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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig8 Lee Sustain20 13-1.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png|240px]]</div>
'''"[[Journal:Towards a risk catalog for data management plans|Towards a risk catalog for data management plans]]"'''
'''"[[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]]"'''


[[Personal health record]]s (PHRs) have many benefits for things such as [[Public health surveillance|health surveillance]], [[Epidemiology|epidemiological surveillance]], self-control, links to various services, [[public health]] and health management, and international surveillance. The implementation of an international standard for interoperability is essential to accessing PHRs. In Taiwan, the nationwide exchange platform for [[electronic medical record]]s (EMRs) has been in use for many years. The [[Health Level 7|Health Level Seven International]] (HL7) Clinical Document Architecture (CDA) was used as the standard for those EMRs. However, the complication of implementing CDA became a barrier for many [[hospital]]s to realizing standard EMRs. In this study, we implemented a [[Health Level 7#Fast Healthcare Interoperability Resources (FHIR)|Fast Healthcare Interoperability Resources]] (FHIR)-based PHR transformation process, including a user interface module to review the contents of PHRs. ('''[[Journal:Towards a risk catalog for data management plans|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...)
Recently featured: