Title Primjena umjetne inteligencije kod magnetne rezonancije koljena
Title (english) Application of artificial intelligence in magnetic resonance imaging of the knee
Author Petra Kujundžić
Mentor Tatjana Matijaš (mentor)
Committee member Diana Aranza (predsjednik povjerenstva)
Committee member Frane Mihanović (član povjerenstva)
Committee member Tatjana Matijaš (član povjerenstva)
Granter University of Split (University Department of Health Studies) Split
Defense date and country 2023-07-14, Croatia
Scientific / art field, discipline and subdiscipline BIOMEDICINE AND HEALTHCARE Clinical Medical Sciences
Abstract Uvod: Tehnološki napredak potaknuo je sve veće korištenje radioloških snimanja, a povećanjem njihovog broja dolazi do povećanog radnog opterećenja radiologa. Stoga se pokretačem primjene AI u radiologiji smatra upravo smanjenje radnog opterećenja radiologa i potreba za sve većom preciznošću i učinkovitošću.
Cilj rada: Cilj ovog rada je približiti čitatelju samu AI i njenu primjenu u radiologiji, a posebno kod modaliteta MRI te na koji način algoritmi dubokog učenja pospješuju rekonstrukciju slike te dovode do bržih i preciznijih rezultata.
Rasprava: Brojna su istraživanja potvrdila značaj implementacije strojnog učenja, podskupa umjetne inteligencija, u radiološki sustav. U ovom preglednom radu izdvojena su brojna istraživanja primjene dubokog učenja kod magnetne rezonancije, a naglasak je na modelima za automatsku segmentaciju. Automatska segmentacija pokazala je izvrsne rezultate kod ranog otkrivanja osteoartritisa, zatim kod puknuća prednjeg križnog ligamenta i meniskusa, najčešćih ozljeda koljena, a također se u novije vrijeme model dubokog učenja istaknuo i kod automatske procjene koštane dobi. Automatskom segmentacijom postigla se, prije svega visoka točnost i preciznost, objektivnost i iznimna ušteda vremena.
Zaključak: Dosadašnja istraživanja već su istaknula značajnu prednost primjene strojnog učenja u radiologiji te iznimnu kompatibilnost u radu radiologa i strojnog učenja, čime se postižu precizne i brze dijagnoze. Sve je to veliki poticaj za daljnja istraživanja, a tehnološki napredak zasigurno će ubrzati njegovu integraciju u kliničku praksu.
Abstract (english) Introduction: Technological progress has encouraged the increasing use of radiological imaging, and the increase in their number leads to an increased workload for radiologists. Therefore, the driver of the application of artificial intelligence in radiology is considered to be precisely the reduction of the workload of radiologists and the need for ever greater precision and efficiency.
Aim: The aim of this paper is to bring the reader closer to artificial intelligence itself and its application in radiology, especially in magnetic resonance modalities, and how deep learning algorithms accelerate image reconstruction and lead to faster and more accurate results.
Discussion: Numerous studies have confirmed the importance of implementing machine learning, a subset of artificial intelligence, in the radiology system. In this review paper, numerous researches on the application of deep learning in magnetic resonance imaging are highlighted, and the emphasis is on models for automatic segmentation. Automatic segmentation has shown excellent results in the early detection of osteoarthritis, then in anterior cruciate ligament and meniscus tears, the most common knee injuries, and also more recently, the deep learning model has excelled in automatic bone age estimation. Automatic segmentation achieved, above all, high accuracy and precision, objectivity and exceptional time saving.
Conclusion: Previous research has already highlighted the significant advantage of using machine learning in radiology and the exceptional compatibility between the work of radiologists and machine learning, which achieves precise and quick diagnoses. All this is a great incentive for further research, and technological progress will certainly speed up its integration into clinical practice.
Keywords
automatizirana segmentacija
duboko učenje
MRI
umjetna inteligencija
Keywords (english)
artificial intelligence
automated segmentation
deep learning
MRI
Language croatian
URN:NBN urn:nbn:hr:176:234021
Study programme Title: Radiologic Technology (university/undergraduate) Study programme type: university Study level: undergraduate Academic / professional title: sveučilišni prvostupnik/prvostupnica (baccalaureus/baccalaurea) radiološke tehnologije (sveučilišni prvostupnik/prvostupnica (baccalaureus/baccalaurea) radiološke tehnologije)
Type of resource Text
File origin Born digital
Access conditions Open access
Terms of use
Created on 2023-07-17 09:50:30