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Vol. 1 No. 2 (2024)

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December 31, 2024

Copyright (c) 2024 Shaykhah Abdulaziz Majed Aldawsari, Mohamed Ali Abdallah Hawi, Nadiah Othman Mohammed Moussa Hakami, Hajar Yahya Ali Nabbash, Faizah Ali Hadadi, Abdulrahman Raja Al Harbi, Ashwag Owid Alenezi, Mohammed Ayidh Alotaibi, Mohammed Abuhadeen Aljubran, Hassan Abdoh Mohammed Sawadi, Mohammed Dughyyim Alharbi, Ahmed Mohammed Eshwi, Fawaz Abdullah Alshammari

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Artificial Intelligence-Based Early Warning Systems for Preeclampsia: A Narrative Review of the Integration of Ocular Signs and Oral Health

https://doi.org/10.64483/202412705
Shaykhah Abdulaziz Majed Aldawsari
King Salman Hospital, ministry of health, Saudi Arabia
Mohamed Ali Abdallah Hawi
Sabya General Hospital, Jizan, ministry of health , Saudi Arabia
Nadiah Othman Mohammed Moussa Hakami
KFCH Jizan, ministry of health , Saudi Arabia
Hajar Yahya Ali Nabbash
Damad General Hospital, ministry of health , Saudi Arabia
Faizah Ali Hadadi
Al-Jouf Specialized Dental Center, Ministry of Health, Saudi Arabia
Abdulrahman Raja Al Harbi
First Health Cluster - School Health – Riyadh, Ministry of Health, Saudi Arabia
Ashwag Owid Alenezi
Hail Women's and Maternity Hospital, Hail Health Cluster, ministry of health , Saudi Arabia
Mohammed Ayidh Alotaibi
Prince Mohammed bin Abdulaziz, ministry of health , Saudi Arabia
Mohammed Abuhadeen Aljubran
branch in Riyadh, ministry of health , Saudi Arabia
Hassan Abdoh Mohammed Sawadi
Irada Mental Health Hospital in Jazan, Jazan Health Cluster, ministry of health , Saudi Arabia
Mohammed Dughyyim Alharbi
Al-Kharj Maternity and Children's Hospital, ministry of health , Saudi Arabia
Ahmed Mohammed Eshwi
Jazan Health Cluster -school health Department, ministry of health , Saudi Arabia
Naimah jazem alruwaili
Tarif General Hospital, Nursing Technician ,ministry of health, Saudi Arabia

Corresponding Author(s) : Shaykhah Abdulaziz Majed Aldawsari

Madawealdosari@gmail.com

Saudi Journal of Medicine and Public Health, Vol. 1 No. 2 (2024)

Abstract

Background: Preeclampsia (PE) remains a leading cause of maternal and perinatal morbidity and mortality worldwide. Its pathophysiology involves systemic endothelial dysfunction and inflammation, yet early detection remains challenging due to heterogeneous clinical presentations. Aim: This narrative review synthesizes evidence on AI-based early warning systems (AI-EWS) for PE by integrating novel data streams: ocular signs (optometry), periodontal disease (dentistry), vital signs and patient education (nursing), electronic health records (health informatics), and pharmacologic protocols (pharmacy), within maternal and child health (M&CH) risk stratification. Methods: A structured literature search (2015–2024) in PubMed, Scopus, and IEEE Xplore identified 40 peer-reviewed articles. Thematic synthesis across disciplines was performed. Results: Retinal vascular changes detected via deep learning, periodontal inflammatory markers, and longitudinal vital sign trajectories improve AI-EWS predictive accuracy (AUC 0.85–0.94). Nursing-led education enhances adherence to monitoring. Pharmacy protocols integrated with AI alerts optimize timely magnesium sulfate administration. Conclusion: Multidisciplinary AI-EWS can transform antenatal surveillance, but prospective validation and implementation science are needed. 


 

Keywords

Preeclampsia; artificial intelligence; early warning system; retinal imaging; periodontal disease; maternal health.
Aldawsari, S. A. M., Mohamed Ali Abdallah Hawi, Nadiah Othman Mohammed Moussa Hakami, Hajar Yahya Ali Nabbash, Faizah Ali Hadadi, Abdulrahman Raja Al Harbi, … Naimah jazem alruwaili. (2024). Artificial Intelligence-Based Early Warning Systems for Preeclampsia: A Narrative Review of the Integration of Ocular Signs and Oral Health. Saudi Journal of Medicine and Public Health, 1(2), 2479–2487. https://doi.org/10.64483/202412705
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