Heart, Eye, and Artificial Intelligence: A Review

Gupta, Kush; Reddy, Sarath

doi:10.14740/cr1179

Cited by 9 publications

(10 citation statements)

References 48 publications

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“…AF is naturally linked to the eye through the vascular system. Some cardiovascular diseases have been found to be related to specific features of the retinal structure and microvessels, suggesting that microvessels and macrovessels are mutually affected in an interwoven manner in heart diseases ( 18 ). Consequently, it can be speculated that AF and IS may be presented with different pathological microscopic changes in the fundus.…”

Section: Discussionmentioning

confidence: 99%

“…It has also been reported that some ocular symptoms of neuropsychiatric diseases occur up to 5 years before classical symptoms, and retinal pathological manifestations may precede symptoms of the brain ( 29 ). For instance, fundus vascular changes were reported several years before the onset of IS in chronic central diseases, while microvasculature and macrovasculature are affected in an intertwined manner ( 18 ). Therefore, it is assumed that the patients with IS have had microscopic pathological manifestations at least 1 year before stroke onset; however, these pathological symptoms cannot be observed by the naked eye, especially in the fundus blood vessels.…”

Section: Methodsmentioning

confidence: 99%

“…Technological advancements have led to the non-invasive visualization of blood vessels and imaging of the fundus ( 14 ). However, there is a rich collection of capillaries in the fundus ( 13 ), and microvasculature and macrovasculature are mutually affected in an intertwined manner ( 18 ). Machine learning, especially deep learning, can help capture the subtle differences in image information to identify abnormalities.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting ischemic stroke risk from atrial fibrillation based on multi-spectral fundus images using deep learning

Li,

Gao,

Song

et al. 2023

Front. Cardiovasc. Med.

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BackgroundIschemic stroke (IS) is one of the most common serious secondary diseases of atrial fibrillation (AF) within 1 year after its occurrence, both of which have manifestations of ischemia and hypoxia of the small vessels in the early phase of the condition. The fundus is a collection of capillaries, while the retina responds differently to light of different wavelengths. Predicting the risk of IS occurring secondary to AF, based on subtle differences in fundus images of different wavelengths, is yet to be explored. This study was conducted to predict the risk of IS occurring secondary to AF based on multi-spectrum fundus images using deep learning.MethodsA total of 150 AF participants without suffering from IS within 1 year after discharge and 100 IS participants with persistent arrhythmia symptoms or a history of AF diagnosis in the last year (defined as patients who would develop IS within 1 year after AF, based on fundus pathological manifestations generally prior to symptoms of the brain) were recruited. Fundus images at 548, 605, and 810 nm wavelengths were collected. Three classical deep neural network (DNN) models (Inception V3, ResNet50, SE50) were trained. Sociodemographic and selected routine clinical data were obtained.ResultsThe accuracy of all DNNs with the single-spectral or multi-spectral combination images at the three wavelengths as input reached above 78%. The IS detection performance of DNNs with 605 nm spectral images as input was relatively more stable than with the other wavelengths. The multi-spectral combination models acquired a higher area under the curve (AUC) scores than the single-spectral models.ConclusionsThe probability of IS secondary to AF could be predicted based on multi-spectrum fundus images using deep learning, and combinations of multi-spectrum images improved the performance of DNNs. Acquiring different spectral fundus images is advantageous for the early prevention of cardiovascular and cerebrovascular diseases. The method in this study is a beneficial preliminary and initiative exploration for diseases that are difficult to predict the onset time such as IS.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting ischemic stroke risk from atrial fibrillation based on multi-spectral fundus images using deep learning

Li,

Gao,

Song

et al. 2023

Front. Cardiovasc. Med.

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“…AI analysis based on fundus and retinal photographs can also be used to predict 48 cardiovascular and cerebrovascular diseases. Rim et al proposed a novel cardiovascular risk stratification system based on DL, their model called RetiCAC used 216,152 retinal photographs from five datasets as input and outperformed all single clinical parameter models in predicting coronary artery calcium, which is a marker of cardiovascular disease risk, and is also comparable to CT scan‐measured CAC.…”

Section: In Precision Medicinementioning

confidence: 99%

“…And based on data such as corneal topography, anterior segment OCT, and 3D images of the Pentacam anterior segment, several research teams[44][45][46][47] have successively developed multiple automatic grading methods for keratoconus using CNNs, with an accuracy of 99.3%. AI analysis based on fundus and retinal photographs can also be used to predict48 cardiovascular and cerebrovascular diseases. Rim et al proposed a novel cardiovascular risk stratification system based on DL, their model called RetiCAC used 216,152 retinal photographs from five datasets as input and outperformed all single clinical parameter models in predicting coronary artery calcium, which is a marker of cardiovascular disease risk, and is also comparable to CT scan-measured CAC.…”

mentioning

confidence: 99%

Deep learning in precision medicine and focus on glioma

Liu

2023

Bioengineering & Transla Med

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Deep learning (DL) has been successfully applied to different fields for a range of tasks. In medicine, DL methods have been also used to improve the efficiency of disease diagnosis. In this review, we first summarize the history of the development of artificial intelligence models, demonstrate the features of the subtypes of machine learning and different DL networks, and then explore their application in the different fields of precision medicine, such as cardiology, gastroenterology, ophthalmology, dermatology, and oncology. By digging more information and extracting multilevel features from medical data, we found that DL helps doctors assess diseases automatically and monitor patients' physical health. In gliomas, research regarding application prospect of DL was mainly shown through magnetic resonance imaging and then by pathological slides. However, multi‐omics data, such as whole exome sequence, RNA sequence, proteomics, and epigenomics, have not been covered thus far. In general, the quality and quantity of DL datasets still need further improvements, and more fruitful multi‐omics characteristics will bring more comprehensive and accurate diagnosis in precision medicine and glioma.

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Artificial intelligence in assessing cardiovascular diseases and risk factors via retinal fundus images: A review of the last decade

Abdollahi,

Jafarizadeh,

Ghafouri‐Asbagh

et al. 2024

WIREs Data Min & Knowl

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Cardiovascular diseases (CVDs) are the leading cause of death globally. The use of artificial intelligence (AI) methods—in particular, deep learning (DL)—has been on the rise lately for the analysis of different CVD‐related topics. The use of fundus images and optical coherence tomography angiography (OCTA) in the diagnosis of retinal diseases has also been extensively studied. To better understand heart function and anticipate changes based on microvascular characteristics and function, researchers are currently exploring the integration of AI with noninvasive retinal scanning. There is great potential to reduce the number of cardiovascular events and the financial strain on healthcare systems by utilizing AI‐assisted early detection and prediction of cardiovascular diseases on a large scale. A comprehensive search was conducted across various databases, including PubMed, Medline, Google Scholar, Scopus, Web of Sciences, IEEE Xplore, and ACM Digital Library, using specific keywords related to cardiovascular diseases and AI. The study included 87 English‐language publications selected for relevance, and additional references were considered. This article provides an overview of the recent developments and difficulties in using AI and retinal imaging to diagnose cardiovascular diseases. It provides insights for further exploration in this field. Researchers are trying to develop precise disease prognosis patterns in response to the aging population and the growing global burden of CVD. AI and DL are revolutionizing healthcare by potentially diagnosing multiple CVDs from a single retinal image. However, swifter adoption of these technologies in healthcare systems is required.This article is categorized under: Application Areas > Health Care Technologies > Artificial Intelligence

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Heart, Eye, and Artificial Intelligence: A Review

Cited by 9 publications

References 48 publications

Predicting ischemic stroke risk from atrial fibrillation based on multi-spectral fundus images using deep learning

Predicting ischemic stroke risk from atrial fibrillation based on multi-spectral fundus images using deep learning

Deep learning in precision medicine and focus on glioma

Artificial intelligence in assessing cardiovascular diseases and risk factors via retinal fundus images: A review of the last decade

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