From Data to Decisions: AI in Varicose Veins—Predicting, Diagnosing, and Guiding Effective Management
Background: Varicose veins (VVs) of the lower limbs, characterized by palpable, dilated, and tortuous veins, affect 2–73% of the global population. Artificial intelligence (AI) offers significant potential to enhance healthcare efficiency and decision-making, particularly in managing VVs through imp...
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| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Journal of Vascular Diseases |
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| Online Access: | https://www.mdpi.com/2813-2475/4/2/19 |
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| author | Lakshmi Sree Pugalenthi Chris Garapati Srivarshini Maddukuri Fnu Kanwal Jaspreet Kumar Naghmeh Asadimanesh Surbhi Dadwal Vibhor Ahluwalia Sidhartha Gautam Senapati Shivaram P. Arunachalam |
| author_facet | Lakshmi Sree Pugalenthi Chris Garapati Srivarshini Maddukuri Fnu Kanwal Jaspreet Kumar Naghmeh Asadimanesh Surbhi Dadwal Vibhor Ahluwalia Sidhartha Gautam Senapati Shivaram P. Arunachalam |
| author_sort | Lakshmi Sree Pugalenthi |
| collection | DOAJ |
| description | Background: Varicose veins (VVs) of the lower limbs, characterized by palpable, dilated, and tortuous veins, affect 2–73% of the global population. Artificial intelligence (AI) offers significant potential to enhance healthcare efficiency and decision-making, particularly in managing VVs through improved risk factor identification, diagnosis, and treatment planning. Objective: This abstract explores the role of AI in VV management, focusing on its applications in risk detection, image analysis, treatment planning, and surgical interventions, while addressing challenges to its widespread adoption. Methods: AI leverages advanced techniques such as computer vision and deep learning to analyze patient data, including medical history, symptoms, physical examinations, and imaging (e.g., ultrasounds, venography). It identifies patterns in large datasets to support personalized treatment plans, early risk detection, and disease severity assessment. Results: AI demonstrates promise in automating VV detection and classification, assessing disease severity, and aiding treatment planning. It enhances surgical interventions through preoperative planning, intraoperative navigation, and recurrence risk prediction. However, its adoption is limited by a lack of large-scale studies, concerns over accuracy, and the need for regulatory and ethical oversight. Conclusion: AI has the potential to revolutionize VV management by improving diagnosis, treatment precision, and patient outcomes. Further research, validation, and integration are critical to overcoming current limitations and fully realizing AI’s capabilities in clinical practice. |
| format | Article |
| id | doaj-art-00e04f20c7ff4a159d7abcc6576d618c |
| institution | Kabale University |
| issn | 2813-2475 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Vascular Diseases |
| spelling | doaj-art-00e04f20c7ff4a159d7abcc6576d618c2025-08-20T03:27:25ZengMDPI AGJournal of Vascular Diseases2813-24752025-05-01421910.3390/jvd4020019From Data to Decisions: AI in Varicose Veins—Predicting, Diagnosing, and Guiding Effective ManagementLakshmi Sree Pugalenthi0Chris Garapati1Srivarshini Maddukuri2Fnu Kanwal3Jaspreet Kumar4Naghmeh Asadimanesh5Surbhi Dadwal6Vibhor Ahluwalia7Sidhartha Gautam Senapati8Shivaram P. Arunachalam9Mercy Catholic Medical Center, Darby, PA 19023, USAAll India Institute of Medical Sciences, Raipur 492099, Chhattisgarh, IndiaDr. D. Y. Patil Medical College, Hospital & Research Centre, Pune 411018, Maharashtra, IndiaIcahn School of Medicine at Mount Sinai, Elmhurst Hospital Center, Elmhurst, NY 11373, USADayanand Medical College and Hospital, Ludhiana 141001, Punjab, IndiaDepartment of Critical Care Medicine, Mayo Clinic, Jacksonville, FL 55905, USADepartment of Critical Care Medicine, Mayo Clinic, Jacksonville, FL 55905, USANazareth Hospital, Philadelphia, PA 19152, USATexas Tech University Health Sciences Center, El Paso, TX 79905, USADepartment of Critical Care Medicine, Mayo Clinic, Jacksonville, FL 55905, USABackground: Varicose veins (VVs) of the lower limbs, characterized by palpable, dilated, and tortuous veins, affect 2–73% of the global population. Artificial intelligence (AI) offers significant potential to enhance healthcare efficiency and decision-making, particularly in managing VVs through improved risk factor identification, diagnosis, and treatment planning. Objective: This abstract explores the role of AI in VV management, focusing on its applications in risk detection, image analysis, treatment planning, and surgical interventions, while addressing challenges to its widespread adoption. Methods: AI leverages advanced techniques such as computer vision and deep learning to analyze patient data, including medical history, symptoms, physical examinations, and imaging (e.g., ultrasounds, venography). It identifies patterns in large datasets to support personalized treatment plans, early risk detection, and disease severity assessment. Results: AI demonstrates promise in automating VV detection and classification, assessing disease severity, and aiding treatment planning. It enhances surgical interventions through preoperative planning, intraoperative navigation, and recurrence risk prediction. However, its adoption is limited by a lack of large-scale studies, concerns over accuracy, and the need for regulatory and ethical oversight. Conclusion: AI has the potential to revolutionize VV management by improving diagnosis, treatment precision, and patient outcomes. Further research, validation, and integration are critical to overcoming current limitations and fully realizing AI’s capabilities in clinical practice.https://www.mdpi.com/2813-2475/4/2/19artificial intelligencevaricose veinschronic venous insufficiencymachine learningdeep learning |
| spellingShingle | Lakshmi Sree Pugalenthi Chris Garapati Srivarshini Maddukuri Fnu Kanwal Jaspreet Kumar Naghmeh Asadimanesh Surbhi Dadwal Vibhor Ahluwalia Sidhartha Gautam Senapati Shivaram P. Arunachalam From Data to Decisions: AI in Varicose Veins—Predicting, Diagnosing, and Guiding Effective Management Journal of Vascular Diseases artificial intelligence varicose veins chronic venous insufficiency machine learning deep learning |
| title | From Data to Decisions: AI in Varicose Veins—Predicting, Diagnosing, and Guiding Effective Management |
| title_full | From Data to Decisions: AI in Varicose Veins—Predicting, Diagnosing, and Guiding Effective Management |
| title_fullStr | From Data to Decisions: AI in Varicose Veins—Predicting, Diagnosing, and Guiding Effective Management |
| title_full_unstemmed | From Data to Decisions: AI in Varicose Veins—Predicting, Diagnosing, and Guiding Effective Management |
| title_short | From Data to Decisions: AI in Varicose Veins—Predicting, Diagnosing, and Guiding Effective Management |
| title_sort | from data to decisions ai in varicose veins predicting diagnosing and guiding effective management |
| topic | artificial intelligence varicose veins chronic venous insufficiency machine learning deep learning |
| url | https://www.mdpi.com/2813-2475/4/2/19 |
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