>https://bioengineer.org/ai-advances-brain-wide-histopathology-in-synucleinopathy-models/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Tue, 18 Nov 2025 18:56:47 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[Automated pathology analysis** **Açıklama:** * **AI in neurodegeneration:** Ana konu olan yapay zekanın nörodejeneratif hastalık araştırmalarındaki rolünü kapsar. * **Convolution]]></category>
		<category><![CDATA[Brain-wide histopathology]]></category>
		<category><![CDATA[convolutional neural networks]]></category>
		<category><![CDATA[İşte içerik için uygun 5 etiket: **AI in neurodegeneration]]></category>
		<category><![CDATA[Synucleinopathy models]]></category>
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					<description><![CDATA[In the rapidly evolving landscape of neurodegenerative disease research, a groundbreaking study published in npj Parkinson’s Disease details the development of cutting-edge computational tools designed to revolutionize histopathological analysis of synucleinopathies in mouse models. Employing convolutional neural networks (CNNs), a sophisticated form of deep learning technology, this novel approach enables fully automated, brain-wide examination of [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">298400</post-id>	</item>
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		<title>Exploring Archaeal Promoters with Explainable CNN Models</title>
		<link>https://bioengineer.org/exploring-archaeal-promoters-with-explainable-cnn-models/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Sun, 26 Oct 2025 02:52:43 +0000</pubDate>
				<category><![CDATA[Biology]]></category>
		<category><![CDATA[Archaeal promoters]]></category>
		<category><![CDATA[convolutional neural networks]]></category>
		<category><![CDATA[Explainable AI in genomics]]></category>
		<category><![CDATA[Machine learning in genomics]]></category>
		<category><![CDATA[Transcriptional control in Archaea]]></category>
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					<description><![CDATA[In a groundbreaking study published in BMC Genomics, researchers Mohammed Shujaat and S. Q. Mao presented an innovative approach to characterizing archaeal promoters by leveraging cutting-edge explainable artificial intelligence techniques. This research marks a significant milestone in genomics, shedding light on the complexities of archaeal gene regulation. The ability to decipher the underlying mechanisms of [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">286925</post-id>	</item>
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		<title>Deep Learning Classifies Mandibular Condyle Variations in Radiographs</title>
		<link>https://bioengineer.org/deep-learning-classifies-mandibular-condyle-variations-in-radiographs/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Wed, 27 Aug 2025 18:13:00 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[convolutional neural networks]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[Dental Radiology AI]]></category>
		<category><![CDATA[Mandibular Condyle Variations]]></category>
		<category><![CDATA[Panoramic Radiograph Analysis]]></category>
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					<description><![CDATA[In a groundbreaking study, researchers have successfully applied advanced deep learning techniques to classify the morphological variations of the mandibular condyle as observed through panoramic radiographs. The mandibular condyle, a vital component in the temporomandibular joint, plays an essential role in mastication and overall jaw function. Any morphological discrepancies can lead to significant clinical implications, [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">258666</post-id>	</item>
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		<title>Deep Learning Radiomics Advances Tongue Cancer Staging</title>
		<link>https://bioengineer.org/deep-learning-radiomics-advances-tongue-cancer-staging/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Fri, 22 Aug 2025 08:24:41 +0000</pubDate>
				<category><![CDATA[Cancer]]></category>
		<category><![CDATA[convolutional neural networks]]></category>
		<category><![CDATA[deep learning radiomics]]></category>
		<category><![CDATA[MRI in oncology]]></category>
		<category><![CDATA[personalized cancer treatment]]></category>
		<category><![CDATA[tongue cancer staging]]></category>
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					<description><![CDATA[In a groundbreaking advancement for oncological imaging, researchers have unveiled a sophisticated deep learning radiomics model leveraging MRI technology to enhance the accuracy of tongue cancer T-staging. This innovative approach integrates cutting-edge artificial intelligence techniques directly with magnetic resonance imaging data, promising to revolutionize how clinicians evaluate tumor progression and personalize treatment strategies for affected [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">256915</post-id>	</item>
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		<title>CNN Automates CT Scoring for Sinus Imaging</title>
		<link>https://bioengineer.org/cnn-automates-ct-scoring-for-sinus-imaging/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Sun, 27 Apr 2025 20:59:54 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[AI in radiology]]></category>
		<category><![CDATA[chronic rhinosinusitis]]></category>
		<category><![CDATA[convolutional neural networks]]></category>
		<category><![CDATA[CT scan automation]]></category>
		<category><![CDATA[Lund-Mackay scoring]]></category>
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					<description><![CDATA[In a groundbreaking advance poised to transform diagnostic radiology, researchers have harnessed the power of convolutional neural networks (CNNs) to automate the scoring of computed tomography (CT) scans of the paranasal sinuses. This innovative approach promises to standardize and expedite the evaluation of chronic rhinosinusitis (CRS), a condition that affects millions worldwide and has long [&#8230;]]]></description>
		
		
		
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