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Artificiell Intelligens inom medicinsk bilddiagnostik: En allmän litteraturstudie
Luleå University of Technology, Department of Health Sciences.
Luleå University of Technology, Department of Health Sciences.
2019 (Swedish)Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesisAlternative title
Artificial Intelligence in medical imaging : A general litterature review (English)
Abstract [sv]

Bakgrund: Artificiell Intelligens (AI) kommer in i vårt samhälle och våra hem i allt större utsträckning. Inom sjukvården och radiologin kan AI utgöra ett hjälpmedel för både radiologer och röntgensjuksköterskor i deras profession. Forskning om AI fortsätter med oförminskad kraft för att finna allt bättre och mer funktionsdugliga algoritmer som kan anta den utmaningen. Syfte: Syftet med denna litteraturstudie är att sammanställa vid vilka modaliteter AI används som stöd. Metod: Studien utfördes som en allmän litteraturstudie vilket genererade femton artiklar som kvalitetsgranskades och kategoriserades efter analys. Resultat: Beroende på tidpunkt när artiklarna var skrivna varierade metoderna hur träning av AI genomfördes. Det varierade även hur bilderna skulle förbearbetats inför träning. Bilderna måste genomgå brusreducering och segmentering för att AI ska kunna klassificera den sjukliga förändringen. Den processen underlättades i senare versioner av AI där alla dessa moment utfördes på en och samma gång. Slutsats: Stora förändringar kommer att ske inom radiologin och förändringarna kommer sannolikt att påverka alla på en röntgenavdelning. Författarna kan se att utvecklingen bara börjat och forskningen måste fortgå många år framöver.

Abstract [en]

Background: Artificial Intelligence (AI) increasingly comes in to our society and homes. In the field of medical care and radiology, AI will provide an aid for radiologists and radiographers in their professions. Research on AI continues in finding better and more functional algorithms which can achieve that.

Purpose: The purpose of this literature study is to compile facts about modalities using artificial intelligence as support. Method: The study was conducted as a general literature study, which generated fifteen articles that were quality-reviewed and categorized after analysis. Result: Depending on the date when the articles were written the methods varied concerning how training of AI was performed. It also varied how the images were pre-processed before training. The images need to be processed by noise reduction and segmentation for AI in order to be able to classify the pathological change. That process was facilitated in later versions of AI where all these steps were performed at the same time. Conclusion: Major changes may occur in radiology and the changes are likely to affect everyone in an X-ray ward. The authors can see that the development has just begun and research has to continue for many years to come.

Place, publisher, year, edition, pages
2019. , p. 34
Keywords [en]
Artificial Intelligence, AI, Deep Learning, Machine Learning, CADe, CADx
Keywords [sv]
Artificiell Intelligens, AI, djupinlärning, maskininlärning, CADe, CADx
National Category
Other Health Sciences
Identifiers
URN: urn:nbn:se:ltu:diva-72969OAI: oai:DiVA.org:ltu-72969DiVA, id: diva2:1290657
Subject / course
Student thesis, at least 15 credits; Student thesis, at least 15 credits
Educational program
Diagnostic Radiology Nursing, bachelor's level; Diagnostic Radiology Nursing, bachelor's level
Supervisors
Examiners
Available from: 2019-02-21 Created: 2019-02-21 Last updated: 2019-02-21Bibliographically approved

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4243444546474845 of 59
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