Clinical Report: Efficacy of Artificial Intelligence in Forecasting GDM to T2DM
Overview
This systematic review and meta-analysis evaluate the effectiveness of artificial intelligence (AI) in predicting the transition from gestational diabetes mellitus (GDM) to type 2 diabetes mellitus (T2DM).
Background
Gestational diabetes mellitus (GDM) significantly increases the risk of developing type 2 diabetes mellitus (T2DM) in women, with a reported progression rate of 30%-50% within 5 to 10 years postpartum. The rising prevalence of GDM necessitates effective screening and intervention strategies.
Data Highlights
No specific numerical data or trial results were provided in the source material.
Key Findings
Women with a history of GDM have a tenfold higher risk of developing T2DM compared to those with normoglycemic pregnancies.
30%-50% of women with previous GDM develop T2DM within 5 to 10 years postpartum.
The annual rate of progression from GDM to T2DM is estimated at 9.6% based on a meta-analysis of 170,139 women.
Compliance with postpartum diabetes screening recommendations is low, reported at 16%-19%.
AI models have shown potential in identifying women at risk for T2DM following GDM.
Clinical Implications
Structured follow-up and screening for women with a history of GDM are important to prevent the onset of T2DM.
Conclusion
The use of AI in predicting the transition from GDM to T2DM is being explored for its potential in postpartum care.
by Alaa Abd-alrazaq, Shahira Padinharepattel Mohamed, Mohannad Alajlani, Aliya Tabassum, José Manuel Ordóñez-Mena, Shehel Yoosuf, Mais Alkhateeb, Arfan Ahmed, Mohammed Bashir, Junaid Qadir, Ali AlSanousi, Javaid Sheikh
