A recent study reveals that machine learning (ML) models can help identify women at risk of severe cognitive decline during menopause, offering a more affordable and efficient approach to managing cognitive health. Published today in Menopause, the journal of The Menopause Society, the study demonstrates how AI-driven models could be instrumental in the early detection of subjective cognitive decline, a condition linked to higher risks of neurodegenerative diseases, such as Alzheimer’s.
Subjective cognitive decline refers to individuals’ self-reported experiences of memory or cognitive function deterioration. It is one of the more common and troubling symptoms women face during the menopause transition. This decline can significantly impact a woman’s quality of life and may signal a heightened risk of serious long-term cognitive disorders.
Previous research has pointed to several risk factors for cognitive decline, including aging, obesity, hypertension, and depression. However, current models predominantly focus on dementia, a disease for which there are limited treatment options once diagnosed. While subjective cognitive decline does not always predict dementia, having a model capable of predicting cognitive health trends could facilitate early interventions that protect women’s brain health.
Traditionally, cognitive performance tests have relied on complex and costly diagnostic tools, such as blood tests, brain imaging, and other laboratory indicators. These models can be difficult to implement in everyday clinical practice due to their high cost and complexity. In contrast, questionnaire-based models, which rely on factors like sociodemographic information, lifestyle habits, mental health, and menstrual history, offer a more practical and cost-effective alternative.
The emerging field of machine learning, which excels at analyzing large datasets and uncovering patterns, has shown significant promise in cognitive health. By automating the analysis of complex relationships between variables, machine learning models can create reliable, scalable predictions. In this study, researchers analyzed data from over 1,200 women undergoing the menopause transition. They developed and validated an ML model capable of identifying those experiencing severe subjective cognitive decline and the associated risk factors.
The study’s findings mark an important step forward in improving cognitive health interventions for women navigating menopause. By offering a clearer picture of risk, this approach could lead to more targeted, early interventions aimed at preserving cognitive function. However, the researchers emphasize that further validation is needed, as well as exploration into other potential influencing factors, before the model can be widely adopted in clinical settings.
You Might Be Interested In:
-
Cesarean Deliveries During Labor Linked to Risks in Future Pregnancies
-
AI-Driven Tool Revolutionizes Placenta Examination at Bedside
