M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images

References

• Alqahtani A, Alsubai S, Sha M, Vilcekova L, Javed T. 2022. Cvddetection using ensemble learning. Comput Intell Neurosci. 2022:1–9. doi: 10.1155/2022/5267498.
   Google Scholar
• Anitha K, Arunprasath M, Kumar D, Gokul P, Gowtham N. 2023. Cardio vascular disease prediction based on ANN algorithm. 2023 Second International Conference on Electronics and Renewable Systems (ICEARS); Tuticorin, India. p. 919–925. doi: 10.1109/ICEARS56392.2023.10085084.
   Google Scholar
• Barriada RG, Masip D. 2022. An overview of deep-learning-based methods for cardiovascular risk assessment with retinal images. Diagnostics. 13(1):68.
   Web of Science ®Google Scholar
• Bensenane H, Aksa D, Omari FW, Rahmoun A. 2022. A deep learning-based cardio-vascular disease diagnosis system. Indones J Electr Eng Comput Sci. 25(2):963. doi: 10.11591/ijeecs.v25.i2.pp963-971.
   Google Scholar
• Borra SPR, Sumathy B, Ismail BM, Kumar SN, Phasinam K, Ramesh R. 2022. Retracted article: Google’s new AI technology detects cardiac issues using retinal scan. Appl Nanosci. 13(4):3137. doi: 10.1007/s13204-021-02208-5.
   Google Scholar
• Castro AR, Silva SO, Soares SC. 2018. The use of high sensitivity C-Reactive protein in CVDDetection. J Pharm Pharm Sci. 21(1):496–503. doi: 10.18433/jpps29872.
   PubMedGoogle Scholar
• Chakravadhanula K. 2020. An inexpensive smartphone-based device and predictive models for rapid, non-invasive, and point-of-care monitoring of ocular and cardiovascular complications related to diabetes mellitus. medRxiv.
   Google Scholar
• Chaudhary S, Gajjar S, Bhowmick P. 2020. Detection of Cardio Vascular Disease Using Fuzzy Logic.
   Google Scholar
• Cheung CY, Dejiang X, Cheng C-Y, Sabanayagam C, Tham Y-C, Marco Y, Hyungtaek Rim T. 2021. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nat Biomed Eng. 5(6):498–508.
   PubMed Web of Science ®Google Scholar
• Damodharan D, Kumar Goel A, Wang H, Wang Z, Hu S, Cao C, Xiao H. 2022. In-depth metaproteomics analysis of oral microbiome for lung cancer. Int J Electr Electron Res. 2022. doi: 10.34133/2022/9781578.
   Google Scholar
• Dongwon Y. 2018. Letter: Cardio-ankle vascular index as a surrogate marker of early atherosclerotic cardiovascular disease in Koreans with type 2 diabetes mellitus (diabetes & metabolism journal 2018;42: 285-95). Diabetes Metab J. 42(5):447–448. doi: 10.4093/dmj.2018.0185.
   PubMedGoogle Scholar
• Dutta R Zouaoui-Boudjeltia K, Kotsalos C, Rousseau A, de Sousa, DR, Desmet, JM, Van Meerhaeghe A, Mira A, Chopard B. “Interpretable pathological test for cardio-vascular disease: approximate bayesian computation with distance learning.” arXiv: Methodology (2020)
   Google Scholar
• Dutta R, Zouaoui Boudjeltia K, Kotsalos C, Rousseau A, Ribeiro de Sousa D, Desmet J-M, Van Meerhaeghe A, Mira A, Chopard B. 2020. Personalized pathology test for cardio-vascular disease: approximate bayesian computation with discriminative summary statistics learning. PLoS Comput Biol. 18(3):e1009910. doi: 10.1371/journal.pcbi.1009910.
   Web of Science ®Google Scholar
• Goyal S. 2023. Ev-MDP: a novel metaheuristic technique for heart disease detection. 2023 International Conference on Device Intelligence, Computing and Communication Technologies (DICCT); Dehradun, India. p. 188–191. doi: 10.1109/DICCT56244.2023.10110296.
   Google Scholar
• Graby J, Sellek J, Khavandi A, Loughborough W, Hudson BJ, Shirodaria C, Downie P, Antoniades C, Rodrigues JCL. 2022. Coronary CT angiography derived pericoronary inflammation and bespoke cardiovascular risk prediction in the lipid clinic: beyond the calcium score. Eur Heart J. 43(Supplement_2). doi: 10.1093/eurheartj/ehac544.2315.
   Google Scholar
• Kavitha T, Hemalatha S, Mownika S, Priya, GM, Vigashini, JK. 2022. Review on cardio vascular disease prediction using machine learning. 2022 International Conference on Computer Communication and Informatics (ICCCI); Coimbatore, India. p. 1–4.
   Google Scholar
• Konstantonis GD, Singh, KV, Sfikakis, PP, Jamthikar, AD, Kitas, GD, Gupta, SK, Saba L, Verrou K, Khanna, NN, Ruzsa Z, Sharma, AM. 2022. Cvddetection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients. Rheumatol Int 42:1–25.
   PubMedGoogle Scholar
• Kumar P, Sridevi M. 2023. GFCI IDENTIFICATION OF CARDIO VASCULAR PICTURE. Int Res J Mod Eng Technol Sci.
   Google Scholar
• Lakshmi Prabha P, Jayanthy AK, Prem Kumar C, Ramraj B. 2021. Prediction of cardiovascular risk by measuring carotid intima media thickness from an ultrasound image for type II diabetic mellitus subjects using machine learning and transfer learning techniques. J Supercomput. 77(9):10289–10306. doi: 10.1007/s11227-021-03676-w.
   Web of Science ®Google Scholar
• Lan E. Performer: a novel PPG-to-ECG reconstruction transformer for a digital biomarker of CVDDetection. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2022): 1990–1998.
   Google Scholar
• Lee YC, Cha J, Shim I, Park W-Y, Kang SW, Lim DH, Won H-H. 2023. Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction. NPJ Digital Med. 6(1). doi: 10.1038/s41746-023-00748-4.
   Web of Science ®Google Scholar
• Lin Z,Shi D, Zhang D, Shang X, He M, Ge Z. 2022. Camera Adaptation for Fundus-Image-Based CVD Risk Estimation. Inter Conf Med Image Computing Computer-Assisted Inter 13432:593–603.
   Google Scholar
• Parmar BPatel S, Kanani J, Vaghasia M, Patel K. “Cardio-Vascular Risk Detection System using different Machine Learning Techniques.” 2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) (2022): 1–6.
   Google Scholar
• Rajjliwal NS and Chetty G. 2022. Cvddetection based on interpretable and explainable AI. 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE); Gold Coast, Australia. p. 1–7.
   Google Scholar
• Reena A, et al. 2019. Comparison of cardio vascular magnetic resonance imaging (mri) and positron emission tomography (pet) for detection of myocardial viability. Int Cardiovasc Res J. 8:1–4.
   Google Scholar
• Sahu DN, Singh VP. 2022. Predictive analysis for the detection of cardio vascular disease (CVD) based on machine learning classification algorithm. IJARCCE. 11(1). doi: 10.17148/IJARCCE.2022.11148.
   Google Scholar
• Sarmiladevi N, Basker K, Tamilarasu R, Nivetha L. 2022. An efficient detection system for cardio vascular disease using machine learning algorithms. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE); Greater Noida, India. p. 951–955.
   Google Scholar
• Sharma A, Christodorescu R, Agbariah A, Duda-Seiman D, Dahdal D, Man D, Kundnani NR, Cretu OM, Dragan S. 2022. Cardiovascular risk prediction parameters for better management in rheumatic diseases. Healthcare. 10(2):312. doi: 10.3390/healthcare10020312.
   Web of Science ®Google Scholar
• Song G, Zhang J, Mao D, Chen G, Pang C. 2022. A multimodel fusion method for CVDDetection using ECG. Emerg Med Int. 2022:1–10. doi: 10.1155/2022/3561147.
   Web of Science ®Google Scholar
• Srikanth V and Mahesh TR. A CVDDetection system using machine learning. 2022 5th International Conference on Contemporary Computing and Informatics (IC3I) (2022): 685–689.
   Google Scholar
• Vaghefi E,Squirrell D, An S, Yang S, Marshall J. “Replacing the framingham-based equation for prediction of CVDrisk and adverse outcome by using artificial intelligence and retinal imaging.” ArXiv abs/2207.14685 (2022)
   Google Scholar
• Wang P,Lin Z, Yan X, Chen Z, Ding M, Song Y, Meng L. 2022. A wearable ECG monitor for deep learning based real-time CVDDetection. Electr Eng Syst Sci.
   Google Scholar