Artificial intelligence in the management of chronic venous insufficiency: a systematic review
Main Article Content
Keywords
Artificial Intelligence, Chronic venous insufficiency, Disease Management
Abstract
Introduction: Chronic venous insufficiency (CVI) is a prevalent condition with significant health and economic burdens. As the condition progresses, it can severely impact patients' quality of life. Recent developments in Artificial Intelligence (AI) in healthcare offer promising solutions, providing tools that can enhance the accuracy of diagnosis, improve disease staging, and guide treatment decisions. This study aims to comprehensively synthesize and evaluate the role of AI techniques applicable to the management of CVI based on current evidence.
Methods: Adhering to 2020 PRISMA guidelines, we systematically searched Pubmed, Science Direct, CENTRAL, and Scopus for studies published in 2014–2024 which applied AI techniques in the management of CVI. We excluded studies that were case reports, case series, review articles, guidelines, or those that contained unpublished or incomplete data, or where the full text was not available in English or Indonesian. Analyses used descriptive statistics to summarize findings, emphasizing the reported statistical results. Risk-of-bias was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST).
Results: Our review of 9 studies found that AI, including deep learning and machine learning, achieved moderate to high accuracy or performance in CVI management, including diagnosis and prognosis. AI techniques employed include deep convolutional neural networks, natural language processing, computer vision, fuzzy logic, logistic regression, and random forest. CVI severity, ulcer size and etiologies, as well as the risk of ulcer development could be predicted by the AI. The majority of the studies (88,9%) demonstrated a high or unclear risk of bias. AI has demonstrated significant potential in enhancing the management of CVI patients, particularly in diagnosis, prognosis, and decision-making support.
Conclusion: AI has shown significant potential in enhancing the management of CVI patients, particularly in diagnosis, prognosis, and decision-making support. However, further high-quality quantitative studies are needed to confirm its effectiveness.
References
2. Prochaska JH, Arnold N, Falcke A, Kopp S, Schulz A, Buch G, et al. Chronic venous insufficiency, cardiovascular disease, and mortality: a population study. Eur Heart J. 2021;42(40):4157–65. Available from: http://dx.doi.org/10.1093/eurheartj/ehab495
3. Nazeha N, Lee JY, Saffari SE, Meng L, Ho P, Ng YZ, et al. The burden of costs on health services from patients with venous leg ulcers in Singapore. Int Wound J. 2022/09/13. 2023;20(3):845–52. Available from: https://pubmed.ncbi.nlm.nih.gov/36098383
4. Ma H, O’Donnell TF, Rosen NA, Iafrati MD. The real cost of treating venous ulcers in a contemporary vascular practice. J Vasc Surg Venous Lymphat Disord. 2014;2(4):355–61. Available from: http://dx.doi.org/10.1016/j.jvsv.2014.04.006
5. Azar J, Rao A, Oropallo A. Chronic venous insufficiency: a comprehensive review of management. J Wound Care. 2022;31(6):510–9. Available from: http://dx.doi.org/10.12968/jowc.2022.31.6.510
6. Seetharam K, Brito D, Farjo PD, Sengupta PP. The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review. Front Cardiovasc Med. 2020;7:618849. Available from: https://pubmed.ncbi.nlm.nih.gov/33426010
7. Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Ann Intern Med. 2019;170(1):51–8. Available from: http://dx.doi.org/10.7326/m18-1376
8. Athavale A, Baier J, Ross E, Fukaya E. The potential of chatbots in chronic venous disease patient management. JVS-vascular insights. 2023/06/19. 2023;1:100019. Available from: https://pubmed.ncbi.nlm.nih.gov/37701430
9. Chan KS, Liang S, Cho YT, Chan YM, Tan AHM, Muthuveerappa S, et al. Clinical validation of a machine-learning-based handheld 3-dimensional infrared wound imaging device in venous leg ulcers. Int Wound J. 2021/06/14. 2022;19(2):436–46. Available from: https://pubmed.ncbi.nlm.nih.gov/34121320
10. Deinsberger J, Moschitz I, Marquart E, Manz‐Varga AK, Gschwandtner ME, Brugger J, et al. Development of a localization‐based algorithm for the prediction of leg ulcer etiology. JDDG J der Dtsch Dermatologischen Gesellschaft. 2023;21(11):1339–49. Available from: http://dx.doi.org/10.1111/ddg.15192
11. Fong KY, Lai TP, Chan KS, See IJ Le, Goh CC, Muthuveerappa S, et al. Clinical validation of a smartphone application for automated wound measurement in patients with venous leg ulcers. Int Wound J. 2022/08/08. 2023;20(3):751–60. Available from: https://pubmed.ncbi.nlm.nih.gov/36787270
12. de Franciscis S, Fregola S, Gallo A, Argirò G, Barbetta A, Buffone G, et al. PredyCLU: a prediction system for chronic leg ulcers based on fuzzy logic; part I - exploring the venous side. Int Wound J. 2015/11/06. 2016;13(6):1349–53. Available from: https://pubmed.ncbi.nlm.nih.gov/26542425
13. Han X, Hu N. Prediction of one- and three-months yoga practices effect on chronic venous insufficiency based on machine learning classifiers. Egypt Informatics J. 2024;27:100507. Available from: http://dx.doi.org/10.1016/j.eij.2024.100507
14. Malihi L, Hüsers J, Richter ML, Moelleken M, Przysucha M, Busch D, et al. Automatic Wound Type Classification with Convolutional Neural Networks [Internet]. Studies in Health Technology and Informatics. IOS Press; 2022. Available from: http://dx.doi.org/10.3233/shti220717
15. Oliveira B, Torres HR, Morais P, Baptista A, Fonseca J, Vilaca JL. Classification of Chronic Venous Disorders using an Ensemble Optimization of Convolutional Neural Networks [Internet]. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE; 2022. p. 516–9. Available from: http://dx.doi.org/10.1109/embc48229.2022.9871502
16. Oliveira B, Torres HR, Morais P, Veloso F, Baptista AL, Fonseca JC, et al. A multi-task convolutional neural network for classification and segmentation of chronic venous disorders. Sci Rep. 2023;13(1):761. Available from: https://pubmed.ncbi.nlm.nih.gov/36641527
17. Yamuna U, Majumdar V, Saoji AA. Effect of Yoga on homocysteine level, symptomatology and quality of life in industrial workers with Chronic Venous Insufficiency: Study protocol for a randomized controlled trial. Adv Integr Med. 2022;9(2):119–25. Available from: http://dx.doi.org/10.1016/j.aimed.2022.02.002
18. Honi DG, Szathmary L. A one-dimensional convolutional neural network-based deep learning approach for predicting cardiovascular diseases. Informatics Med Unlocked. 2024;49:101535. Available from: http://dx.doi.org/10.1016/j.imu.2024.101535
19. Arunkumar M, Mohanarathinam A, Subramaniam K. Detection of varicose vein disease using optimized kernel Boosted ResNet-Dropped long Short term Memory. Biomed Signal Process Control. 2024;87:105432. Available from: http://dx.doi.org/10.1016/j.bspc.2023.105432
20. Thanka MR, Edwin EB, Joy RP, Priya SJ, Ebenezer V. Varicose Veins Chronic Venous Diseases Image Classification Using Multidimensional Convolutional Neural Networks [Internet]. 2022 6th International Conference on Devices, Circuits and Systems (ICDCS). IEEE; 2022. p. 364–8. Available from: http://dx.doi.org/10.1109/icdcs54290.2022.9780842
21. Barulina M, Sanbaev A, Okunkov S, Ulitin I, Okoneshnikov I. Deep Learning Approaches to Automatic Chronic Venous Disease Classification. Mathematics. 2022;10(19):3571. Available from: http://dx.doi.org/10.3390/math10193571
22. Reifs D, Casanova-Lozano L, Reig-Bolaño R, Grau-Carrion S. Clinical validation of computer vision and artificial intelligence algorithms for wound measurement and tissue classification in wound care. Informatics Med Unlocked. 2023;37:101185. Available from: http://dx.doi.org/10.1016/j.imu.2023.101185
23. Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Heal. 2019;1(6):e271–97. Available from: http://dx.doi.org/10.1016/s2589-7500(19)30123-2
24. Chen JH, Asch SM. Machine Learning and Prediction in Medicine - Beyond the Peak of Inflated Expectations. N Engl J Med. 2017;376(26):2507–9. Available from: https://pubmed.ncbi.nlm.nih.gov/28657867
25. Summers KL, Kerut EK, To F, Sheahan CM, Sheahan MG. Machine learning-based prediction of abdominal aortic aneurysms for individualized patient care. J Vasc Surg. 2024;79(5):1057-1067.e2. Available from: http://dx.doi.org/10.1016/j.jvs.2023.12.046
26. Chlorogiannis D-D, Apostolos A, Chlorogiannis A, Palaiodimos L, Giannakoulas G, Pargaonkar S, et al. The Role of ChatGPT in the Advancement of Diagnosis, Management, and Prognosis of Cardiovascular and Cerebrovascular Disease. Healthc (Basel, Switzerland). 2023;11(21):2906. Available from: https://pubmed.ncbi.nlm.nih.gov/37958050
27. Dave T, Athaluri SA, Singh S. ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations. Front Artif Intell. 2023;6:1169595. Available from: https://pubmed.ncbi.nlm.nih.gov/37215063