CovidStopHospital: e-Health Service for X-Ray-Based COVID-19 Classification and Radiologist-Assisted Dataset Creation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU150169" target="_blank" >RIV/00216305:26220/23:PU150169 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00098892:_____/23:10158534

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/10333292" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10333292</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

CovidStopHospital: e-Health Service for X-Ray-Based COVID-19 Classification and Radiologist-Assisted Dataset Creation

Popis výsledku v původním jazyce

Image data processing using artificial intelligence (AI) algorithms has various applications, including medicine. During the SARS-CoV-2 pandemic, many successful COVID-19 classification algorithms were trained. However, to be effectively used in clinical settings, these algorithms need to be deployed in hospitals. Existing platforms for AI algorithm deployment may not be usable in hospitals that rely on proprietary information systems lacking application interfaces. This paper introduces an easily modifiable general AI-X-ray service capable of deploying AI algorithms even in hospitals using proprietary information systems lacking application interfaces. The CovidStopHospital service, based on the AI-X-ray architecture, is also presented. It is designed for COVID-19 classification and can seamlessly incorporate any classification AI algorithm; the presented solution uses DeepCovid-XR algorithm. The service also includes functionality for radiologists to label X-ray images, facilitating the creation of new datasets. CovidStopHospital underwent testing to ensure its stability and performance, with an average X-ray analysis time of 11.53 seconds and a maximum of 14.01 seconds. The tool can potentially be a valuable diagnostic support tool and is currently in experimental deployment at the University Hospital of Olomouc

Název v anglickém jazyce

CovidStopHospital: e-Health Service for X-Ray-Based COVID-19 Classification and Radiologist-Assisted Dataset Creation

Popis výsledku anglicky

Image data processing using artificial intelligence (AI) algorithms has various applications, including medicine. During the SARS-CoV-2 pandemic, many successful COVID-19 classification algorithms were trained. However, to be effectively used in clinical settings, these algorithms need to be deployed in hospitals. Existing platforms for AI algorithm deployment may not be usable in hospitals that rely on proprietary information systems lacking application interfaces. This paper introduces an easily modifiable general AI-X-ray service capable of deploying AI algorithms even in hospitals using proprietary information systems lacking application interfaces. The CovidStopHospital service, based on the AI-X-ray architecture, is also presented. It is designed for COVID-19 classification and can seamlessly incorporate any classification AI algorithm; the presented solution uses DeepCovid-XR algorithm. The service also includes functionality for radiologists to label X-ray images, facilitating the creation of new datasets. CovidStopHospital underwent testing to ensure its stability and performance, with an average X-ray analysis time of 11.53 seconds and a maximum of 14.01 seconds. The tool can potentially be a valuable diagnostic support tool and is currently in experimental deployment at the University Hospital of Olomouc

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/VK01010153" target="_blank" >VK01010153: Vývoj umělé inteligence pro systém multimodální nedestruktivní forenzní analýzy materiálů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

979-8-3503-9328-6

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

62-67

Název nakladatele

Neuveden

Místo vydání

Ghent

Místo konání akce

Gent, Belgium

Datum konání akce

30. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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