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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Day 253 - West Midlands Police - Forensic Science Lab (7969822920).jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png|240px]]</div>
'''The [[American Society of Crime Laboratory Directors/Laboratory Accreditation Board]]''' ('''ASCLD/LAB''') is a Missouri-based not-for-profit that "offers voluntary accreditation to public and private crime laboratories" around the world.
'''"[[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 main objectives of the ASCLD/LAB are:
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'':
#To improve the quality of laboratory services provided to the criminal justice  system.
{{flowlist |
#To adopt, develop and maintain criteria which may be used by  a laboratory  to assess its level of performance and to strengthen its  operation.
* [[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]]
#To provide an independent, impartial, and objective system by which  laboratories can  benefit from a total operational review.
* [[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]
#To offer to the general public and to users of laboratory services a means of  identifying those laboratories which have demonstrated that they meet established standards. ('''[[The American Society of Crime Laboratory Directors/Laboratory Accreditation Board|Full article...]]''')<br />
* [[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]
 
}}
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''Recently featured'': [[Environmental informatics]], [[Application programming interface]], [[Immunoinformatics]]

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...)
Recently featured: