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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Dialysis machines by irvin calicut.jpg|250px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png|240px]]</div>
An '''[[end-stage renal disease facility]]''' ('''ESRD facility''', '''dialysis facility''', or '''dialysis center''') is a medical facility that operates to assist people with irreversible loss of kidney function (stage five), requiring a regular course of dialysis or a kidney transplant to survive. The facility may operate independently, as part of a [[hospital]]-based unit, or as a self-care unit that furnishes only self-dialysis services.
'''"[[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 U.S. [[Centers for Medicare and Medicaid Services]] (CMS) describes four types of ESRD facilities, including the renal transplantation center, for ESRD transplant patients; the renal dialysis center, a hospital unit for ESRD dialysis patients; a renal dialysis facility, a direct or stand-alone dialysis unit for ESRD patients; and a self-dialysis unit attached to one of the previous three, providing self-dialysis services. Patients undergoing dialysis at these facilities require two important documentation steps: the patient assessment and the patient plan of care. U.S. Federal regulation requires a comprehensive 13-point assessment, including current health status, laboratory profile, and nutritional status. ('''[[End-stage renal disease facility|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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