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The impact of Deep learning algorithms with MRI Systematic review

The current trend in the medical diagnosis world is aiming towards utilizing Artificial Intelligence (AI) in the medical imaging field to highlight what is supposed to be abnormal signs and abnormal structures in the medical image. In our study we construct a systematic review to gather the deep learning algorithms, that have a direct impact with MRI in one single article, to analyze, synthesize and focus on their advantages, and limitations, to ensure that the future researchers will try to get over these limitations, and to take the benefits from the previous. We used Ramey and Rao’s that separate the research into two main phases, In the first phase, we are planning to review the published literature on the deep learning algorithms and their impact with MRI, to identify the main algorithms to identify the main limitations and gaps in the current literature. In the second phase conducting the review, this includes how we collect the data, what is the search keywords, the databases where used, and what is inclusion and exclusion criteria. Then we extract the main themes in our researches and build taxonomy connect the whole articles with their themes. And in conclusion we can conclude the deep learning algorithms and their impact with MRI can facilitate the image diagnosis and can also make an improvement on the images whether in resolution or get thinner slice thickness from the thicker one, furthermore, they can enhance the decision-making process that has a direct impact with patient care.