Artificial intelligence in sickle disease

Ahmed Adel Elsabagh; Mohamed Elhadary; Basel Elsayed; Amgad Mohamed Elshoeibi; Khaled Ferih; Rasha Kaddoura; Salam Alkindi; Awni Alshurafa; Mona Alrasheed; Abdullah Alzayed; Abdulrahman Al-Abdulmalek; Jaffer Abduljabber Altooq; Mohamed Yassin

doi:10.1016/j.blre.2023.101102

Artificial intelligence in sickle disease

Blood Rev. 2023 Sep:61:101102. doi: 10.1016/j.blre.2023.101102. Epub 2023 Jun 8.

Affiliations

¹ College of Medicine, QU Health, Qatar University, Doha, Qatar. Electronic address: ae1802661@qu.edu.qa.
² College of Medicine, QU Health, Qatar University, Doha, Qatar.
³ Pharmacy Department, Heart Hospital, Hamad Medical Corporation (HMC), Doha, Qatar.
⁴ Professor of Hematology, Sultan Qaboos University, Oman.
⁵ Department of Hematology, Hamad Medical Corporation (HMC), Doha, Qatar.
⁶ Hematology Unit, Department of Medicine, Aladnan Hospital, Ministry of Health, Kuwait.
⁷ Qatif Central Hospital, Saudi Arabia.
⁸ Internal Medicine Department, Montreal General Hospital, Montreal, Canada.
⁹ Internal Medicine Department, Bahrain Sulaymaniyah Hospital, Bahrain.
¹⁰ Hematology Section, Medical Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha, Qatar. Electronic address: ae1802661@qu.edu.qa.

PMID: 37355428
DOI: 10.1016/j.blre.2023.101102

Abstract

Artificial intelligence (AI) is rapidly becoming an established arm in medical sciences and clinical practice in numerous medical fields. Its implications have been rising and are being widely used in research, diagnostics, and treatment options for many pathologies, including sickle cell disease (SCD). AI has started new ways to improve risk stratification and diagnosing SCD complications early, allowing rapid intervention and reallocation of resources to high-risk patients. We reviewed the literature for established and new AI applications that may enhance management of SCD through advancements in diagnosing SCD and its complications, risk stratification, and the effect of AI in establishing an individualized approach in managing SCD patients in the future. Aim: to review the benefits and drawbacks of resources utilizing AI in clinical practice for improving the management for SCD cases.

Keywords: Artificial intelligence; Convolutional neural networks; Hemoglobinopathies; Machine learning; Sickle cell.

Publication types

Review