Revolutionizing disease diagnosis and management: Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification

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.