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10.1016_j.blre.2023.101133.pdf (1.73 MB)

Using artificial intelligence to improve body iron quantification: A scoping review

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submitted on 2023-11-22, 09:16 and posted on 2023-11-22, 10:01 authored by Abdulqadir J. Nashwan, Ibraheem M. Alkhawaldeh, Nour Shaheen, Ibrahem Albalkhi, Ibrahim Serag, Khalid Sarhan, Ahmad A. Abujaber, Alaa Abd-Alrazaq, Mohamed A. Yassin

This scoping review explores the potential of artificial intelligence (AI) in enhancing the screening, diagnosis, and monitoring of disorders related to body iron levels. A systematic search was performed to identify studies that utilize machine learning in iron-related disorders. The search revealed a wide range of machine learning algorithms used by different studies. Notably, most studies used a single data type. The studies varied in terms of sample sizes, participant ages, and geographical locations. AI's role in quantifying iron concentration is still in its early stages, yet its potential is significant. The question is whether AI-based diagnostic biomarkers can offer innovative approaches for screening, diagnosing, and monitoring of iron overload and anemia.

Other Information

Published in: Blood Reviews
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.blre.2023.101133

Additional institutions affiliated with: Artificial Intelligence (AI) Center for Precision Health - WCM-Q

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

Elsevier

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Medical Corporation
  • Hazm Mebaireek General Hospital - HMC
  • Hamad General Hospital - HMC
  • Qatar University
  • Qatar University Health - QU
  • College of Health Sciences - QU HEALTH
  • Weill Cornell Medicine - Qatar