Aptar Digital Health has signed a licensing agreement with AstraZeneca for detecting renal, cardiovascular and metabolic conditions such as chronic kidney disease (CKD), using the latter’s AI-driven screening algorithms.
The collaboration aims to integrate these algorithms into routine eye examinations, facilitating the diagnosis of CKD.
AstraZeneca’s AI algorithms, developed based on biomarkers and diagnosis data points, have shown promise in broadening the scope of CKD screening.
AstraZeneca cardiovascular, renal and metabolism senior vice-president Mina Makar stated: "CKD remains one of the most significant global health challenges, with persistently low diagnosis rates.
“This partnership with Aptar represents an exciting opportunity to drive innovation in CKD management and our commitment to enhance diagnostics in novel ways. We are collaborating at scale to deliver sustainable solutions for millions of people living with CKD worldwide."
Aptar Digital Health noted that ophthalmologists will soon be able to employ these algorithms during fundus imaging for early screening and detection of CKD indicators.
Post-development, the AI-driven solution will undergo clinical assessment to ensure its secure market access and deployment via stakeholders, including pharma firms, ophthalmic equipment manufacturers and healthcare systems.
According to Aptar Digital, CKD often goes undetected by patients and healthcare professionals.
While diabetes and hypertension are common contributors to CKD, other causes include glomerulonephritis, infections and environmental factors such as air pollution and exposure to certain chemicals. Genetic factors may also influence CKD risk.
Aptar Digital health president Romain Marmot stated: “With early detection and timely treatment, patients are likely to experience better health outcomes.
“This collaboration aims to enhance the overall quality of care for patients with CKD by providing an innovative and effective early detection tool.”
In April 2025, AstraZeneca, Tempus and Pathos AI made a multi-year agreement to develop a large-scale multimodal deep learning model to expedite the discovery of cancer treatments.
