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Artificial intelligence in magnetic resonance imaging of the knee

Petra Kujundžić ; University of Split, University Department of Health Studies, Split, Croatia
Tatjana Matijaš orcid id ; University of Split, University Department of Health Studies, Split, Croatia

Puni tekst: engleski pdf 1.038 Kb

str. 37-45

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Puni tekst: hrvatski pdf 1.038 Kb

str. 37-45

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Introduction: Technological progress leads to an increasing use of radiological imaging, and an increase in the number of imaging results in an increased workload for radiologists. The driver of the application of AI in radiology is considered to be the reduction of the workload of radiologists and the need for faster and more accurate diagnosis.
Aim: The aim of this paper is to bring the reader closer to the implementation of AI in radiology, especially in the MRI modality, and how deep learning algorithms improve image reconstruction.
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 more recently, the deep learning model has excelled in automatic bone age estimation. Automatic segmentation has achieved, above all, high accuracy and precision, objectivity and 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.

Ključne riječi

artificial intelligence; automatic segmentation; deep learning; MRI

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Podaci na drugim jezicima: hrvatski

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