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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig4 Celesti Sensors20 20-9.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png|240px]]</div>
'''"[[Journal:Blockchain-based healthcare workflow for IoT-connected laboratories in federated hospital clouds|Blockchain-based healthcare workflow for IoT-connected laboratories in federated hospital clouds]]"'''
'''"[[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]]"'''


In a [[pandemic]]-related situation such as that caused by the [[SARS-CoV-2]] virus, the need for [[telemedicine]] and other distanced services becomes dramatically fundamental to reducing the movement of patients, and by extension reducing the risk of infection in healthcare settings. One potential avenue for achieving this is through leveraging [[cloud computing]] and [[internet of things]] (IoT) technologies. This paper proposes an IoT-connected laboratory service where clinical exams are performed on patients directly in a [[hospital]] by technicians through the use of IoT medical diagnostic devices, with results automatically being sent via the hospital's cloud to doctors of federated hospitals for validation and/or consultation. In particular, we discuss a distributed scenario where nurses, technicians, and medical doctors belonging to different hospitals cooperate through their federated hospital clouds ... ('''[[Journal:Blockchain-based healthcare workflow for IoT-connected laboratories in federated hospital clouds|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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