Ai reveals the differences between women and men for the diagnosis of Alzheimer and Parkinson’s

The application of the AI ​​has made it possible to identify and differentiate the main predictive factors for men and womendeepening the understanding of how sex influences the development and progression of these pathologies.

Research is the result of an interdisciplinary collaboration that includes important institutes and universities, underlining the importance of a joint and multidisciplinary approach in the treatment of neurodegenerative diseases. Among the collaborators include the Milan 4 research area of ​​the CNR, the Mondino Foundation, the University of Pavia, and other prestigious institutions.

“The novelty of the study consists in having adopted an integrated approach in the analysis of the tests, consistently with the theory we developed at the CNR-Istc, according to which both pathologies -alzheimer and Parkinson-could be manifestations of a single disease, called neurodegenerative Elderly Syndrome (NES)“, Explains the scientific manager of the research, Daniele Caligiore, research manager at the CNR-Istc and director of the Advanced School in Artificial Intelligence (As-Ai)a post-graduate school organized by CNR-Istc and its spin-off ai2Life srl and dedicated to studying and interdisciplinary application of the AI. “In the analysis of the tests we started from analyzing the differences between healthy patients and sick patients, regardless of whether they were men or women: in fact, there are many studies that compare the outcome of the predictive tests on the basis of the genre, but do not consider that some characteristics can be relevant for both groups, regardless of the absolute values ​​of test scores. Our research face this problem for the first time through an explainable machine learning algorithm, that is, able to make the used decision -making process transparent, increasing reliability and promoting adoption in the medical field“.

For Alzheimer’s diseasethe algorithm highlighted significant differences in the predictors of onset between men and women, such as memory and orientation. “The machine learning system that we developed shows how MMSE is a more effective predictor than Alzheimer’s in women, while in men it is essential for long -term monitoring. Lideltotal is more predictive in women for the onset of the disease, while Avtot is more relevant in men. In addition, the level of education affects differently on Alzheimer’s risk, with women presenting a greater risk“, The researcher continues. For Parkinson’s, however, Factors such as muscle rigidity and neurological dysfunctions have emerged as critics, with relevant variations between the sexes. These results could guide the development of more personalized and targeted treatment strategies.

The project demonstrates the importance of considering the genre as a crucial variable in medical research, proposing a model that could significantly improve the diagnostic and therapeutic effectiveness for patients with these serious conditions.

The innovative approach adopted by the CNR-Istc is an example of how artificial intelligence can be used to face some of the most complex challenges in medicine. With the continuous integration of new technologies and interdisciplinary collaboration, the future of diagnosis and treatment of neurodegenerative diseases appears increasingly oriented towards personalization and effectiveness.