Retinal imaging and artificial intelligence: A systematic review and meta-analysis of diagnostic techniques for neurodegenerative diseases.

BACKGROUND

Artificial intelligence (AI)-driven analysis of retinal images holds promise for noninvasive, early detection of neurodegenerative disorders. We conducted a prospectively registered systematic review and meta‑analysis to quantify diagnostic accuracy of AI‑assisted retinal imaging across Alzheimer's disease (AD), Parkinson's disease (PD), and related conditions.

METHODS

We searched six databases for studies (2010-2024) employing AI/ML models for binary classification of neurodegenerative disease versus healthy controls. Risk of bias was assessed using QUADAS-2. We extracted pooled area under the receiver operating characteristic curve (AUC) with 95 % confidence intervals (CIs), heterogeneity statistics, and participant numbers. Certainty of evidence was graded using GRADE criteria.

RESULTS

Our analysis included ten studies (seven on AD, four on PD; 496 patients, 441 patients, and 36,990 healthy controls). The overall pooled AUC was 0.73 (95 % CI, 0.69-0.77; I²=78 %). Subgroup analyses showed an AUC of 0.72 for AD and 0.70 for PD. QUADAS-2 assessments indicated low-to-moderate risk of bias, with patient selection variability being a key concern. GRADE evaluations showed moderate to high certainty in the evidence.

CONCLUSIONS

AI‑assisted retinal imaging demonstrates consistent, moderate accuracy for early detection of AD and PD. Future research should standardize imaging protocols and patient selection criteria, and pursue large-scale, prospective validation.