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

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Buabbas JMIRMedInfo2016 4-2.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png|240px]]</div>
'''"[[Journal:Users’ perspectives on a picture archiving and communication system (PACS): An in-depth study in a teaching hospital in Kuwait|Users’ perspectives on a picture archiving and communication system (PACS): An in-depth study in a teaching hospital in Kuwait]]"'''
'''"[[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]]"'''


The [[picture archiving and communication system]] (PACS) is a well-known [[imaging informatics]] application in health care organizations, specifically designed for the radiology department. Health care providers have exhibited willingness toward evaluating PACS in hospitals to ascertain the critical success and failure of the technology, considering that evaluation is a basic requirement.
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 />
 
''Recently featured'':
This study aimed to evaluate the success of a PACS in a regional teaching hospital of Kuwait, from users’ perspectives, using information systems success criteria.
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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]]
An in-depth study was conducted by using quantitative and qualitative methods. This mixed-method study was based on: (1) questionnaires, distributed to all radiologists and technologists and (2) interviews, conducted with PACS administrators. ('''[[Journal:Users’ perspectives on a picture archiving and communication system (PACS): An in-depth study in a teaching hospital in Kuwait|Full article...]]''')<br />
* [[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]
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* [[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]
''Recently featured'':  
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: ▪ [[Journal:Effective information extraction framework for heterogeneous clinical reports using online machine learning and controlled vocabularies|Effective information extraction framework for heterogeneous clinical reports using online machine learning and controlled vocabularies]]
: ▪ [[Journal:Selecting a laboratory information management system for biorepositories in low- and middle-income countries: The H3Africa experience and lessons learned|Selecting a laboratory information management system for biorepositories in low- and middle-income countries: The H3Africa experience and lessons learned]]
: ▪ [[Journal:Baobab Laboratory Information Management System: Development of an open-source laboratory information management system for biobanking|Baobab Laboratory Information Management System: Development of an open-source laboratory information management system for biobanking]]

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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