Subscribe to RSS
DOI: 10.1055/a-2792-8597
Artificial Intelligence and Ocular Imaging in the Evaluation of Neurologic Disorders: The New Era of Neuro-Oculomics?
Authors
Abstract
Links between the eye and the central nervous system (CNS) have been recognized since the origins of the ophthalmoscope. Owing to the elegant topography of the afferent visual pathway and its close embryonic, anatomical, and physiological connections to the brain, it is possible to capture structural effects of CNS injury in the retina. The availability of large-scale, high-quality retinal imaging datasets and ongoing advances in artificial intelligence (AI) have paved the way for Oculomics, a field in which ocular measures act as biomarkers for systemic diseases. Similarly, ocular images have been used in AI models to provide critical insights about neurologic disorders in the fledgling discipline of what might be considered Neuro-Oculomics. In this review, we will describe key ocular imaging techniques and highlight emerging roles for AI in the diagnosis and management of important neurological conditions.
Keywords
retina - optic nerve - optical coherence tomography - artificial intelligence - oculomics - neuro-oculomics - neurologic disordersPublication History
Received: 10 August 2025
Accepted: 19 January 2026
Article published online:
02 February 2026
© 2026. Thieme. All rights reserved.
Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA
-
References
- 1 Gałęzowski X. Etude ophtalmoscopique sur les altérations du nerf optique et les maladies cérébrales dont elles dépendent. Librairie de L. Leclerc; 1866
- 2 Grzybowski A. Comment on: an eye on the brain: adding insight to injury. Am J Ophthalmol 2024; 261: 208
- 3 Pearce JM. Sir Thomas Clifford Allbutt. J Neurol Neurosurg Psychiatry 2003; 74 (10) 1443
- 4 Allbutt TC. On the use of the ophthalmoscope in diseases of the nervous system and kidneys; also, in certain other general disorders. London and New York: Macmillan; 1871
- 5 Lydston JL. The relationship between the eye and the brain. JAMA 1895; 25 (19) 792-793
- 6 Danesh-Meyer HV. An eye on the brain: adding insight to injury. Am J Ophthalmol 2023; 255: A1-A3
- 7 Costello F. The afferent visual pathway: designing a structural-functional paradigm of multiple sclerosis. ISRN Neurol 2013; 2013: 134858
- 8 Zhu Z, Wang Y, Qi Z. et al. Oculomics: current concepts and evidence. Prog Retin Eye Res 2025; 106: 101350
- 9 Costello Visual field testing: the key to localizing afferent visual pathway lesions. Neurol Clin, In press
- 10 Li T, Bo W, Hu C. et al. Applications of deep learning in fundus images: a review. Med Image Anal 2021; 69: 101971
- 11 Shin HJ, Costello F. Imaging the optic nerve with optical coherence tomography. Eye (Lond) 2024; 38 (12) 2365-2379
- 12 Bruce BB, Lamirel C, Wright DW. et al. Nonmydriatic ocular fundus photography in the emergency department. N Engl J Med 2011; 364 (04) 387-389
- 13 Bruce BB, Thulasi P, Fraser CL. et al. Diagnostic accuracy and use of nonmydriatic ocular fundus photography by emergency physicians: phase II of the FOTO-ED study. Ann Emerg Med 2013; 62 (01) 28-33.e1
- 14 Bruce BB, Bidot S, Hage R. et al. Fundus photography vs. ophthalmoscopy outcomes in the emergency department (FOTO-ED) phase III: web-based, in-service training of emergency providers. Neuroophthalmology 2018; 42 (05) 269-274
- 15 Sathianvichitr K, Lamoureux O, Nakada S. et al. Through the eyes into the brain, using artificial intelligence. Ann Acad Med Singap 2023; 52 (02) 88-95
- 16 Tukur HN, Uwishema O, Akbay H, Sheikhah D, Correia IFS. AI-assisted ophthalmic imaging for early detection of neurodegenerative diseases. Int J Emerg Med 2025; 18 (01) 90
- 17 Girach Z, Sarian A, Maldonado-García C. et al. Retinal imaging for the assessment of stroke risk: a systematic review. J Neurol 2024; 271 (05) 2285-2297
- 18 Yan Y, Ludwig CA, Liao YJ. Multimodal imaging features of optic disc drusen. Am J Ophthalmol 2021; 225: 18-26
- 19 Graven-Nielsen M, Dubra A, Dodd RL, Hamann S, Moss HE. Application of novel non-invasive ophthalmic imaging to visualize peripapillary wrinkles, retinal folds and peripapillary hyperreflective ovoid mass-like structures associated with elevated intracranial pressure. Front Neurol 2024; 15: 1383210
- 20 Sugiyama T, Araie M, Riva CE, Schmetterer L, Orgul S. Use of laser speckle flowgraphy in ocular blood flow research. Acta Ophthalmol 2010; 88 (07) 723-729
- 21 Leong YY, Vasseneix C, Finkelstein MT, Milea D, Najjar RP. Artificial intelligence meets neuro-ophthalmology. Asia Pac J Ophthalmol (Phila) 2022; 11 (02) 111-125
- 22 Chew BH, Ngiam KY. Artificial intelligence tool development: what clinicians need to know?. BMC Med 2025; 23 (01) 244
- 23 Linde G, Rodrigues de Souza Jr W, Chalakkal R, Danesh-Meyer HV, O'Keeffe B, Chiong Hong S. A comparative evaluation of deep learning approaches for ophthalmology. Sci Rep 2024; 14 (01) 21829
- 24 Zipori AB, Kerley CI, Klein A, Kenney RC. Real-world translation of artificial intelligence in neuro-ophthalmology: the challenges of making an artificial intelligence system applicable to clinical practice. J Neuroophthalmol 2022; 42 (03) 287-291
- 25 Costin H-N, Fira M, Goraș L. Artificial intelligence in ophthalmology: advantages and limits. Appl Sci (Basel) 2025; 15 (04) 1913
- 26 Zhu Z, Shi D, Guankai P. et al. Retinal age gap as a predictive biomarker for mortality risk. Br J Ophthalmol 2023; 107 (04) 547-554
- 27 Zhu Z, Hu W, Chen R. et al. Retinal age gap as a predictive biomarker of stroke risk. BMC Med 2022; 20 (01) 466
- 28 Grimbly MJ, Koopowitz SM, Chen R. et al. Estimating biological age from retinal imaging: a scoping review. BMJ Open Ophthalmol 2024; 9 (01) e001794
- 29 Baecker L, Garcia-Dias R, Vieira S, Scarpazza C, Mechelli A. Machine learning for brain age prediction: Introduction to methods and clinical applications. EBioMedicine 2021; 72: 103600
- 30 Stefaniak JD, Mak E, Su L. et al. Brain age gap, dementia risk factors and cognition in middle age. Brain Commun 2024; 6 (06) fcae392
- 31 Skattebøl L, Nygaard GO, Leonardsen EH. et al. Brain age in multiple sclerosis: a study with deep learning and traditional machine learning. Brain Commun 2025; 7 (03) fcaf152
- 32 Chan E, Tang Z, Najjar RP. et al; Bonsai Group. A deep learning system for automated quality evaluation of optic disc photographs in neuro-ophthalmic disorders. Diagnostics (Basel) 2023; 13 (01) 160
- 33 Li M, Wan C. The use of deep learning technology for the detection of optic neuropathy. Quant Imaging Med Surg 2022; 12 (03) 2129-2143
- 34 Liu TYA, Ting DSW, Yi PH. et al. Deep learning and transfer learning for optic disc laterality detection: implications for machine learning in neuro-ophthalmology. J Neuroophthalmol 2020; 40 (02) 178-184
- 35 Echegaray S, Zamora G, Yu H, Luo W, Soliz P, Kardon R. Automated analysis of optic nerve images for detection and staging of papilledema. Invest Ophthalmol Vis Sci 2011; 52 (10) 7470-7478
- 36 Akbar S, Akram MU, Sharif M, Tariq A, Yasin UU. Decision support system for detection of papilledema through fundus retinal images. J Med Syst 2017; 41 (04) 66
- 37 Fatima KN, Hassan T, Akram MU, Akhtar M, Butt WH. Fully automated diagnosis of papilledema through robust extraction of vascular patterns and ocular pathology from fundus photographs. Biomed Opt Express 2017; 8 (02) 1005-1024
- 38 Agne J, Wang JK, Kardon RH, Garvin MK. Determining degree of optic nerve edema from color fundus photography. In: Hadjiiski LM, Tourassi GD, eds. Proceedings of Medical Imaging 2015: Computer-Aided Diagnosis; 2015: 94140F1–9
- 39 Ahn JM, Kim S, Ahn KS, Cho SH, Kim US. Accuracy of machine learning for differentiation between optic neuropathies and pseudopapilledema. BMC Ophthalmol 2019; 19 (01) 178
- 40 O'Neill EC, Danesh-Meyer HV, Kong GX. et al; Optic Nerve Study Group. Optic disc evaluation in optic neuropathies: the optic disc assessment project. Ophthalmology 2011; 118 (05) 964-970
- 41 Yang HK, Oh JE, Han SB, Kim KG, Hwang JM. Automatic computer-aided analysis of optic disc pallor in fundus photographs. Acta Ophthalmol 2019; 97 (04) e519-e525
- 42 Milea D, Najjar RP, Zhubo J. et al; BONSAI Group. BONSAI group. artificial intelligence to detect papilledema from ocular fundus photographs. N Engl J Med 2020; 382 (18) 1687-1695
- 43 Vasseneix C, Nusinovici S, Xu X. et al; BONSAI (Brain and Optic Nerve Study With Artificial Intelligence) Group. Deep learning system outperforms clinicians in identifying optic disc abnormalities. J Neuroophthalmol 2023; 43 (02) 159-167
- 44 Vasseneix C, Najjar RP, Xu X. et al; BONSAI Group. Accuracy of a deep learning system for classification of papilledema severity on ocular fundus photographs. Neurology 2021; 97 (04) e369-e377
- 45 Bouthour W, Biousse V, Newman NJ. Diagnosis of optic disc oedema: fundus features, ocular imaging findings, and artificial intelligence. Neuroophthalmology 2023; 47 (04) 177-192
- 46 Biousse V, Newman NJ, Najjar RP. et al; BONSAI (Brain and Optic Nerve Study with Artificial Intelligence) Study Group. Optic disc classification by deep learning versus expert neuro-ophthalmologists. Ann Neurol 2020; 88 (04) 785-795
- 47 Yang HK, Kim YJ, Sung JY, Kim DH, Kim KG, Hwang J-M. Efficacy for differentiating nonglaucomatous versus glaucomatous optic neuropathy using deep learning systems. Am J Ophthalmol 2020; 216: 140-146
- 48 Gu S, Bao T, Wang T. et al. Multimodal AI diagnostic system for neuromyelitis optica based on ultrawide-field fundus photography. Front Med (Lausanne) 2025; 12: 1555380
- 49 Ha A, Sun S, Kim YK. et al. Deep-learning-based enhanced optic-disc photography. PLoS One 2020; 15 (10) e0239913
- 50 Bénard-Séguin É, Nielsen C, Sarhan A. et al; COIL (Calgary Ophthalmology Innovation Laboratory). The role of artificial intelligence in predicting optic neuritis subtypes from ocular fundus photographs. J Neuroophthalmol 2024; 44 (04) 462-468
- 51 Feng Y, Chow LS, Gowdh NM, Ramli N, Tan LK, Abdullah S. Classification of optic neuritis in neuromyelitis optica spectrum disorders (NMOSD) on MRI using CNN with transfer learning and manipulation of pre-processing on augmentation. Biomed Phys Eng Express 2024;10(5)
- 52 Lee DK, Choi YJ, Lee SJ, Kang HG, Park YR. Development of a deep learning model to distinguish the cause of optic disc atrophy using retinal fundus photography. Sci Rep 2024; 14 (01) 5079
- 53 Lin MY, Najjar RP, Tang Z. et al; BONSAI (Brain and Optic Nerve Study with Artificial Intelligence) group. The BONSAI (brain and optic nerve study with artificial intelligence) deep learning system can accurately identify pediatric papilledema on standard ocular fundus photographs. J AAPOS 2024; 28 (01) 103803
- 54 Ciftci Kavaklioglu B, Erdman L, Goldenberg A. et al. Machine learning classification of multiple sclerosis in children using optical coherence tomography. Mult Scler 2022; 28 (14) 2253-2262
- 55 Dong L, He W, Zhang R. et al. Artificial intelligence for screening of multiple retinal and optic nerve diseases. JAMA Netw Open 2022; 5 (05) e229960
- 56 Liu K, Liu S, Tan X. et al. Deep learning system for distinguishing optic neuritis from non-arteritic anterior ischemic optic neuropathy at acute phase based on fundus photographs. Front Med (Lausanne) 2023; 10: 1188542
- 57 Li A, Tandon AK, Sun G, Dinkin MJ, Oliveira C. Early detection of optic nerve changes on optical coherence tomography using deep learning for risk-stratification of papilledema and glaucoma. J Neuroophthalmol 2024; 44 (01) 47-52
- 58 Kenney RC, Liu M, Hasanaj L. et al. The role of optical coherence tomography criteria and machine learning in multiple sclerosis and optic neuritis diagnosis. Neurology 2022; 99 (11) e1100-e1112
- 59 Kenney RC, Flagiello TA, D' Cunha A. et al. Advancing optical coherence tomography diagnostic capabilities: machine learning approaches to detect autoimmune inflammatory diseases. J Neuroophthalmol 2025; 45 (04) 413-419
- 60 Gungor A, Sarbout I, Gilbert AL. et al. Artificial intelligence-based detection of central retinal artery occlusion within 4.5 hours on standard fundus photographs. J Am Heart Assoc 2025; 14 (13) e041441
- 61 Tran C, Shen K, Liu K. et al. Deep learning predicts prevalent and incident Parkinson's disease from UK Biobank fundus imaging. Sci Rep 2024; 14 (01) 3637
- 62 Tian J, Smith G, Guo H. et al. Modular machine learning for Alzheimer's disease classification from retinal vasculature. Sci Rep 2021; 11 (01) 238
- 63 Wisely CE, Wang D, Henao R. et al. Convolutional neural network to identify symptomatic Alzheimer's disease using multimodal retinal imaging. Br J Ophthalmol 2022; 106 (03) 388-395
- 64 Kenney RC, Requarth TW, Jack AI, Hyman SW, Galetta SL, Grossman SN. AI in neuro-ophthalmology: current practice and future opportunities. J Neuroophthalmol 2024; 44 (03) 308-318
- 65 Bermudez C, Lesnick TG, More SS. et al. Optical coherence tomography angiography retinal imaging associations with burden of small vessel disease and amyloid positivity in the brain. J Neuroophthalmol 2025; 45 (01) 63-70
- 66 Suh A, Hampel G, Vinjamuri A. et al. Oculomics analysis in multiple sclerosis: current ophthalmic clinical and imaging biomarkers. Eye (Lond) 2024; 38 (14) 2701-2710
- 67 Prem Senthil M, Kurban C, Thuy Nguyen N. et al. Role of noninvasive ocular imaging as a biomarker in peripheral artery disease (PAD): a systematic review. Vasc Med 2024; 29 (02) 215-222
- 68 Song JE, Lee EJ, Kim TW, Kim H. Multicolor imaging compared with red-free fundus photography in the detection of glaucomatous retinal nerve fiber layer thinning. Photodiagnosis Photodyn Ther 2023; 42: 103352
- 69 Mishra C, Tripathy K. Fundus Camera. [Updated 2023 Aug 25]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025
- 70 Woof W, de Guimarães TAC, Al-Khuzaei S. et al. Quantification of fundus autofluorescence features in a molecularly characterized cohort of more than 3500 inherited retinal disease patients from the United Kingdom. Ophthalmol Sci 2024; 5 (02) 100652
- 71 Bellini V, Cascella M, Cutugno F. et al. Understanding basic principles of artificial intelligence: a practical guide for intensivists. Acta Biomed 2022; 93 (05) e2022297
- 72 Costello F, Chen JJ. The role of optical coherence tomography in the diagnosis of afferent visual pathway problems: a neuroophthalmic perspective. Handb Clin Neurol 2021; 178: 97-113
- 73 Wicklein R, Kreitner L, Wild A. et al. Retinal small vessel pathology is associated with disease burden in multiple sclerosis. Mult Scler 2024; 30 (07) 812-819