ReviewDiagnostics (Basel, Switzerland)2022
An Overview of Deep-Learning-Based Methods for Cardiovascular Risk Assessment with Retinal Images.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review.Journal of advanced research · 2025Pooled it
- Artificial Intelligence in the Heart of Medicine: A Systematic Approach to Transforming Arrhythmia Care with Intelligent Systems.Current cardiology reviews · 2025Pooled it
- Enhanced gastrointestinal disease classification using a convvit hybrid model on endoscopic images.Physical and engineering sciences in medicine · 2025Article
- Deep learning-based multimodal risk stratification for atherosclerosis management.Archives of medical science : AMS · 2025Article
- Prediction of cardiovascular markers and diseases using retinal fundus images and deep learning: a systematic scoping review.European heart journal. Digital health · 2024Article
- Association of subretinal drusenoid deposits and cardiovascular disease.Scientific reports · 2024Article
- Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review.Ophthalmology and therapy · 2024Review
- Oculomics: A Crusade Against the Four Horsemen of Chronic Disease.Ophthalmology and therapy · 2024Review
- Machine-learning-based models to predict cardiovascular risk using oculomics and clinic variables in KNHANES.BioData mining · 2024Article
- The Application of Deep Learning for the Segmentation and Classification of Coronary Arteries.Diagnostics (Basel, Switzerland) · 2023Article
- Applications of artificial intelligence-assisted retinal imaging in systemic diseases: A literature review.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Cardiovascular diseases (CVDs) are one of the most prevalent causes of premature death. Early detection is crucial to prevent and address CVDs in a timely manner. Recent advances in oculomics show that retina fundus imaging (RFI) can carry relevant information for the early diagnosis of several systemic diseases. There is a large corpus of RFI systematically acquired for diagnosing eye-related diseases that could be used for CVDs prevention. Nevertheless, public health systems cannot afford to dedicate expert physicians to only deal with this data, posing the need for automated diagnosis tools that can raise alarms for patients at risk. Artificial Intelligence (AI) and, particularly, deep learning models, became a strong alternative to provide computerized pre-diagnosis for patient risk retrieval. This paper provides a novel review of the major achievements of the recent state-of-the-art DL approaches to automated CVDs diagnosis. This overview gathers commonly used datasets, pre-processing techniques, evaluation metrics and deep learning approaches used in 30 different studies. Based on the reviewed articles, this work proposes a classification taxonomy depending on the prediction target and summarizes future research challenges that have to be tackled to progress in this line.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.