Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Scoring System-Based Approach for Positive Intracoronary Acetylcholine Provocation Tests: The Original and Modified ABCD Scores

BACKGROUND

Although intracoronary acetylcholine (ACh) provocation testing is a guideline-recommended invasive standard for the diagnosis of vasospastic angina (VSA), ACh tests are largely underused in clinical practice globally. Recently, the ABCD score, consisting of clinical presentation, myocardial bridge, C-reactive protein, and dyslipidemia, was developed to predict positive ACh test results.

OBJECTIVES

The authors aimed to externally validate the diagnostic ability of the score and attempted to improve the predictivity for identifying patients with VSA.

METHODS

From May 2012 to September 2023, a total of 723 patients undergoing ACh provocation tests for diagnosing VSA were included. The original ABCD score was calculated according to the predefined criteria, and the modified ABCD score was internally developed to improve the diagnostic accuracy. The positive ACh provocation test (ie, VSA) was defined as a significant angiographic vasospasm accompanied by chest pain and/or ischemic electrocardiographic changes.

RESULTS

Of the 723 patients, 383 (53.0%) had positive ACh provocation test results. The receiver-operating characteristics curve analysis indicated that the original ABCD score was significantly predictive of positive ACh tests. Using best cutoff values on receiver-operating characteristics curve analyses, we developed the modified ABCD score, which was simpler than the original score. The modified rather than original ABCD score had better diagnostic ability for positive ACh test results (area under the curve 0.65 vs 0.55; P < 0.001).

CONCLUSIONS

The original ABCD score was predictive of VSA in this external validation study with modest diagnostic accuracy, while the modified ABCD score achieved better predictivity for identifying patients with VSA.

A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions

This review provides a thorough and organized overview of machine learning (ML) applications in predicting heart disease, covering technological advancements, challenges, and future prospects. As cardiovascular diseases (CVDs) are the leading cause of global mortality, there is an urgent demand for early and precise diagnostic tools. ML models hold considerable potential by utilizing large-scale healthcare data to enhance predictive diagnostics. To systematically investigate this field, the literature is organized into five thematic categories such as "Heart Disease Detection and Diagnostics," "Machine Learning Models and Algorithms for Healthcare," "Feature Engineering and Optimization Techniques," "Emerging Technologies in Healthcare," and "Applications of AI Across Diseases and Conditions." The review incorporates performance benchmarking of various ML models, highlighting that hybrid deep learning (DL) frameworks, e.g., convolutional neural network-long short-term memory (CNN-LSTM) consistently outperform traditional models in terms of sensitivity, specificity, and area under the curve (AUC). Several real-world case studies are presented to demonstrate the successful deployment of ML models in clinical and wearable settings. This review showcases the progression of ML approaches from traditional classifiers to hybrid DL structures and federated learning (FL) frameworks. It also discusses ethical issues, dataset limitations, and model transparency. The conclusions provide important insights for the development of artificial intelligence (AI) powered, clinically applicable heart disease prediction systems.

Artificial intelligence in clinical genetics: current practice and attitudes among the clinical genetics workforce

Purpose

Artificial intelligence (AI) applications for clinical genetics hold the potential to improve patient care through supporting diagnostics and management as well as automating administrative tasks, thus enhancing and potentially enabling clinician/patient interactions. While the introduction of AI into clinical genetics is increasing, there remain unclear questions about risks and benefits, and the readiness of the workforce.

Methods

To assess the current clinical genetics workforce's use, knowledge, and attitudes toward available medical AI applications, we conducted a survey involving 215 US-based genetics clinicians and trainees.

Results

Over half (51.2%) of participants report little to no knowledge of AI in clinical genetics and 64.3% reported no formal training in AI applications. Formal training directly correlated with self-reported knowledge of AI in clinical genetics, with 69.3% of respondents with formal training reporting intermediate to extensive knowledge of AI vs. 37.5% without formal training. Most participants reported that they lacked sufficient knowledge of clinical AI (83.4%) and agreed that there should be more education in this area (97.6%) and would take a course if offered (89.3%). The majority (51.6%) of clinician participants said they never used AI applications in the clinic. However, after a tutorial describing clinical AI applications, 75.8% reported some use of AI applications in the clinic. When asked specifically about clinical AI application usage, the majority of clinician participants used facial diagnostic applications (54.9%) and AI-generated genomic testing results (62.1%), whereas other applications such as chatbots, large language models (LLMs), pedigree or medical summary generators, and risk assessment were only used by a fraction of the clinicians, ranging from 11.1 to 12.5%. Nearly all participants (94.6%) reported clinical genetics professionals as being overburdened.

Conclusion

Further clinician education is both desired and needed to optimally utilize clinical AI applications with the potential to enhance patient care and alleviate the current strain on genetics clinics.

Emerging Therapeutic Strategies for Heart Failure: A Comprehensive Review of Novel Pharmacological and Molecular Targets

Heart failure (HF) is a complex clinical syndrome characterized by the heart's inability to meet the body's metabolic demands. HF remains a global health challenge with high morbidity and mortality. Outcomes of beta-blockers, angiotensin receptor-neprilysin inhibitors (ARNIs), and mineralocorticoid receptor antagonists (MRAs) in HF remain suboptimal. HF is a heterogeneous syndrome driven by neurohormonal dysregulation, fibrosis, metabolic disturbances, and inflammation, contributing to symptoms like dyspnea, fatigue, and fluid retention. Recent advances in pharmacological therapies, including sodium-glucose cotransporter 2 inhibitors (SGLT2 inhibitors), soluble guanylate cyclase stimulators (sGC stimulators), and cardiac myosin activators, have shown promise in HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), offering mechanism-specific interventions. Moreover, molecular-targeted therapies, such as clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (Cas9) gene editing, RNA-based therapeutics, and adeno-associated virus serotype 9-sarcoplasmic reticulum calcium ATPase 2a (AAV9-SERCA2a gene) therapy, are emerging as potential disease-modifying treatments aimed at addressing genetic and inflammatory drivers of cardiomyopathies. Artificial intelligence (AI) is transforming HF care by enhancing predictive modelling, risk stratification, and precision medicine, with applications in multi-omics data integration. AI-driven tools, including machine learning (ML) algorithms, improve echocardiographic phenotyping, optimize treatment strategies, and refine patient selection for therapies. Despite these promising developments, challenges such as data quality, standardization, scalability, and regulatory barriers remain. Furthermore, gene therapies' long-term safety and efficacy are still uncertain, with concerns about immune responses, off-target effects, and sustained gene expression. Regenerative medicine strategies, including induced pluripotent stem cells (iPSC)-derived cardiomyocytes, extracellular vesicles (EVs), and 3D-bioprinted cardiac patches, offer potential solutions for myocardial repair. However, immune rejection, graft integration, and long-term viability remain significant obstacles. Additionally, high costs associated with novel biologics and gene-based therapies limit accessibility, particularly in low-resource settings. The future of HF management depends on overcoming these translational challenges. Key steps include validating AI-driven phenotyping tools in clinical trials, advancing scalable biomanufacturing technologies, and refining regulatory frameworks to facilitate clinical integration. By addressing these barriers, precision medicine, AI, and regenerative therapies can transform HF management, providing more personalized, effective, and accessible treatments and ultimately improving patient outcomes globally.

Artificial intelligence in cardiovascular procedures: a bibliometric and visual analysis study

Background

The integration of artificial intelligence (AI) into cardiovascular procedures has significantly advanced diagnostic accuracy, outcome prediction, and robotic-assisted surgeries. However, a comprehensive bibliometric analysis of AI's impact in this field is lacking. This study examines research trends, key contributors, and emerging themes in AI-driven cardiovascular interventions.

Methods

We retrieved relevant publications from the Web of Science Core Collection and analyzed them using VOSviewer, CiteSpace, and Biblioshiny to map research trends and collaborations.

Results

AI-related cardiovascular research has grown substantially from 1993 to 2024, with a sharp increase from 2020 to 2023, peaking at 93 publications in 2023. The USA (127 papers), China (79), and England (31) were the top contributors, with Harvard University leading institutional output (17 papers). Frontiers in Cardiovascular Medicine was the most prolific journal. Core research themes included "machine learning," "mortality," and "cardiac surgery," with emerging trends in "association," "implantation," and "aortic stenosis," underscoring AI's expanding role in predictive modeling and surgical outcomes.

Conclusion

AI demonstrates transformative potential in cardiovascular procedures, particularly in diagnostic imaging, predictive modeling, and patient management. This bibliometric analysis highlights the growing interest in AI applications and provides a framework for integrating AI into clinical workflows to enhance diagnostic accuracy, treatment strategies, and patient outcomes.

Chronic Inflammatory Diseases and Cardiovascular Risk: Current Insights and Future Strategies for Optimal Management

Chronic inflammation is a pivotal driver in the progression of atherosclerosis, significantly contributing to the burden of cardiovascular disease (CVD). Patients with chronic inflammatory diseases, such as inflammatory bowel diseases (IBDs) (e.g., ulcerative colitis and Crohn's disease), rheumatological disorders, as well as individuals with auto-immune diseases (such as systemic lupus erythematosus), present a higher risk of major adverse cardiac events (MACEs). Despite their elevated CVD risk, these populations remain underrepresented in cardiovascular research, leading to a critical underestimation of their cardiovascular risk (CVR) in clinical practice. Furthermore, even recent CVR scores poorly predict the risk of events in these specific populations. This narrative review examines the physiopathological mechanisms linking chronic inflammation, immunomodulation, atherosclerosis, thrombosis and cardiovascular events. We review data from epidemiological studies and clinical trials to explore the potential cardiovascular benefits of anti-inflammatory and immunomodulatory therapies. Despite existing evidence, significant gaps in knowledge remain. Future research is mandatory, focusing on innovative strategies for risk stratification and optimization, including lipidomics, proteomics, advanced inflammatory markers, microbiota profiling, and cardiovascular imaging. Addressing these unmet needs will enhance understanding of cardiovascular risk in chronic inflammatory diseases, enabling tailored interventions and better outcomes.

Transformer and Attention-Based Architectures for Segmentation of Coronary Arterial Walls in Intravascular Ultrasound: A Narrative Review

Background: The leading global cause of death is coronary artery disease (CAD), necessitating early and precise diagnosis. Intravascular ultrasound (IVUS) is a sophisticated imaging technique that provides detailed visualization of coronary arteries. However, the methods for segmenting walls in the IVUS scan into internal wall structures and quantifying plaque are still evolving. This study explores the use of transformers and attention-based models to improve diagnostic accuracy for wall segmentation in IVUS scans. Thus, the objective is to explore the application of transformer models for wall segmentation in IVUS scans to assess their inherent biases in artificial intelligence systems for improving diagnostic accuracy. Methods: By employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we pinpointed and examined the top strategies for coronary wall segmentation using transformer-based techniques, assessing their traits, scientific soundness, and clinical relevancy. Coronary artery wall thickness is determined by using the boundaries (inner: lumen-intima and outer: media-adventitia) through cross-sectional IVUS scans. Additionally, it is the first to investigate biases in deep learning (DL) systems that are associated with IVUS scan wall segmentation. Finally, the study incorporates explainable AI (XAI) concepts into the DL structure for IVUS scan wall segmentation. Findings: Because of its capacity to automatically extract features at numerous scales in encoders, rebuild segmented pictures via decoders, and fuse variations through skip connections, the UNet and transformer-based model stands out as an efficient technique for segmenting coronary walls in IVUS scans. Conclusions: The investigation underscores a deficiency in incentives for embracing XAI and pruned AI (PAI) models, with no UNet systems attaining a bias-free configuration. Shifting from theoretical study to practical usage is crucial to bolstering clinical evaluation and deployment.

Cross-sectional study on smoking types and stroke risk: development of a predictive model for identifying stroke risk

Background

Stroke, a major global health concern, is responsible for high mortality and long-term disabilities. With the aging population and increasing prevalence of risk factors, its incidence is on the rise. Existing risk assessment tools have limitations, and there is a pressing need for more accurate and personalized stroke risk prediction models. Smoking, a significant modifiable risk factor, has not been comprehensively examined in current models regarding different smoking types.

Methods

Data were sourced from the 2015-2018 National Health and Nutrition Examination Survey (NHANES) and the 2020-2021 Behavioral Risk Factor Surveillance System (BRFSS). Tobacco use (including combustible cigarettes and e-cigarettes) and stroke history were obtained through questionnaires. Participants were divided into four subgroups: non-smokers, exclusive combustible cigarette users, exclusive e-cigarette users, and dual users. Covariates such as age, sex, race, education, and health conditions were also collected. Multivariate logistic regression was used to analyze the relationship between smoking and stroke. Four machine-learning models (XGBoost, logistic regression, Random Forest, and Gaussian Naive Bayes) were evaluated using the area under the receiver-operating characteristic curve (AUC), and Shapley's additive interpretation method was applied for feature importance ranking and model interpretation.

Results

A total of 273,028 individuals were included in the study. Exclusive combustible cigarette users had an elevated stroke risk (β: 1.36, 95% CI: 1.26-1.47, P < 0.0001). Among the four machine-learning models, the XGBoost model showed the best discriminative ability with an AUC of 0.794 (95% CI = 0.787-0.802).

Conclusion

This study reveals a significant association between smoking types and stroke risk. An XGBoost-based stroke prediction model was established, which has the potential to improve the accuracy of stroke risk assessment and contribute to personalized interventions for stroke prevention, thus alleviating the healthcare burden related to stroke.

Analysis of Personalized Cardiovascular Drug Therapy: From Monitoring Technologies to Data Integration and Future Perspectives

Cardiovascular diseases have long been a major challenge to human health, and the treatment differences caused by individual variability remain unresolved. In recent years, personalized cardiovascular drug therapy has attracted widespread attention. This paper reviews the strategies for achieving personalized cardiovascular drug therapy through traditional dynamic monitoring and multidimensional data integration and analysis. It focuses on key technologies for dynamic monitoring, dynamic monitoring based on individual differences, and multidimensional data integration and analysis. By systematically reviewing the relevant literature, the main challenges in current research and the proposed potential directions for future studies were summarized.

Can artificial intelligence lower the global sudden cardiac death rate? A narrative review

PURPOSE OF REVIEW

WHO defines SCD as sudden unexpected death either within 1 h of symptom onset (witnessed) or within 24 h of having been observed alive and symptom-free (unwitnessed). Sudden cardiac arrest is a major cause of mortality worldwide, with survival to hospital discharge for hospital cardiac arrest and in-hospital cardiac arrest being only 9.3 % and 21.2 %, respectively, despite treatment highlighting the importance of effectively predicting and preventing cardiac arrest. This literature review aims to explore the role and application of AI (Artificial Intelligence) in predicting and preventing sudden cardiac arrest.

MATERIAL AND METHODS

Eligible studies were searched from PubMed and Web of Science. The inclusion criteria were fulfilled if sudden cardiac death prediction and prevention, artificial intelligence, machine learning, and deep learning were included.

CONCLUSIONS

Artificial intelligence, machine learning, and deep learning have shown remarkable prospects in SCA risk stratification, which can improve the survival rate from SCA. Nonetheless, they have not been adequately trained and tested, necessitating further studies with explainable techniques, larger sample sizes, external validation, more diverse patient samples, multimodal tools, ethics, and bias mitigation to unlock their full potential.

The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease

The application of artificial intelligence (AI) and machine learning (ML) in medicine and healthcare has been extensively explored across various areas. AI and ML can revolutionize cardiovascular disease management by significantly enhancing diagnostic accuracy, disease prediction, workflow optimization, and resource utilization. This review summarizes current advancements in AI and ML concerning cardiovascular disease, including their clinical investigation and use in primary cardiac imaging techniques, common cardiovascular disease categories, clinical research, patient care, and outcome prediction. We analyze and discuss commonly used AI and ML models, algorithms, and methodologies, highlighting their roles in improving clinical outcomes while addressing current limitations and future clinical applications. Furthermore, this review emphasizes the transformative potential of AI and ML in cardiovascular practice by improving clinical decision making, reducing human error, enhancing patient monitoring and support, and creating more efficient healthcare workflows for complex cardiovascular conditions.

Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement longitudinal study (CHARLS)

BACKGROUND

Due to the ageing population and evolving lifestyles occurring in China, middle-aged and elderly populations have become high-risk groups for cardiovascular disease (CVD). The aim of this study was to analyse the incidence characteristics of CVD in these populations and develop a prediction model by using data from the China Health and Retirement Longitudinal Study (CHARLS).

METHODS

We used follow-up data from the CHARLS to analyse CVD incidence in the Chinese middle-aged and elderly population over a time span of 9 years. Five machine learning (ML) algorithms were employed for risk prediction. Data preprocessing included missing value imputation via random forest. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (Lasso CV) method with cross-validation prior to model training. The application of the synthetic minority over-sampling technique (SMOTE) to address class imbalance. Model performance was evaluated via analyses including the area under the ROC curve (AUC), precision, recall, F1 score, and SHAP plots for interpretability.

RESULTS

In accordance with the exclusion criteria, 12,580, 12,061, 11,545, and 11,619 participants were enrolled in four follow-up rounds. The cumulative incidence (CI) of CVD at 2, 4, 7, and 9 years was 2.846%, 8.971%, 17.869% and 20.518%,, respectively. Significant differences in CVD incidence were observed across gender, age, ethnicity, and region, with higher rates observed in females and in the northeast region. Ultimately, 8,080 participants and 24 features were analysed for CVD risk prediction. Five ML models were built based on these features. Although the LGB model achieves an AUC of 0.818, indicating strong overall performance, its F1 score and recall rate are relatively low, at 0.509 and 43.1%, respectively. Shapley additive explanations (SHAP) analyses revealed the importance of key features, such as night sleep duration, TG levels, and waist circumference, in predicting outcomes, and highlighted the nonlinear relationships between these features and CVD risk.

CONCLUSIONS

Gender, age, ethnicity, and region are significant factors influencing CVD incidence. Although the LGB model demonstrates good overall performance, its low F1 score and recall rate reveal limitations in identifying high-risk cardiovascular disease patients.

Machine learning-based myocardial infarction bibliometric analysis

Purpose

This study analyzed the research trends in machine learning (ML) pertaining to myocardial infarction (MI) from 2008 to 2024, aiming to identify emerging trends and hotspots in the field, providing insights into the future directions of research and development in ML for MI. Additionally, it compared the contributions of various countries, authors, and agencies to the field of ML research focused on MI.

Method

A total of 1,036 publications were collected from the Web of Science Core Collection database. CiteSpace 6.3.R1, Bibliometrix, and VOSviewer were utilized to analyze bibliometric characteristics, determining the number of publications, countries, institutions, authors, keywords, and cited authors, documents, and journals in popular scientific fields. CiteSpace was used for temporal trend analysis, Bibliometrix for quantitative country and institutional analysis, and VOSviewer for visualization of collaboration networks.

Results

Since the emergence of research literature on medical imaging and machine learning (ML) in 2008, interest in this field has grown rapidly, particularly since the pivotal moment in 2016. The ML and MI domains, represented by China and the United States, have experienced swift development in research after 2015, albeit with the United States significantly outperforming China in research quality (as evidenced by the higher impact factors of journals and citation counts of publications from the United States). Institutional collaborations have formed, notably between Harvard Medical School in the United States and Capital Medical University in China, highlighting the need for enhanced cooperation among domestic and international institutions. In the realm of MI and ML research, cooperative teams led by figures such as Dey, Damini, and Berman, Daniel S. in the United States have emerged, indicating that Chinese scholars should strengthen their collaborations and focus on both qualitative and quantitative development. The overall direction of MI and ML research trends toward Medicine, Medical Sciences, Molecular Biology, and Genetics. In particular, publications in "Circulation" and "Computers in Biology and Medicine" from the United States hold prominent positions in this study.

Conclusion

This paper presents a comprehensive exploration of the research hotspots, trends, and future directions in the field of MI and ML over the past two decades. The analysis reveals that deep learning is an emerging research direction in MI, with neural networks playing a crucial role in early diagnosis, risk assessment, and rehabilitation therapy.

Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes

INTRODUCTION

Artificial intelligence (AI) is changing the field of nanomedicine by exploring novel nanomaterials for developing therapies of high efficacy. AI works on larger datasets, finding sought-after nano-properties for different therapeutic aims and eventually enhancing nanomaterials' safety and effectiveness. AI leverages patient clinical and genetic data to predict outcomes, guide treatments, and optimize drug dosages and forms, enhancing benefits while minimizing side effects. AI-supported nanomedicine faces challenges like data fusion, ethics, and regulation, requiring better tools and interdisciplinary collaboration. This review highlights the importance of AI regarding patient care and urges scientists, medical professionals, and regulators to adopt AI for better outcomes.

AREAS COVERED

Personalized Nanomedicine, Material Discovery, AI-Driven Therapeutics, Data Integration, Drug Delivery, Patient Centric Care.

EXPERT OPINION

Today, AI can improve personalized health wellness through the discovery of new types of drug nanocarriers, nanomedicine of specific properties to tackle targeted medical needs, and an increment in efficacy along with safety. Nevertheless, problems such as ethical issues, data security, or unbalanced data sets need to be addressed. Potential future developments involve using AI and quantum computing together and exploring telemedicine i.e. the Internet-of-Medical-Things (IoMT) approach can enhance the quality of patient care in a personalized manner by timely decision-making.

Bibliometric Analysis of Comprehensive Geriatric Assessment from 2004 to 2023

Background

The global aging population necessitates specialized tools for complex geriatric health issues. Comprehensive Geriatric Assessment (CGA) provides multidimensional evaluations of elderly, integrating inputs from various professionals to create individualized care plans. This study aims to visually assess the research trends and hotspots in the field of CGA, review mainstream perspectives in this field, and provide a foundation for future research and treatment.

Methods

Original and review articles related to comprehensive geriatric assessment, published from 2004 to December 2023, were extracted from the Web of Science database. Four different software tools-CiteSpace, VOSviewer, Bibliometrix R package, and the Online Analysis Platform of Bibliometrics-were utilized for this comprehensive analysis.

Results

According to our retrieval strategy, we found a total of 4,411 related literatures. There has been a substantial increase in the research on comprehensive geriatric assessment in the past 20 years. These publications have been cited 157,366 times, with a mean of 35.68 citations per publication. The largest number of publications were from the US, and Italy ranked second (14.98%). Keyword burst and concurrence showed that "randomized trial", "adjuvant chemotherapy" and "breast cancer" were the top 3 most frequently occurring keywords.

Conclusion

Our bibliometric analysis reveals significant growth in CGA research over the past two decades, with a shift from cancer-focused studies to chronic conditions like frailty and sarcopenia. These findings highlight evolving priorities in geriatric care and underscore the need for future research to integrate technological advancements, such as AI, to enhance the precision, scalability, and cost-effectiveness of CGA in diverse settings.

Health Risk Assessment Using Machine Learning: Systematic Review

According to the World Health Organization, chronic illnesses account for over 70% of deaths globally, underscoring the need for effective health risk assessment (HRA). While machine learning (ML) has shown potential in enhancing HRA, no systematic review has explored its application in general health risk assessments. Existing reviews typically focus on specific conditions. This paper reviews published articles that utilize ML for HRA, and it aims to identify the model development methods. A systematic review following Tranfield et al.’s three-stage approach was conducted, and it adhered to the PRISMA protocol. The literature was sourced from five databases, including PubMed. Of the included articles, 42% (11/26) addressed general health risks. Secondary data sources were most common (14/26, 53.85%), while primary data were used in eleven studies, with nine (81.81%) using data from a specific population. Random forest was the most popular algorithm, which was used in nine studies (34.62%). Notably, twelve studies implemented multiple algorithms, while seven studies incorporated model interpretability techniques. Although these studies have shown promise in addressing digital health inequities, more research is needed to include diverse sample populations, particularly from underserved communities, to enhance the generalizability of existing models. Furthermore, model interpretability should be prioritized to ensure transparent, trustworthy, and broadly applicable healthcare solutions.

Prediction of cardiovascular markers and diseases using retinal fundus images and deep learning: a systematic scoping review

Rapid development in deep learning for image analysis inspired studies to focus on predicting cardiovascular risk using retinal fundus images. This scoping review aimed to identify and describe studies using retinal fundus images and deep learning to predict cardiovascular risk markers and diseases. We searched MEDLINE and Embase on 17 November 2023. Abstracts and relevant full-text articles were independently screened by two reviewers. We included studies that used deep learning for the analysis of retinal fundus images to predict cardiovascular risk markers or cardiovascular diseases (CVDs) and excluded studies only using predefined characteristics of retinal fundus images. Study characteristics were presented using descriptive statistics. We included 24 articles published between 2018 and 2023. Among these, 23 (96%) were cross-sectional studies and eight (33%) were follow-up studies with clinical CVD outcomes. Seven studies included a combination of both designs. Most studies (96%) used convolutional neural networks to process images. We found nine (38%) studies that incorporated clinical risk factors in the prediction and four (17%) that compared the results to commonly used clinical risk scores in a prospective setting. Three of these reported improved discriminative performance. External validation of models was rare (21%). There is increasing interest in using retinal fundus images in cardiovascular risk assessment with some studies demonstrating some improvements in prediction. However, more prospective studies, comparisons of results to clinical risk scores, and models augmented with traditional risk factors can strengthen further research in the field.

Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency

BACKGROUND & OBJECTIVES

Mental health disorders pose an increasing public health challenge worsened by the COVID-19 pandemic. The pandemic highlighted gaps in preparedness, emphasizing the need for early identification of at-risk groups and targeted interventions. This study aims to develop a risk assessment tool for anxiety, depression, and self-perceived stress using machine learning (ML) and explainable AI to identify key risk factors and stratify the population into meaningful risk profiles.

METHODS

We utilized a cohort of 9291 individuals from Northern Spain, with extensive post-COVID-19 mental health surveys. ML classification algorithms predicted depression, anxiety, and self-reported stress in three classes: healthy, mild, and severe outcomes. A novel combination of SHAP (SHapley Additive exPlanations) and UMAP (Uniform Manifold Approximation and Projection) was employed to interpret model predictions and facilitate the identification of high-risk phenotypic clusters.

RESULTS

The mean macro-averaged one-vs-one AUROC was 0.77 (± 0.01) for depression, 0.72 (± 0.01) for anxiety, and 0.73 (± 0.02) for self-perceived stress. Key risk factors included poor self-reported health, chronic mental health conditions, and poor social support. High-risk profiles, such as women with reduced sleep hours, were identified for self-perceived stress. Binary classification of healthy vs. at-risk classes yielded F1-Scores over 0.70.

CONCLUSIONS

Combining SHAP with UMAP for risk profile stratification offers valuable insights for developing effective interventions and shaping public health policies. This data-driven approach to mental health preparedness, when validated in real-world scenarios, can significantly address the mental health impact of public health crises like COVID-19.