By developing diagnostic techniques, adopting a multi-modal approach, encouraging cooperation, contributing to standardisation and validation, and making it easier to transfer information and put it into practice, the project is in line with national and global research activities. The initiative strengthens global efforts to improve early identification and management of Alzheimer’s disease (AD) by enhancing the body of already existing research in the subject.
The primary objective of this research is to create an Artificial intelligence as a/in a medical device (AIaMD) that can detect AD using retinal fundus images or optical coherence tomography (OCT) scans of both eyes to analyse the macula and the head of the optic nerve. With a variety of non-overlapping studies that were labelled as having dementia or not due to Alzheimer’s disease, we will evaluate the trained deep learning model. Below are the innovative aspects of the proposed research:
Early Intervention: Timely interventions and therapies are made possible by early detection, which can delay the onset of AD.
Non-Invasive and Cost-Effective: Retinal scans offer a non-invasive and affordable way for locating possible biomarkers, making it available for extensive screenings and standard assessments.
Population Screening: The strategy may allow for population-level tests that would be able to identify at-risk people before any observable cognitive signs appeared.
Enhanced Diagnostic Accuracy: The integration of numerous retinal characteristics and modalities enhances diagnostic accuracy, completing current approaches and lowering the possibility of misdiagnosis.