Deep learning applications in analyzing ultrasound images of thyroid nodules: Protocol for systematic review

Publish Year: 1399
نوع سند: مقاله ژورنالی
زبان: English
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JR_IJIMI-9-1_008

تاریخ نمایه سازی: 30 مرداد 1401

Abstract:

Introduction: 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. With the best of our knowledgethere 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.Material and Methods: 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 ۶۲۳ studies retrieved, ۲۷ studies were included, after quality assessment data was extracted based on defined categories.Results: The result of this systematic review can help researchers with 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.

Authors

Yasaman Sharifi

PhD Student of Medical Informatics, Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Saeed Eslami Hassan Abady

Associate Professor, Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Morteza Danai Ashgzari

MSc Student of Computer Engineering, Department of Computer, Faculty of Engineering, Islamic Azad University, Mashhad Branch, Mashhad, Iran

Mahdi Sargolzaei

Assistant Professor, Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran