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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Snyder PLOSDigHlth22 1-11.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png|240px]]</div>
'''"[[Journal:From months to minutes: Creating Hyperion, a novel data management system expediting data insights for oncology research and patient care|From months to minutes: Creating Hyperion, a novel data management system expediting data insights for oncology research and patient care]]"'''
'''"[[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]]"'''


Ensuring timely access to accurate data is critical for the functioning of a [[cancer]] center. Despite overlapping data needs, data are often fragmented and sequestered across multiple systems (such as the [[electronic health record]] [EHR], state and federal registries, and research [[database]]s), creating high barriers to data access for clinicians, researchers, administrators, quality officers, and patients. The creation of [[System integration|integrated data systems]] also faces technical, leadership, cost, and human resource barriers, among others. The University of Rochester's James P. Wilmot Cancer Institute (WCI) hired a small team of individuals with both technical and clinical expertise to develop a custom [[Information management|data management]] software platform—Hyperion— addressing five challenges: lowering the skill level required to maintain the system, reducing costs, allowing users to access data autonomously, optimizing [[Information security|data security]] and utilization, and shifting technological team structure to encourage rapid innovation ... ('''[[Journal:From months to minutes: Creating Hyperion, a novel data management system expediting data insights for oncology research and patient care|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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