Medical imaging datasets, preparation, and availability for artificial intelligence in medical imaging

• Published in: JAD reports Vol. 8; no. 1; pp. 1471 - 1483
• Main Authors: Alabduljabbar, Abdulrahman, Khan, Sajid Ullah, Alsuhaibani, Anas, Almarshad, Fahdah, Altherwy, Youssef N
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2024
• Subjects: dataset preparation; medical imaging dataset; dataset curation tools; Alzheimer's disease; medical image analysis
• ISSN: 2542-4823; 2542-4823
• DOI: 10.3233/ADR-240129

Background Artificial intelligence (AI) persists as a focal subject within the realm of medical imaging, heralding a multitude of prospective applications that span the comprehensive imaging lifecycle. However, a key hurdle to the development and real-world application of AI algorithms is the necessity for large amounts of well-organized and carefully planned training data, including professional annotations (labelling). Modern supervised AI techniques require thorough data curation to efficiently train, validate, and test models. Objective The proper processing of medical images for use by AI-driven solutions is a critical component in the development of dependable and resilient AI algorithms. Currently, research organizations and corporate entities frequently confront data access limits, working with small amounts of data from restricted geographic locations. Methods This study provides an in-depth examination of the publicly accessible datasets in the field of medical imaging. This work also determines the methods required for preparing medical imaging data for the development of AI algorithms, emphasizes current limitations in dataset curation. Furthermore, this study explores inventive strategies to address the challenge of data availability, offering a detailed overview of data curation technologies. Results This study provides a comprehensive evaluation of medical imaging datasets emphasizes their vital significance in improving diagnostic accuracy and AI models, while also addressing key problems such as dataset diversity, labelling, and ethical implications. Conclusions The paper concludes with an insightful discussion and analysis of challenges in medical image analysis, along with potential future directions in the field.