Difference between revisions of "Template:Article of the week"

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'''[[Clinical pathology]]''' (US, UK, Ireland, Commonwealth, Portugal, Brazil, Italy), '''laboratory medicine''' (Germany, Romania, Poland, Eastern Europe), '''clinical analysis''' (Spain), or '''clinical/medical biology''' (France, Belgium, Netherlands, Austria, North and West Africa) is a medical specialty concerned with the diagnosis of disease based on the [[laboratory]] analysis of bodily fluids, such as blood, urine, and tissues using the tools of chemistry, microbiology, hematology, and molecular pathology. Clinical pathologists work in close collaboration with clinical scientists (clinical biochemists, clinical microbiologists, etc.), medical technologists, [[hospital]] administrators, and referring physicians to ensure the accuracy and optimal utilization of laboratory testing. This specialty requires a medical residency and should not be confused with biomedical science, which is not necessarily related to medicine.
'''"[[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]"'''


Clinical pathology is one of two major divisions of pathology, the other being [[anatomical pathology]]. Often, pathologists practice both anatomical and clinical pathology, a combination sometimes known as general pathology. The distinction between clinical and anatomic pathology is increasingly blurred by the introduction of technologies that require new expertise and the need to provide patients and referring physicians with integrated diagnostic reports. ('''[[Clinical pathology|Full article...]]''')<br />
Full [[laboratory automation]] is revolutionizing work habits in an increasing number of clinical [[microbiology]] facilities worldwide, generating huge streams of [[Imaging|digital images]] for interpretation. Contextually, [[deep learning]] (DL) architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic [[Bacteria|bacterial]] [[Cell culture|culture]] plates, including presumptive [[pathogen]] identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony ... ('''[[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology|Full article...]]''')<br />
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''Recently featured'':
''Recently featured'': [[Anatomical pathology]], [[Information]], [[Clinical laboratory]]
{{flowlist |
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* [[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]
* [[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]
* [[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]
}}

Latest revision as of 15:02, 3 June 2024

Fig1 Signoroni NatComm23 14.png

"Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology"

Full laboratory automation is revolutionizing work habits in an increasing number of clinical microbiology facilities worldwide, generating huge streams of digital images for interpretation. Contextually, deep learning (DL) architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic bacterial culture plates, including presumptive pathogen identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony ... (Full article...)
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