AI-assisted ophthalmic imaging for early detection of neurodegenerative diseases
Dublin Core
Title
AI-assisted ophthalmic imaging for early detection of neurodegenerative diseases
Subject
Artificial intelligence, AI, Retinal biomarkers, Neurodegeneration, Optical coherence tomography, OCT,
Alzheimer's, Parkinson's, Early diagnosis, Ophthalmic imaging
Alzheimer's, Parkinson's, Early diagnosis, Ophthalmic imaging
Description
Abstract
Background Artificial intelligence (AI) plays a promising role in ophthalmic imaging by providing innovative,
non-invasive tools for the early detection of neurodegenerative diseases such as Alzheimer’s disease (AD) and
Parkinson’s disease (PD). Since early diagnosis is crucial for slowing disease progression and improving patient
outcomes, leveraging AI-assisted ophthalmic imaging retinal imaging can enhance detection accuracy and clinical
decision-making.
Methods This review examines clinical applications of AI in identifying retinal biomarkers associated with
neurodegenerative diseases. Relevant data was gathered through a comprehensive literature review using PubMed,
ScienceDirect, and Google Scholar to evaluate studies utilizing AI algorithms for retinal imaging analysis, focusing on
diagnostic performance, sensitivity, specificity, and clinical relevance.
Results AI-assisted ophthalmic imaging retinal imaging enhances the early identification of neurodegenerative
diseases by detecting microscopic structural and vascular changes in the retina. Studies have demonstrated that
AI models analyzing Optical Coherence Tomography (OCT) and fundus images achieve high diagnostic accuracy.
Studies have reported an area under the curve (AUC) of up to 0.918 in PD detection, with sensitivity ranging from 80
to 100% and specificity up to 85%. Similarly, AI-assisted OCT angiography (OCT-A) analysis has successfully identified
retinal vascular alterations in AD patients, correlating with cognitive decline and an AUC of 0.73–0.91. These findings
highlight AI’s potential to detect preclinical disease stages before significant neurological symptoms manifest.
Discussion The integration of AI technologies into ophthalmic imaging holds the potential to improve early
diagnosis and transform patient outcomes. However, challenges such as model interpretability, dataset biases, and
ethical considerations must be addressed to ensure the responsible integration of AI into clinical practice. Future
research should focus on refining AI algorithms, integrating multimodal imaging techniques, and developing
predictive biomarkers to optimize early intervention strategies for neurodegenerative diseases.
Clinical trial number Not applicable.
Keywords Artificial intelligence, AI, Retinal biomarkers, Neurodegeneration, Optical coherence tomography, OCT,
Alzheimer's, Parkinson's, Early diagnosis, Ophthalmic imaging
Background Artificial intelligence (AI) plays a promising role in ophthalmic imaging by providing innovative,
non-invasive tools for the early detection of neurodegenerative diseases such as Alzheimer’s disease (AD) and
Parkinson’s disease (PD). Since early diagnosis is crucial for slowing disease progression and improving patient
outcomes, leveraging AI-assisted ophthalmic imaging retinal imaging can enhance detection accuracy and clinical
decision-making.
Methods This review examines clinical applications of AI in identifying retinal biomarkers associated with
neurodegenerative diseases. Relevant data was gathered through a comprehensive literature review using PubMed,
ScienceDirect, and Google Scholar to evaluate studies utilizing AI algorithms for retinal imaging analysis, focusing on
diagnostic performance, sensitivity, specificity, and clinical relevance.
Results AI-assisted ophthalmic imaging retinal imaging enhances the early identification of neurodegenerative
diseases by detecting microscopic structural and vascular changes in the retina. Studies have demonstrated that
AI models analyzing Optical Coherence Tomography (OCT) and fundus images achieve high diagnostic accuracy.
Studies have reported an area under the curve (AUC) of up to 0.918 in PD detection, with sensitivity ranging from 80
to 100% and specificity up to 85%. Similarly, AI-assisted OCT angiography (OCT-A) analysis has successfully identified
retinal vascular alterations in AD patients, correlating with cognitive decline and an AUC of 0.73–0.91. These findings
highlight AI’s potential to detect preclinical disease stages before significant neurological symptoms manifest.
Discussion The integration of AI technologies into ophthalmic imaging holds the potential to improve early
diagnosis and transform patient outcomes. However, challenges such as model interpretability, dataset biases, and
ethical considerations must be addressed to ensure the responsible integration of AI into clinical practice. Future
research should focus on refining AI algorithms, integrating multimodal imaging techniques, and developing
predictive biomarkers to optimize early intervention strategies for neurodegenerative diseases.
Clinical trial number Not applicable.
Keywords Artificial intelligence, AI, Retinal biomarkers, Neurodegeneration, Optical coherence tomography, OCT,
Alzheimer's, Parkinson's, Early diagnosis, Ophthalmic imaging
Creator
Hajar Nasir Tukur1,2, Olivier Uwishema1,5* , Hatice Akbay1,3, Dalal Sheikhah1,2 and Inês Filipa Silva Correia1,4
Source
https://doi.org/10.1186/s12245-025-00870-y
Date
2025
Contributor
Peri Irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Hajar Nasir Tukur1,2, Olivier Uwishema1,5* , Hatice Akbay1,3, Dalal Sheikhah1,2 and Inês Filipa Silva Correia1,4, “AI-assisted ophthalmic imaging for early detection of neurodegenerative diseases,” Repository Horizon University Indonesia, accessed April 11, 2026, https://repository.horizon.ac.id/items/show/12740.