AI-aided cardiovascular disease diagnosis in cattle from retinal images: Machine learning vs. deep learning models

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 226, P. 109391 - 109391

Published: Aug. 28, 2024

Language: Английский

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Artificial intelligence in stroke risk assessment and management via retinal imaging

Frontiers in Computational Neuroscience, Journal Year: 2025, Volume and Issue: 19

Published: Feb. 17, 2025

Retinal imaging, used for assessing stroke-related retinal changes, is a non-invasive and cost-effective method that can be enhanced by machine learning deep algorithms, showing promise in early disease detection, severity grading, prognostic evaluation stroke patients. This review explores the role of artificial intelligence (AI) patient care, focusing on imaging integration into clinical workflows. has revealed several microvascular including decrease central artery diameter an increase vein diameter, both which are associated with lacunar intracranial hemorrhage. Additionally, such as arteriovenous nicking, increased vessel tortuosity, arteriolar light reflex, decreased fractals, thinning nerve fiber layer also reported to higher risk. AI models, Xception EfficientNet, have demonstrated accuracy comparable traditional risk scoring systems predicting For diagnosis, models like Inception, ResNet, VGG, alongside classifiers, shown high efficacy distinguishing patients from healthy individuals using imaging. Moreover, random forest model effectively distinguished between ischemic hemorrhagic subtypes based features, superior predictive performance compared characteristics. support vector achieved classification pial collateral status. Despite this advancements, challenges lack standardized protocols modalities, hesitance trusting AI-generated predictions, insufficient data electronic health records, need validation across diverse populations, ethical regulatory concerns persist. Future efforts must focus validating ensuring algorithm transparency, addressing issues enable broader implementation. Overcoming these barriers will essential translating technology personalized care improving outcomes.

Language: Английский

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Current and future roles of artificial intelligence in retinopathy of prematurity

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(6)

Published: March 29, 2025

Abstract Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, detachment, and potential blindness. While semi-automated systems have been used in the past diagnose ROP-related plus disease by quantifying features, traditional machine learning (ML) models face challenges like accuracy overfitting. Recent advancements deep (DL), especially convolutional neural networks (CNNs), significantly improved ROP detection classification. The i-ROP (i-ROP-DL) system also shows promise detecting disease, offering reliable diagnosis potential. This research comprehensively examines contemporary progress associated with using imaging artificial intelligence (AI) detect ROP, valuable insights that can guide further investigation this domain. Based on 84 original studies field (out 2025 were reviewed), we concluded methods for suffer from subjectivity manual analysis, inconsistent clinical decisions. AI holds great improving management. review explores AI’s detection, classification, diagnosis, prognosis.

Language: Английский

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Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging

Journal of Diabetes & Metabolic Disorders, Journal Year: 2025, Volume and Issue: 24(1)

Published: April 12, 2025

Language: Английский

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