Jaradat JH, Nashwan AJ. Revolutionizing disease diagnosis and management: Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification. World J Clin Cases 2024; 12(17): 2921-2924 [PMID: 38898864 DOI: 10.12998/wjcc.v12.i17.2921]
Corresponding Author of This Article
Abdulqadir J Nashwan, MSc, Research Scientist, Department of Nursing, Hamad Medical Corporation, Rayyan Road, Doha 3050, Qatar. anashwan@hamad.qa
Research Domain of This Article
Methodology
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Clin Cases. Jun 16, 2024; 12(17): 2921-2924 Published online Jun 16, 2024. doi: 10.12998/wjcc.v12.i17.2921
Revolutionizing disease diagnosis and management: Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification
Jaber H Jaradat, Abdulqadir J Nashwan
Jaber H Jaradat, Faculty of Medicine, Mutah University, Al-Karak 61101, Jordan
Abdulqadir J Nashwan, Department of Nursing, Hamad Medical Corporation, Doha 3050, Qatar
Author contributions: Jaradat JH and Nashwan AJ contributed to the manuscript's conceptualization, writing, editing, and literature review. All authors have read and approved the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Abdulqadir J Nashwan, MSc, Research Scientist, Department of Nursing, Hamad Medical Corporation, Rayyan Road, Doha 3050, Qatar. anashwan@hamad.qa
Received: February 26, 2024 Revised: April 4, 2024 Accepted: April 18, 2024 Published online: June 16, 2024 Processing time: 98 Days and 22.4 Hours
Abstract
Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) techniques, such as convolutional neural networks (CNNs), have emerged as transformative technologies with vast potential in healthcare. Body iron load is usually assessed using slightly invasive blood tests (serum ferritin, serum iron, and serum transferrin). Serum ferritin is widely used to assess body iron and drive medical management; however, it is an acute phase reactant protein offering wrong interpretation in the setting of inflammation and distressed patients. Magnetic resonance imaging is a non-invasive technique that can be used to assess liver iron. The ML and DL algorithms can be used to enhance the detection of minor changes. However, a lack of open-access datasets may delay the advancement of medical research in this field. In this letter, we highlight the importance of standardized datasets for advancing AI and CNNs in medical imaging. Despite the current limitations, embracing AI and CNNs holds promise in revolutionizing disease diagnosis and treatment.
Core Tip: This editorial emphasizes the revolutionary impact of artificial intelligence (AI), particularly machine learning and deep learning techniques like convolutional neural networks (CNNs), in healthcare. Highlighting the limitations of traditional, slightly invasive blood tests for assessing body iron load, it advocates for magnetic resonance imaging 's non-invasive advantages. The editorial underscores the critical role of AI and CNNs in improving disease diagnosis and treatment, especially through the precise detection of minor changes in liver iron levels. However, it points out the significant hurdle of lacking open-access datasets, which hampers medical research progress. The call for standardized datasets is a crucial step towards leveraging AI in medical imaging, promising to transform patient care and management.
