Potential of artificial intelligence methods in diseases of the venous system of the lower extremities

The use of new Artificial Intelligence (AI) methods is rapidly changing the face of modern medicine, offering advanced tools for diagnosis and treatment. The aim of this review is to map key concepts and research directions in the application of AI methods in the diagnosis and treatment oflower extremity venous diseases. This review is presented in accordance with the PRISMA ScR guidelines. A search was conducted in Medline/PubMed, Web of Science, Scopus, Embase, ResearchGate, Google Scholar, and the Cochrane Database of Systematic Reviews for studies published through December 2025. Studies were included in the review if they applied AI methods in diagnostics and clinical practice. A total of 1,071 studies were analyzed. The use of AI methods inlower extremity venous pathology is rapidly evolving and demonstrates significant potential for increasing diagnostic precision, automating workflows, and improving clinical decision making. The achieved accuracy of AI algorithms exceeded 90%, significantly reducing interobserver variability and ensuring consistent interpretation, eliminating operator dependency. The implementation of AI accelerated diagnostic workflows, reducing image analysis time by more than 50%. However, most studies relied on in-house datasets withlimited model interpretability and alack of external validation. Clinical implementation and outcome evaluation remain understudied. AI methods represent a transformative innovation that, by increasing diagnostic accuracy, streamlining workflows, and enabling a personalized approach, has significant potential to improve treatment outcomes for lower extremity venous pathology. AI has a promising future in its widespread use for prognostication, developing personalized treatment strategies for patients based on individual profiles, and creatinglarge-scale multicenter datasets to improve the reliability and versatility of algorithms. Priority should be given to external validation, standardization, and the implementation of AI in real-world clinical practice.

1. Attaran R, Edwards M, Bunte MC, Castro-Dominguez Y, Fukaya E, Harth K et al. SCAI Technical Review on Management of Chronic Venous Disease. J Soc Cardiovasc Angiogr Interv. 2025;4(8):103730. https://doi.org/10.1016/j.jscai.2025.103730.

2. Kumar P, Khan IA, Das A, Shah H. Chronic venous disease. Part 1: Pathophysiology and clinical features. Clin Exp Dermatol. 2022;47:1228–1239. https://doi.org/10.1111/ced.15143.

3. Katorkin SE, Kushnarchuk MY. Chronic compartment-syndrome of the lower limb: modern strategies for diagnosis and treatment. Ambulatornaya Khirurgiya. 2023;20(1):69–80. (In Russ.) https://doi.org/10.21518/akh2023-009.

4. Bogachev VYu, Komov KV, Somov NO, Varich GA, Mirzonov VA, Golosnitskiy PYu et al. Venous foot pump: Aliterature review and commentaries. Ambulatornaya Khirurgiya. 2025;22(1):70–82. (In Russ.) https://doi.org/10.21518/akh2025-019.

5. Gloviczki P, Lawrence PF, Wasan SM, Meissner MH, Almeida J, Brown KR et al. The 2023 Society for Vascular Surgery, American Venous Forum, and American Vein and Lymphatic Society clinical practice guidelines for the management of varicose veins of the lower extremities. Part II: Endorsed by the Society of Interventional Radiology and the Society for Vascular Medicine. J Vasc Surg Venous Lymphat Disord. 2024;12(1):101670. https://doi.org/10.1016/j.jvsv.2023.08.011.

6. Kotelnikov GP, Losev II, Sizonenko YV, Katorkin SE. Peculiarities of diagnostics and treatment tactics of patients with combinedlesion of the musculoskeletal and venous systems of thelowerlimbs. Novosti Khirurgii. 2013;21(3):42–53. (In Russ.) Available at: https://core.ac.uk/download/pdf/53875487.pdf.

7. Davydenko VV, Galileeva AN, Genzik OV, Monaenkova OS, Khartakhoyeva GL. The use of telemedicine after outpatient surgical treatment of varicose veins of the lower extremities. Ambulatornaya Khirurgiya. 2023;20(2):28-34. (In Russ.) https://doi.org/10.21518/akh2023-018.

8. Klingelhöfer D, Braun M, Dröge J, Groneberg DA, Brüggmann D. Research on artificial intelligence, machine and deep learning in medicine: global characteristics, readiness, and equity. Global Health. 2025;21(1):36. https://doi.org/10.1186/s12992-025-01128-1.

9. London AJ. Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability. Hastings Cent Rep. 2019;49(1):15–21. https://doi.org/10.1002/hast.973.

10. Hofmann B. Artificial intelligence – the emperor's new clothes? Digit Health. 2024;10:20552076241287370. https://doi.org/10.1177/20552076241287370.

11. Cutillo CM, Sharma KR, Foschini L, Kundu S, Mackintosh M, Mandl KD; MI in Healthcare Workshop Working Group. Machine intelligence in healthcare-perspectives on trustworthiness, explainability, usability, and transparency. NPJ Digit Med. 2020;3:47. https://doi.org/10.1038/s41746-020-0254-2.

12. Ogliari FR, Traverso A, Barbieri S, Montagna M, Chiabrando F, Versino E et al. Exploring machine learning tools in a retrospective casestudy of patients with metastatic non-small cell lung cancer treated with first-line immunotherapy: A feasibility single-centre experience. Lung Cancer. 2024;199:108075. https://doi.org/10.1016/j.lungcan.2024.108075.

13. Bhattad PB, Jain V. Artificial Intelligence in Modern Medicine – The Evolving Necessity of the Present and Role in Transforming the Future of Medical Care. Cureus. 2020;12(5):e8041. https://doi.org/10.7759/cureus.8041.

14. Bélisle-Pipon JC, Couture V, Roy MC, Ganache I, Goetghebeur M, Cohen IG. What Makes Artificial Intelligence Exceptional in Health Technology Assessment? Front Artif Intell. 2021;4:736697. https://doi.org/10.3389/frai.2021.736697.

15. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7.

16. Park SH, Do KH, Kim S, Park JH, Lim YS. What should medical students know about artificial intelligence in medicine? J Educ Eval Health Prof. 2019;16:18. https://doi.org/10.3352/jeehp.2019.16.18.

17. Medetalibeyoglu A, Velichko YS, Hart EM, Bagci U. Foundational artificial intelligence models and modern medical practice. BJR Artif Intell. 2024;2:ubae018. https://doi.org/10.1093/bjrai/ubae018.

18. Fasterholdt I, Naghavi-Behzad M, Rasmussen BSB, Kjølhede T, Skjøth MM, Hildebrandt MG, Kidholm K. Value assessment of artificial intelligence in medical imaging: a scoping review. BMC Med Imaging. 2022;22(1):187. https://doi.org/10.1186/s12880-022-00918-y.

19. Briganti G, Le Moine O. Artificial Intelligence in Medicine: Today and Tomorrow. Front Med. 2020;7:27. https://doi.org/10.3389/fmed.2020.00027.

20. Di Bidino R, Daugbjerg S, Papavero SC, Haraldsen IH, Cicchetti A, Sacchini D. Health technology assessment framework for artificial intelligence-based technologies. Int J Technol Assess Health Care. 2024;40(1):e61. https://doi.org/10.1017/S0266462324000308.

21. Khawaja Z, Bélisle-Pipon JC. Your robot therapist is not your therapist: understanding the role of AI-powered mental health chatbots. Front Digit Health. 2023;5:1278186. https://doi.org/10.3389/fdgth.2023.1278186.

22. Asaadi S, Martins KN, Lee MM, Pantoja JL. Artificial intelligence for the vascular surgeon. Semin Vasc Surg. 2023;36(3):394–400. https://doi.org/10.1053/j.semvascsurg.2023.05.001.

23. Wolk S, Kleemann M, Reeps C.Artificial intelligence in vascular surgery and vascular medicine. Chirurg. 2020;91:195–200. https://doi.org/10.1007/s00104-020-01143-5.

24. Matei SC, Olariu S, Ungureanu AM, Malita D, Petrașcu FM. Does Artificial Intelligence Bring New Insights in Diagnosing Phlebological Diseases? – A Systematic Review. Biomedicines. 2025;13(4):776. https://doi.org/10.3390/biomedicines13040776.

25. Butova X, Shayakhmetov S, Fedin M, Zolotukhin I, Gianesini S. Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management. J Pers Med. 2021;11(12):1280. https://doi.org/10.3390/jpm11121280.

26. Lareyre F, Raffort J. Artificial Intelligence in Vascular Diseases: From Clinical Practice to Medical Research and Education. Angiology. 2025:33197251324630. https://doi.org/10.1177/00033197251324630.

27. Li B, Eisenberg N, Beaton D, Lee DS, Aljabri B, Al-Omran L et al. Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair. J Am Heart Assoc. 2025;14:e039221. https://doi.org/10.1161/JAHA.124.039221.

28. Wickström HL, Öien RF, Fagerström C, Anderberg P, Jakobsson U, Midlöv PJ. Comparing video consultation with inperson assessment for Swedish patients with hard-to-heal ulcers: registry-based studies of healing time and of waiting time. BMJ Open. 2018;8(2):e017623. https://doi.org/10.1136/bmjopen-2017-017623.

29. Raffort J, Adam C, Carrier M, Lareyre F. Fundamentals in Artificial Intelligence for Vascular Surgeons. Ann Vasc Surg. 2020;65:254–260. https://doi.org/10.1016/j.avsg.2019.11.037.

30. Peters MDJ, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth. 2020;18(10):2119–2126. https://doi.org/10.11124/JBIES-20-00167.

31. Taylor RJ, Taylor AD, Smyth JV. Using an artificial neural network to predict healing times and risk factors for venous leg ulcers. J Wound Care. 2002;11(3):101–105. https://doi.org/10.12968/jowc.2002.11.3.26381.

32. Fukaya E, Flores AM, Lindholm D, Gustafsson S, Zanetti D, Ingelsson E, Leeper NJ. Clinical and Genetic Determinants of Varicose Veins. Circulation. 2018;138:2869–2880. https://doi.org/10.1161/CIRCULATIONAHA.118.035584.

33. Hoobi MM, Qaswaa A. Detection System of Varicose Disease using Probabilistic Neural Network. Int J Sci Res. 2017;6:2591–2596. Available at: https://www.ijsr.net/get_abstract.php?paper_id=ART20173435.

34. Bhavani RR, Jiji GW. Image registration for varicose ulcer classification using KNN classifier. Int J Comput Appl. 2018;40:88–97. https://doi.org/10.1080/1206212X.2017.1395108.

35. Bhavani R, Jiji W. Varicose ulcer (C6) wound image tissue classification using multidimensional convolutional neural networks. Imaging Sci J. 2019;67:1–11. https://doi.org/10.1080/13682199.2019.1663083.

36. Shi Q, Chen W, Pan Y, Yin S, Fu Y, Mei J, Xue Z. An Automatic Classification Method on Chronic Venous Insufficiency Images. Sci Rep. 2018;8:17952. https://doi.org/10.1038/s41598-018-36284-5.

37. Huang C, Tian J, Yuan C, Zeng P, He X, Chen H et al. Fully Automated Segmentation of Lower Extremity Deep Vein Thrombosis Using Convolutional Neural Network. Biomed Res Int. 2019;2019:3401683. https://doi.org/10.1155/2019/3401683.

38. Willan J, Katz H, Keeling D. The use of artificial neural network analysis can improve the risk-stratification of patients presenting with suspected deep vein thrombosis. Br J Haematol. 2019;185:289–296. https://doi.org/10.1111/bjh.15780.

39. Ohura N, Mitsuno R, Sakisaka M, Terabe Y, Morishige Y, Uchiyama A et al. Convolutional neural networks for wound detection: The role of artificial intelligence in wound care. J Wound Care. 2019;28(Suppl. 10):S13–S24. https://doi.org/10.12968/jowc.2019.28.Sup10.S13.

40. Fujita H. AI-based computer-aided diagnosis (AI-CAD): The latest review to read first. Radiol Phys Technol. 2020;13:6–19. https://doi.org/10.1007/s12194-019-00552-4.

41. Bai Y, Bo F, Ma W, Xu H, Liu D. Effect of Interventional Therapy on Iliac Venous Compression Syndrome Evaluated and Diagnosed by Artificial Intelligence Algorithm-Based Ultrasound Images. J Healthc Eng. 2021;2021:5755671. https://doi.org/10.1155/2021/5755671.

42. Kainz B, Heinrich MP, Makropoulos A, Oppenheimer J, Mandegaran R, Sankar S et al. Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. NPJ Digit Med. 2021;4:137. https://doi.org/10.1038/s41746-021-00503-7.

43. Levshinskii V, Galazis C, Losev A, Zamechnik T, Kharybina T, Vesnin S, Goryanin I. Using AI and Passive medical Radiometry for diagnostics (MWR) of venous diseases. Comput Methods Programs Biomed. 2021;215:106611. https://doi.org/10.1016/j.cmpb.2021.106611.

44. Ragg J. Artificial Intelligence in the Research of Origins of Vein Insufficiency. J Vasc Surg Venous Lymphat Disord. 2021;10:543–544. https://doi.org/10.1016/j.jvsv.2021.12.012.

45. Atreyapurapu V, Vyakaranam M, Atreya S, Gupta P, Atturu G. Assessment of Anatomical Changes in Advanced Chronic Venous Insufficiency Using Artificial Intelligence) and Machine Learning Techniques. J Vasc Surg Venous Lymphat Disord. 2022;10:571–572. https://doi.org/10.1016/j.jvsv.2021.12.059.

46. Barulina M, Sanbaev A, Okunkov S, Ulitin I, Okoneshnikov I. Deep Learning Approaches to Automatic Chronic Venous Disease Classification. Mathematics. 2022;10:3571. https://doi.org/10.3390/math10193571.

47. 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. 2022;19(2):436–446. https://doi.org/10.1111/iwj.13644.

48. Athavale A, Baier J, Ross E, Fukaya E. The potential of chatbots in chronic venous disease patient management. JVS Vasc Insights. 2023;1:100019. https://doi.org/10.1016/j.jvsvi.2023.100019.

49. Krishnan N, Muthu P. Detection of chronic venous insufficiency condition using transfer learning with convolutional neural networks based on thermal images. Biomed Eng Appl Basis Commun. 2023;36:2350030. https://doi.org/10.4015/S1016237223500308.

50. Krishnan N, Muthu P. CVINet: A deep learning based model for thediagnosis of chronic venous insufficiency in lowerextremity using infrared thermal images. Int J Imaging Syst Technol. 2024;34:e23004. https://doi.org/10.1002/ima.23004.

51. 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. Annu Int Conf IEEE Eng Med Biol Soc. 2022;2022:516–519. https://doi.org/10.1109/EMBC48229.2022.9871502.

52. Oliveira B, Torres HR, Morais P, Veloso F, Baptista AL, Fonseca JC, Vilaça JL. A multi-task convolutional neural network for classification and segmentation of chronic venous disorders. Sci Rep. 2023;13(1):761. https://doi.org/10.1038/s41598-022-27089-8.

53. Wu Z, Li Y, Lei J, Qiu P, Liu H, Yang X et al. Developing and optimizing a machine learning predictive model for post-thrombotic syndrome in alongitudinal cohort of patients with proximal deep venous thrombosis. J Vasc Surg Venous Lymphat Disord. 2023;11(3):555–564.e5. https://doi.org/10.1016/j.jvsv.2022.12.006.

54. Zolotukhin IA, Kvasnikov BB, Linnik OZH, Shiyakhmetov SB, Butova KG. Artificial Intelligence Based Application Accuracy in Diagnostics of C1-C2 Chronic Venous Disease. J Venous Disord. 2024;18:132–138. https://doi.org/10.17116/flebo202418021132.

55. Laddi A, Goyal S, Himani, Savlania A. Vein segmentation and visualization of upper and lower extremities using convolution neural network. Biomed Tech. 2024;69(5):455–464. https://doi.org/10.1515/bmt-2023-0331.

56. Lo ZJ, Mak MHW, Liang S, Chan YM, Goh CC, Lai T et al. Development of an explainable artificial intelligence model for Asian vascular wound images. Int Wound J. 2024;21(4):e14565. https://doi.org/10.1111/iwj.14565.

57. Cassidy B, McBride C, Kendrick C, Reeves ND, Pappachan JM, Fernandez CJ et al. An enhanced harmonic densely connected hybrid transformer network architecture for chronic wound segmentation utilising multi-colour space tensor merging. Comput Biol Med. 2025;192(Pt. A):110172. https://doi.org/10.1016/j.compbiomed.2025.110172.

58. Iding AFJ, Ten Cate V, Ten Cate H, Wild PS, Ten Cate-Hoek AJ. Untangling profiles of postthrombotic syndrome using unsupervised machine learning. Blood Adv. 2025;9(14):3631–3641. https://doi.org/10.1182/bloodadvances.2025015829.

59. Raizman R, Ramírez-GarciaLuna JL, Newaz T, Wang SC, Berry GK, Kong LY et al. Empowering Patients and Caregivers to Use Artificial Intelligence and Computer Vision for Wound Monitoring: Nonrandomized, Single-Arm Feasibility Study. J Particip Med. 2025;17:e69470. https://doi.org/10.2196/69470.

60. Tabari A, Ma Y, Alfonso J, Gebran A, Kaafarani H, Bertsimas D, Daye D. An artificial intelligence interpretable tool to predict risk of deep vein thrombosis after endovenous thermal ablation. J Vasc Surg Venous Lymphat Disord. 2025;13(5):102253. https://doi.org/10.1016/j.jvsv.2025.102253.

61. Zyada A, Fakhry A, Nagib S, Seken RA, Farrag M, Abouelseoud A et al. How Well Do Different AI Language Models Inform Patients About Radiofrequency Ablation for Varicose Veins? Cureus. 2025;17(6):e86537. https://doi.org/10.7759/cureus.86537.

62. Marques M, Almeida A, Pereira H. The Medicine Revolution Through Artificial Intelligence: Ethical Challenges of Machine Learning Algorithms in Decision-Making. Cureus. 2024;16(9):e69405. https://doi.org/10.7759/cureus.69405.

63. Aravazhi PS, Gunasekaran P, Benjamin NZY, Thai A, Chandrasekar KK, Kolanu ND et al. The integration of artificial intelligence into clinical medicine: Trends, challenges, and future directions. Dis Mon. 2025;71(6):101882. https://doi.org/10.1016/j.disamonth.2025.101882.

64. Hofmann B. Biases in AI: acknowledging and addressing the inevitable ethical issues. Front Digit Health. 2025;7:1614105. https://doi.org/10.3389/fdgth.2025.1614105.

65. Fischer UM, Shireman PK, Lin JC. Current applications of artificial intelligence in vascular surgery. Semin Vasc Surg. 2021;34(4):268–271. https://doi.org/10.1053/j.semvascsurg.2021.10.008.

66. Yousefi F, Dehnavieh R, Laberge M, Gagnon MP, Ghaemi MM, Nadali M, Azizi N. Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review. BMC Prim Care. 2025;26(1):196. https://doi.org/10.1186/s12875-025-02785-2.

67. Lareyre F, Chaudhuri A, Behrendt CA, Pouhin A, Teraa M, Boyle JR et al. Artificial intelligence-based predictive models in vascular diseases. Semin Vasc Surg. 2023;36:440–447. https://doi.org/10.1053/j.semvascsurg.2023.05.002.

68. Ryan L, Mataraso S, Siefkas A, Pellegrini E, Barnes G, Green-Saxena A et al. A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients. Clin Appl Thromb Hemost. 2021;27:1076029621991185. https://doi.org/10.1177/1076029621991185.

69. Lareyre F, Chaudhuri A, Behrendt CA, Pouhin A, Teraa M, Boyle JR et al. Artificial intelligence-based predictive models in vascular diseases. Semin Vasc Surg. 2023;36:440–447. https://doi.org/10.1053/j.semvascsurg.2023.05.002.

70. Lam BD, Dodge LE, Zerbey S, Robertson W, Rosovsky RP, Lake L et al. The potential use of artificial intelligence for venous thromboembolism prophylaxis and management: Clinician and healthcare informatician perspectives. Sci Rep. 2024;14:12010. https://doi.org/10.1038/s41598-024-62535-9.

71. Dossabhoy SS, Ho VT, Ross EG, Rodriguez F, Arya S. Artificial intelligence in clinical workflow processes in vascular surgery and beyond. Semin Vasc Surg. 2023;36(3):401–412. https://doi.org/10.1053/j.semvascsurg.2023.07.002.

72. Ungureanu A-M, Matei S-C, Malita D. Controversies in the Application of AI in Radiology – Is There Medico-Legal Support? Aspects from Romanian Practice. Diagnostics. 2025;15:230. https://doi.org/10.3390/diagnostics15020230.