Artificial Intelligence Meets Neuro-Ophthalmology

Leong, Yuan-Yuh; Vasseneix, Caroline; Finkelstein, Maxwell T.; Miléa, Dan; Najjar, Raymond P.

doi:10.1097/apo.0000000000000512

Cited by 19 publications

(8 citation statements)

References 93 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…7 Computer-aided diagnostic systems could help improve the detection of papilloedema and other ONH abnormalities. 15 In an early study, Akbar et al 16 developed a classification model aimed at automatically differentiating between papilloedema and normal ONHs on retinal images, based on structural features of the ONH (disc margin obscuration, disc colour and continuity of disc vessels) using support vector machine (SVM) for classification purposes. In the limited sample, the authors achieved good performance for differentiating papilloedema from normal ONH with an accuracy, sensitivity and specificity of 92.9%, 90.0%, and 96.4%, respectively.…”

Section: Detection Of Papilloedema and Other Optic Neuropathiesmentioning

confidence: 99%

Through the eyes into the brain, using artificial intelligence

Sathianvichitr

Lamoureux

Nakada

et al. 2023

Ann Acad Med Singap

Self Cite

View full text Add to dashboard Cite

Introduction: Detection of neurological conditions is of high importance in the current context of increasingly ageing populations. Imaging of the retina and the optic nerve head represents a unique opportunity to detect brain diseases, but requires specific human expertise. We review the current outcomes of artificial intelligence (AI) methods applied to retinal imaging for the detection of neurological and neuro-ophthalmic conditions. Method: Current and emerging concepts related to the detection of neurological conditions, using AI-based investigations of the retina in patients with brain disease were examined and summarised. Results: Papilloedema due to intracranial hypertension can be accurately identified with deep learning on standard retinal imaging at a human expert level. Emerging studies suggest that patients with Alzheimer’s disease can be discriminated from cognitively normal individuals, using AI applied to retinal images. Conclusion: Recent AI-based systems dedicated to scalable retinal imaging have opened new perspectives for the detection of brain conditions directly or indirectly affecting retinal structures. However, further validation and implementation studies are required to better understand their potential value in clinical practice. Keywords: Alzheimer’s disease, deep learning, dementia, optic neuropathy, papilloedema

show abstract

Section: Detection Of Papilloedema and Other Optic Neuropathiesmentioning

confidence: 99%

Through the eyes into the brain, using artificial intelligence

Sathianvichitr

Lamoureux

Nakada

et al. 2023

Ann Acad Med Singap

Self Cite

View full text Add to dashboard Cite

show abstract

“…Liu et al [63] developed a CAD system that uses facial images and video of OMG patients' extraocular movements and eyelid positions taken during surgery. To aid in OMG diagnosis, the neostigmine test, and image segmentation software called OMG-net was developed by the team and MobileNet served as the backbone of the encoder-decoder network.…”

Section: At Ocular Myasthenia Gravismentioning

confidence: 99%

Current roles of artificial intelligence in ophthalmology

Keskinbora

2023

Exploration of Medicine

View full text Add to dashboard Cite

Artificial intelligence (AI) studies are increasingly reporting successful results in the diagnosis and prognosis prediction of ophthalmological diseases as well as systemic disorders. The goal of this review is to detail how AI can be utilized in making diagnostic predictions to enhance the clinical setting. It is crucial to keep improving methods that emphasize clarity in AI models. This makes it possible to evaluate the information obtained from ocular imaging and easily incorporate it into therapeutic decision-making procedures. This will contribute to the wider acceptance and adoption of AI-based ocular imaging in healthcare settings combining advanced machine learning and deep learning techniques with new developments. Multiple studies were reviewed and evaluated, including AI-based algorithms, retinal images, fundus and optic nerve head (ONH) photographs, and extensive expert reviews. In these studies, carried out in various countries and laboratories of the world, it is seen those complex diagnoses, which can be detected systemic diseases from ophthalmological images, can be made much faster and with higher predictability, accuracy, sensitivity, and specificity, in addition to ophthalmological diseases, by comparing large numbers of images and teaching them to the computer. It is now clear that it can be taken advantage of AI to achieve diagnostic certainty. Collaboration between the fields of medicine and engineering foresees promising advances in improving the predictive accuracy and precision of future medical diagnoses achieved by training machines with this information. However, it is important to keep in mind that each new development requires new additions or updates to various social, psychological, ethical, and legal regulations.

show abstract

“…In order to address this question, we aimed to develop, train and test a deep learning system (DLS) able to automatically classify the quality of ONH fundus photographs in neuro-ophthalmic and neurological conditions, based on data from a large, international, multi-ethnic population, using multiple cameras. A DL-driven algorithm for the quality assessment of ONH images could reduce the frequency of diagnostically unusable datasets, especially in neuro-ophthalmology where data are scarce [25,26].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning System for Automated Quality Evaluation of Optic Disc Photographs in Neuro-Ophthalmic Disorders

et al. 2023

Self Cite

View full text Add to dashboard Cite

The quality of ocular fundus photographs can affect the accuracy of the morphologic assessment of the optic nerve head (ONH), either by humans or by deep learning systems (DLS). In order to automatically identify ONH photographs of optimal quality, we have developed, trained, and tested a DLS, using an international, multicentre, multi-ethnic dataset of 5015 ocular fundus photographs from 31 centres in 20 countries participating to the Brain and Optic Nerve Study with Artificial Intelligence (BONSAI). The reference standard in image quality was established by three experts who independently classified photographs as of “good”, “borderline”, or “poor” quality. The DLS was trained on 4208 fundus photographs and tested on an independent external dataset of 807 photographs, using a multi-class model, evaluated with a one-vs-rest classification strategy. In the external-testing dataset, the DLS could identify with excellent performance “good” quality photographs (AUC = 0.93 (95% CI, 0.91–0.95), accuracy = 91.4% (95% CI, 90.0–92.9%), sensitivity = 93.8% (95% CI, 92.5–95.2%), specificity = 75.9% (95% CI, 69.7–82.1%) and “poor” quality photographs (AUC = 1.00 (95% CI, 0.99–1.00), accuracy = 99.1% (95% CI, 98.6–99.6%), sensitivity = 81.5% (95% CI, 70.6–93.8%), specificity = 99.7% (95% CI, 99.6–100.0%). “Borderline” quality images were also accurately classified (AUC = 0.90 (95% CI, 0.88–0.93), accuracy = 90.6% (95% CI, 89.1–92.2%), sensitivity = 65.4% (95% CI, 56.6–72.9%), specificity = 93.4% (95% CI, 92.1–94.8%). The overall accuracy to distinguish among the three classes was 90.6% (95% CI, 89.1–92.1%), suggesting that this DLS could select optimal quality fundus photographs in patients with neuro-ophthalmic and neurological disorders affecting the ONH.

show abstract

Artificial Intelligence Meets Neuro-Ophthalmology

Cited by 19 publications

References 93 publications

Through the eyes into the brain, using artificial intelligence

Through the eyes into the brain, using artificial intelligence

Current roles of artificial intelligence in ophthalmology

A Deep Learning System for Automated Quality Evaluation of Optic Disc Photographs in Neuro-Ophthalmic Disorders

Contact Info

Product

Resources

About