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:School Based Health Clinic.jpg|140px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png|240px]]</div>
A '''[[federally qualified health center]]''' ('''FQHC''') is a reimbursement designation from the [[Centers for Medicare and Medicaid Services]] (CMS) of the [[United States Department of Health and Human Services]] (HHS). This designation is significant for several health programs funded under Section 330 of the Public Health Service Act, as part of the Health Center Consolidation Act. The FQHC program is designed "to enhance the provision of primary care services in underserved urban and rural communities."
'''"[[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]]"'''


FQHCs are community-based organizations that provide comprehensive primary and preventive care, including health, oral, and mental health services to persons of all ages, regardless of their ability to pay or health insurance status. Thus, they are a critical component of the health care safety net. As of 2011 over 1,100 FQHCs operate approximately 6,000 sites throughout the United States and territories, serving an estimated 20 million patients. That number is expected to go up to 40 million people by 2015 thanks to extra grant funding to the program. FQHCs may also be referred to as community/migrant health centers (C/MHC), community health centers (CHC), and 330 funded clinics. ('''[[Federally qualified health center|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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