Volv Global presented two posters at ATS 2025, in collaboration with Takeda
The posters were the result of work to assess how machine learning models can be applied to large clinical datasets to identify patients with Alpha-1 Antitrypsin Deficiency (AATD), a rare disease where patient identification can be challenging because it is difficult to distinguish the signs and symptoms of chronic obstructive pulmonary disease and/or liver disease associated with AATD from those seen in other lung/liver disorders.
Application of an AI model to detect Alpha-1 Antitrypsin Deficiency: Model Performance
This poster outlines how our machine learning models could distinguish patients with AATD from patients with similar conditions without AATD or randomly selected controls with high sensitivity, specificity, and accuracy.
Application of an AI model to detect Alpha-1 Antitrypsin Deficiency: Characterising the Study Population
This poster outlines how we were able to establish a robust machine learning model that can demonstrate the phenotypic and treatment patterns among the different cohorts of patients confirmed to have AATD and thereby accurately identify and differentiate
undiagnosed patients from diagnosed patients.
