{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T14:17:05Z","timestamp":1790000225474,"version":"4.0.1"},"reference-count":41,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T00:00:00Z","timestamp":1789862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, making early identification of individuals at elevated cardiovascular risk essential for preventing major adverse cardiac events (MACE). Retinal fundus photographs provide a non-invasive window into systemic vascular health, as retinal microvascular and optic-disc characteristics have been associated with cardiovascular abnormalities. This study aims to develop an enhanced ConvNeXt-based deep learning framework, termed CSP-ConvNeXt, for automated prediction of CVD-related risk categories from retinal fundus images, including Mild, Moderate, Severe, and Proliferative Diabetic Retinopathy (DR), as well as Hypertensive Retinopathy (HR). Methods: The proposed CSP-ConvNeXt framework integrates a hierarchical convolutional backbone with Cross Stage Partial (CSP) channel partitioning and large-kernel depthwise convolutions to capture both fine-grained retinal microvascular features and broader anatomical structures associated with cardiovascular risk. An Efficient Squeeze-and-Excitation (ESE) attention mechanism is incorporated to enhance the representation of clinically relevant vascular regions while reducing the influence of irrelevant background information. A robust data-augmentation and preprocessing pipeline is employed to improve model generalizability under varying retinal imaging conditions. Two benchmark retinal datasets, comprising Mendeley Retinal Fundus images and the APTOS 2019 dataset, are used for model training and evaluation. Results: The CSP-ConvNeXt framework achieves an overall classification accuracy of 87%, demonstrating effective discrimination among the considered CVD-related retinal risk categories. The model achieves high precision and recall values, indicating consistent classification performance across the evaluated categories. Statistical analysis demonstrates significant interclass differentiation, with a p-value &lt; 1 \u00d7 10\u221216, supporting the discriminative capability of the learned retinal representations. Conclusions: The results demonstrate that the CSP-ConvNeXt framework, combining CSP-based feature extraction, ESE attention, large-kernel depthwise convolutions, and hierarchical representation learning, can effectively capture retinal vascular and anatomical patterns associated with cardiovascular risk. The results highlight the potential of advanced ConvNeXt-based architectures for automated, non-invasive CVD-related risk screening using retinal fundus photographs. This approach may provide a valuable complementary tool for early cardiovascular risk assessment, particularly in settings where conventional cardiovascular screening is less accessible.<\/jats:p>","DOI":"10.3390\/diagnostics16183058","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T12:50:03Z","timestamp":1789995003000},"page":"3058","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cardiovascular Disease Risk Assessment from Retinal Fundus Images Using a CSP-ConvNext Model"],"prefix":"10.3390","volume":"16","author":[{"given":"Dhayanithi","family":"Jaganathan","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Sona College of Technology, Salem 636005, India"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5862-499X","authenticated-orcid":false,"given":"Sathiyabhama","family":"Balasubramaniam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sona College of Technology, Salem 636005, India"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Harini Soundharyaa","family":"T","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sona College of Technology, Salem 636005, India"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0624-1991","authenticated-orcid":false,"given":"Seshathiri","family":"Dhanasekaran","sequence":"additional","affiliation":[{"name":"Department of Computer Science, UiT The Arctic University of Norway, 9037 Troms\u00f8, Norway"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,20]]},"reference":[{"key":"ref_1","unstructured":"(2026, February 01). 2026 Heart Disease & Stroke Statistics Update FactSheet Global Burden of Disease by American Heart Association. Available online: https:\/\/www.heart.org\/en\/about-us\/heart-and-stroke-association-statistics."},{"key":"ref_2","unstructured":"World Health Organization (2026, February 01). WHO Cardiovascular Diseases. Available online: https:\/\/www.who.int\/health-topics\/cardiovascular-diseases#tab=tab_1."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","article-title":"Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning","volume":"2","author":"Poplin","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine Learning in Medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1093\/ehjdh\/ztae068","article-title":"Prediction of cardiovascular markers and diseases using retinal fundus images: A scoping review","volume":"5","author":"Li","year":"2024","journal-title":"Eur. Heart J. Digit. Health"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"20406223231209895","DOI":"10.1177\/20406223231209895","article-title":"Deep learning-based fundus image analysis for cardiometabolic and cardiovascular disease prediction: A review","volume":"14","author":"Chikumba","year":"2023","journal-title":"Ther. Adv. Chronic Dis."},{"key":"ref_9","first-page":"26","article-title":"The Oculomics paradigm: A comprehensive review","volume":"18","author":"Silberberg","year":"2026","journal-title":"Optom. Times J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e0023","DOI":"10.5041\/RMMJ.10558","article-title":"A Narrative Review of Multimodal Data Fusion Strategies for Precision Risk Prediction in Coronary Artery Disease: Advances, Challenges, and Future Informatics Directions","volume":"16","author":"Zhou","year":"2025","journal-title":"Rambam Maimonides Med. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1308\/147870804290","article-title":"Artificial intelligence in medicine","volume":"86","author":"Ramesh","year":"2004","journal-title":"Ann. R. Coll. Surg. Engl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mhamdi, L., Dammak, O., Cottin, F., and Ben Dhaou, I. (2022). Artificial Intelligence for Cardiac Diseases Diagnosis and Prediction Using ECG Images on Embedded Systems. Biomedicines, 10.","DOI":"10.3390\/biomedicines10082013"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Al-Absi, H.R.H., Islam, M.T., Refaee, M.A., Chowdhury, M.E.H., and Alam, T. (2022). Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning. Sensors, 22.","DOI":"10.3390\/s22124310"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.infoh.2024.05.003","article-title":"Enhancing retinal disease diagnosis through AI: Evaluating performance, ethical considerations, and clinical implementation","volume":"1","author":"Fatima","year":"2024","journal-title":"Inform. Health"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105458","DOI":"10.1016\/j.pdpdt.2026.105458","article-title":"AI in ophthalmology: A bibliometric analysis of retinal imaging innovations and global research collaboration","volume":"59","author":"Zhao","year":"2026","journal-title":"Photodiagnosis Photodyn. Ther."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"110057","DOI":"10.1016\/j.engappai.2025.110057","article-title":"Exploring the synergies of hybrid convolutional neural network and Vision Transformer architectures for computer vision: A survey","volume":"144","author":"Haruna","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gencer, G., and Gencer, K. (2025). Advanced retinal disease detection from OCT images using a hybrid squeeze and excitation enhanced model. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0318657"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107886","DOI":"10.1016\/j.engappai.2024.107886","article-title":"CSPNeXt: A new efficient token hybrid backbone","volume":"132","author":"Chen","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Muthuraja, M., Shanthi, N., Aravindhraj, N., Indhuja, T., Madhuvarshini, M., and Hariharan, J. (2026). A Vessel-Aware Deep Learning Framework for Cardiovascular Risk Prediction using Retinal Images. Proceedings of the Fourth International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), IEEE. Available online: https:\/\/api.semanticscholar.org\/CorpusID:288705443.","DOI":"10.1109\/ICAISS68683.2026.11525572"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1007\/s11517-025-03324-y","article-title":"Deep learning for retinal vessel segmentation: A systematic review of techniques and applications","volume":"63","author":"Liu","year":"2025","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1007\/s11042-026-21181-1","article-title":"The role of deep learning-based algorithms in retinal image processing: A review","volume":"85","author":"Celik","year":"2026","journal-title":"Multimed. Tools Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102998","DOI":"10.1016\/j.displa.2025.102998","article-title":"A multimodal deep learning framework for automated major adverse cardiovascular events prediction in patients with end-stage renal disease integrating clinical and cardiac MRI data","volume":"88","author":"Sun","year":"2025","journal-title":"Displays"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"81542","DOI":"10.1109\/ACCESS.2019.2923707","article-title":"Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques","volume":"7","author":"Thirumalai","year":"2019","journal-title":"IEEE Access"},{"key":"ref_26","first-page":"122","article-title":"Deep learning models on Heart Disease Estimation\u2014A review","volume":"4","author":"Sharean","year":"2022","journal-title":"J. Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105624","DOI":"10.1016\/j.compbiomed.2022.105624","article-title":"Ensemble framework for cardiovascular disease prediction","volume":"146","author":"Tiwari","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.neunet.2018.01.009","article-title":"Extracting cardiac dynamics within ECG signal for human identification and cardiovascular diseases classification","volume":"100","author":"Deng","year":"2018","journal-title":"Neural Netw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108028","DOI":"10.1016\/j.bspc.2025.108028","article-title":"Automating vessel segmentation in the heart and brain: A trend to develop multi-modality and label-efficient deep learning techniques","volume":"109","author":"Elsayed","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3928","DOI":"10.1007\/s10278-025-01481-y","article-title":"Vision Transformers in Medical Imaging: A Comprehensive Review of Advancements and Applications Across Multiple Diseases","volume":"38","author":"Aburass","year":"2025","journal-title":"J. Imaging Inform. Med."},{"key":"ref_31","unstructured":"Nikhil, S., and Anjali, T. (2024, January 4\u20136). Enhancing Cardiovascular Risk Prediction with Vision Transformer and Retinal Image Analysis. Proceedings of the 2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"110026","DOI":"10.1016\/j.bspc.2026.110026","article-title":"DRD-ViT: A dual-scale hybrid vision transformer with convolutional embedding for robust diabetic retinopathy detection in fundus images","volume":"120","author":"Abini","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103956","DOI":"10.1016\/j.jvcir.2023.103956","article-title":"MIL-ViT: A multiple instance vision transformer for fundus image classification","volume":"97","author":"Bi","year":"2023","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_34","unstructured":"Jain, S., Bhargava, B.K., Kalra, D., and Groppe, S. (2025). Retinal Fundus Multi-Disease Image Classification Using Hybrid CNN-Transformer-Ensemble Architectures. Proceedings of the International Health Informatics Conference. IHIC 2023. Lecture Notes in Networks and Systems, Springer."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kaur, H. (2025). A Hybrid CNN-Transformer Model for Enhanced Heart Disease Prediction. Proceedings of the 7th International Conference on Signal Processing, Computing and Control (ISPCC), IEEE.","DOI":"10.1109\/ISPCC66872.2025.11039440"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Wu, Y., Li, Y., Cai, L., and Ihnaini, B. (2025). A Comprehensive Review of Explainable Artificial Intelligence (XAI) in Computer Vision. Sensors, 25.","DOI":"10.3390\/s25134166"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1109\/TBME.2012.2205687","article-title":"An ensemble classification-based approach applied to retinal blood vessel segmentation","volume":"59","author":"Fraz","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bhardwaj, R., Sethi, A., and Nambiar, R. (2014). Big data in genomics: An overview. Proceedings of the 2014 IEEE International Conference on Big Data (Big Data), Beijing, China, 4\u20137 August 2014, IEEE.","DOI":"10.1109\/BigData.2014.7004392"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Deepika, K., and Seema, S. (2016). Predictive analytics to prevent and control chronic diseases. Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Bangalore, India, 21\u201323 July 2016, IEEE.","DOI":"10.1109\/ICATCCT.2016.7912028"},{"key":"ref_40","unstructured":"Islam, M.S., and Jahan, I. (2026, January 15). Diabetic Retinopathy and Retinal Diseases Fundus Image Dataset. Mendeley Data 2025, V1. Available online: https:\/\/data.mendeley.com\/datasets\/6ry9n79gb4\/1."},{"key":"ref_41","unstructured":"Karthik, M., and Dane, S. (2026, January 15). APTOS 2019 Blindness Detection. Available online: https:\/\/www.kaggle.com\/competitions\/aptos2019-blindness-detection\/data."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/18\/3058\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T13:24:35Z","timestamp":1789997075000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/18\/3058"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,20]]},"references-count":41,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["diagnostics16183058"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16183058","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,20]]}}}