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Gostaresh Afzar HamaraFrontiers in Health Informatics2676-71049120200517Deep Learning Applications in Analyzing Ultrasound Images of Thyroid Nodules: Protocol for a Systematic Review22022010.30699/fhi.v9i1.220ENYasamanSharifiPh.D. Student of Medical Informatics, Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad. Sharifiy961@mums.ac.irSaeedEslami HassanAbadyMortezaDanai AshgzariMahdiSargolzaei2020042620200517Introduction: Ultrasound images are one of the main contributors for evaluating of thyroid nodules. However, reading ultrasound imaging is not easy and strongly depends to doctors’ experiences. Therefore, a CAD system could assist doctors in evaluating thyroid ultrasound images to reduce the impact of subjective experience on the diagnostic results. Objective: with the best of our knowledge there is not any articles that actually provide a systematic review of deep learning application in analyzing ultrasound images of thyroid nodules and Hence, a comprehensive review of studies in this field can be useful, therefore the protocol of this systematic Review will be presented to reach this goal.Method: This protocol includes five stages: research questions definition, search strategy design, study selection, quality assessment and data extraction. We developed search for relevant English language articles using the PubMed, Scopus and Science Direct. Inclusion and exclusion criteria were defined and flow diagram is conducted, from 623 studies retrieved, 27 studies were included, after quality assessment data was extracted based on defined categories.Result: The result of this systematic review can help researchers by comprehensive view and the summary of evidence to present new ideas and further research and represent a state of the art in this field.Conclusion: in this study a protocol was used for doing a systematic review on various deep learning applications in thyroid ultrasound such as feature selection, classification, localization, detection and segmentation. Articles were screened based on the following items: study and patient information, dataset, method, results and comparison method.

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