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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Mwambe IntJofAdvSciResEng22 8-4.png|240px]]</div>
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'''"[[Journal:Development of a smart laboratory information management system: A case study of NM-AIST Arusha of Tanzania|Development of a smart laboratory information management system: A case study of NM-AIST Arusha of Tanzania]]"'''
'''"[[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]]"'''


Testing laboratories in higher learning institutions of science, technology, and engineering are used by institutional staff, researchers, and external stakeholders in conducting research experiments, [[Sample (material)|sample]] analysis, and result dissemination. However, there exists a challenge in the management of [[laboratory]] operations and processing of laboratory-based data. Operations carried out in the laboratory at Nelson Mandela African Institution of Science and Technology (NM-AIST), in Arusha, Tanzania—where this case study was carried out—are paper-based. There is no automated way of sample registration and identification, and researchers are prone to making errors when handling sensitive reagents. Users have to physically visit the laboratory to enquire about available equipment or reagents before borrowing or reserving those resources. Additionally, paper-based forms have to be filled out and handed to the laboratory manager for approval ... ('''[[Journal:Development of a smart laboratory information management system: A case study of NM-AIST Arusha of Tanzania|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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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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