From Data to Decisions: AI in Varicose Veins—Predicting, Diagnosing, and Guiding Effective Management

Abstract

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.

1. Introduction

VVs are a part of chronic venous diseases, which also encompass various venous abnormalities such as dilated intradermal veins, spider veins, reticular veins, and telangiectasia [1]. They are characterized as palpable veins larger than 3 mm that appear dilated and tortuous, primarily affecting the lower limbs [2]. Studies indicate that VVs affect between 2% and 73% of the global population, with a higher prevalence among women than men [3,4]. They are more frequently observed in industrialized nations compared to underdeveloped regions [5]. Additionally, visible VVs are more common among Hispanics (26.3%) than Asians (18.7%) [4]. Several risk factors were identified, including aging, female gender, family history of cardiovascular diseases such as hypertension and coronary artery disease, prolonged standing, pregnancy, obesity, and hereditary influences [3]. The underlying pathophysiology involves venous hypertension, valvular incompetence, structural vein wall changes, inflammation, and altered shear stress [1].

Patients with VVs usually present symptoms of displeasing visible veins, pain, swelling that worsens on standing or at the end of the day, itching, skin changes, ulceration, thrombophlebitis, and bleeding of the veins [6,7]. Some of the common signs are edema, lipodermatosclerosis, varicose eczema or thrombophlebitis, ulcers typically over the medial malleolus, and atrophy blanche [8]. Patients should be treated immediately if they have complications like superficial bleeding and superficial venous thrombosis [9].

The gold standard imaging used for VVs when the disease is severe or when interventional therapy is considered is venous duplex ultrasonography [10]. It has the ability to determine the incompetence of the saphenous junctions, the diameter of the junctions, the extent of blood reflux, and the location and size of other veins that are incompetent [11]. Other modes of imaging that are considered only if venous ultrasonography is inconclusive or in complex surgical situations are computed tomography (CT), magnetic resonance imaging (MRI), venography, and plethysmography [9].

Treatment options are mainly dependent on the symptoms and the patient’s preferences. The treatment is broadly classified into conservative management and interventional therapy. Conservative management includes compression stockings, leg elevation, lifestyle modification (avoidance of prolonged standing, exercise, and modification of cardiovascular risk factors), weight loss, and phlebotonic drugs [12]. Interventional therapy includes thermal ablation, endovenous sclerotherapy, and surgery (ligation and stripping or phlebectomy) [9].

AI refers to technologies that simulate human intelligence processes, including cognition, deep learning, adaptation, engagement, and sensory perception [13]. AI applications in healthcare have expanded significantly, encompassing diagnosis, treatment, and decision support. Key AI technologies include machine learning—comprising neural networks that assess disease risk and deep learning that enhances imaging analysis—natural language processing for speech and text recognition, and rule-based expert systems for clinical decision-making, which are gradually being replaced by machine learning algorithms [14]. Additionally, physical robots enhance surgical precision by improving visualization, minimizing invasiveness, and refining suturing techniques [15].

AI is emerging as a very helpful tool in optimizing healthcare, adding to the efficiency of a doctor [15]. However, sufficient data are required to determine the validity of their use before they can be routinely utilized in clinical practice [16]. In this literature review, we aim to elucidate the application of AI in detecting risk factors, reducing the occurrence of VVs, and enhancing the management of VVs.

2. Background

2.1. Pathogenesis and Risk Factors

To understand the pathophysiology of VVs, we must begin with a brief overview of the anatomy and physiology of lower extremity veins. Anatomically, veins are dilated below the thorax [17], and the venous system in the lower extremity mainly consists of superficial, deep, and perforator veins [18]. Deep veins carry venous blood towards the heart, and superficial veins carry venous blood from the skin and other superficial tissues to the deep veins through perforator veins [19]. All of these veins have functional, competent, one-way valves, and the blood flows from superficial to deep veins through perforator veins. Perforator veins carry venous blood only in one direction, which is from superficial to deep [20]. These veins carry blood from the lower limb cranially against gravity towards the heart as the muscle acts as a pump, making it go centripetally. The venous blood does not “fall back” due to the presence of valves.

Peripheral and central venous pressures largely depend on the right atrial pressure [21]. Large veins are often assumed to offer no resistance while moving blood, but in reality, there is a little resistance present when the blood flows through compressed portions of the veins at various parts of the body. Thus, the venous pressure in these large veins is slightly higher than that of the right atrium. If the pressures in the right atrium or the large veins increase due to any reason, the peripheral veins must push blood with that much force in order to get the blood flowing. This explains varicosities due to pregnancy, malignancy in the abdomen, obesity, and liver dysfunction causing ascites. Venous pressure in a standing adult is about 80–90 mm Hg, and while ambulating with intact veins, it is less than 30 mm Hg [22]. This occurs because the muscles surrounding the veins “pump” this blood towards the heart [23]. Catheters are introduced inside the veins in order to measure their value.

VVs develop when these valves are rendered incompetent due to various factors. The increase in venous pressure in the veins leads to the stretching of the veins, but the valves do not increase in size. This leads to incompetent valves that do not close properly, causing the blood to “fall back”. VVs are tortuous, dilated, and can be seen superficially. Vein walls were also found to be one of the causes of the development of VVs [24]. Vein walls are found to be dilated, which may have been caused by an altered normal ratio and function of the extracellular matrix of the smooth muscles of the veins [25].

2.1.1. Pathophysiology

The pathophysiology of VVs is multifactorial and is still being extensively studied [25]. Some of the common pathophysiology associated with the development of VVs are venous hypertension, venous insufficiency, vein wall structural changes, vein wall stress and genetic factors, and these are explained briefly in Figure 1 [1].

• Venous hypertension can arise from valvular incompetence, flow obstruction, or dysfunction of the calf muscle pump [26]. Valvular incompetence, particularly, results from the tearing, thinning, deformation, or adhesion of valve leaflets, which ultimately leads to venous reflux and increased pressure.
• Structural changes in the vein wall further contribute to venous dilation and weakening. Research indicates that reduced vein wall elasticity is a critical factor in VV formation, with significant differences observed between normal and VV [27].
• Molecular changes also play a role in vein wall degradation. Overproduction of collagen type I and decreased synthesis of collagen type III, along with disorganized smooth muscle cells and elastin fibers, have been linked to structural weakening [28,29]. Elevated levels of transforming growth factor β1 (TGF-β1) and fibroblast growth factor β1 (FGF-β1) further contribute to extracellular matrix remodeling, leading to vein wall dysfunction.
• Prolonged exposure of venous valves to elevated pressures has been associated with structural remodeling, including reductions in leaflet length and thickness, which further impairs valve function [30].

Additionally, the presence of turbulent flow, reversal of flow, and reductions in shear stress can induce inflammatory and prothrombotic alterations, which may exacerbate the damage to the vein wall and valve leaflets [1]. Inhibition of programmed cell death in adventitia cells, along with varied cyclin D1 expression, suggests these contribute to venous wall weakening. Further research is necessary to unravel the role of programmed cell death in VV formation [31]. Another study compared purinergic signaling pathways in VVs and normal veins. It showed reduced contraction in VVs in response to purinergic molecules [32].

A study showed the possible pathogenesis of hypoxia and the HIF pathway in VVs. In comparison with normal veins, VVs have been found to have increased levels of HIF-1 and HIF-2 when exposed to hypoxia, which are the target genes in VVs compared to non-VVs [33]. Some reviews concluded that in VVs, matrix metalloproteinases other than MMP-1, like MMP-2, MMP-3, MMP-7, and MMP-9, and tissue inhibitors of metalloproteinases, TIMP-1 and TIMP-3, are upregulated [34,35]. The emerging role of MMP-2 in the initial phases of venous dilation and VV formation is studied. This involvement is proposed to stem from MMP-2-triggered smooth muscle hyperpolarization resulting in relaxation in IVC [36].

2.1.2. Risk Factors

Risk factors for the development of VVs slightly differ in males and females. It is generally believed that prolonged standing and pregnancy increase the risk of VVs; some studies found contrasting evidence of the same [37]. While there is a genetic predisposition in both sexes, men who are tall and have a lower educational level are found to have a significant risk. Tall and obese women were found to be at greater risk in a study conducted in Edinburgh [38]. In both sexes, family history of VVs in first-degree relatives and age are found to be two of the most important risk factors in the French population [39].

• Age is widely recognized as a key determinant in the development of VVs [40]. The incidence of VVs increases with age, mainly due to the weakening of lower leg muscles and impaired venous valve function [41]. A study by Sisto et al. reported the prevalence of VVs as 25% in women and 7% in men [42], with the Framingham study and others confirming that VVs are more common in women aged 40–79. However, some studies suggest no significant sex-based prevalence differences [3,43].
• Physiological changes during pregnancy contribute to venous distension and VVs, as pregnancy is associated with increased blood volume [44,45].
• Occupational factors are also significant. A case–control study by Elamrawy et al. found that frequent heavy lifting, standing for more than four hours per day, and insufficient sleep increased the risk of VVs [46]. Professions involving prolonged standing, such as teaching, retail, manual labor, and healthcare (especially nursing), are linked to a higher prevalence of VVs [3].

A population-based study was conducted in Finland by Ahti et al., which revealed that a positive family history correlated to a 1.6 times risk for developing VVs, although it has not been more commonly discussed in many articles [47].

Another less-known risk factor noted was the Western diet, which can commonly cause constipation and result in intra-abdominal pressure which contributes to the etiology of VVs [48,49,50].

VVs can develop in any part of the lower limb venous system; however, it was found that below the knee, the greater saphenous vein occurs more commonly in patients with VVs [51].

Figure 1 illustrates the interrelated roles in VV development, showing how venous insufficiency, hypertension, structural changes, wall stress, and genetic predisposition collectively contribute to the disease’s pathogenesis. This structured layout emphasizes the complex interplay of physiological, structural, and genetic elements in the formation of varicose veins.

2.2. Current Technologies in Diagnosing VVs

In the first step of diagnosis, a thorough medical history is taken, followed by a thorough physical examination using advanced diagnostic methods. Duplex and Doppler ultrasounds provide more precise anatomical and functional assessments of the deep vein system [52].

There are some quick, non-invasive, easy, cost-effective functional tests of the venous system that, although less commonly used, can still be used to diagnose VVs. Some of those functional tests are the Schwartz test, the Linton test, the Trendelenburg test and the Pratt test [53].

The internationally recognized CEAP (Clinical, Etiological, Anatomical, and Pathophysiological) classification was established for the purpose of describing chronic venous disorders [54].

The objectives prior to starting treatment for VVs are ruling out peripheral arterial disease using medical records, an inspection Doppler or duplex ultrasound, the exclusion of acute thrombotic even using ultrasound, and the exclusion of infection [55].

2.2.1. Venous Duplex Ultrasonography

The best method for identifying venous incompetence sites is duplex ultrasound screening, which is increasingly performed during first expert evaluations [56]. In order to achieve a more physiological response, researchers have explored changing Manual calf compression (MCC) with standardized compression pumps, standing on tiptoes, or inducing leg muscular contraction by pressing the patient, to increase blood flow up the veins, and Doppler ultrasound is utilized to track the blood flow [57]. The only investigation that is typically needed for leg veins, as well as perforator veins, is venous Doppler ultrasound [58].

Depending on the disease’s severity, Doppler sonography is frequently used in the diagnostic assessment of venous diseases. Venous leg ulcers (VLUs) are common (1–2%), recur (30–45%) within a year, and are a severe socioeconomic burden. One of the studies compares traditional measurements made by trained wound nurses to the dependability of a handheld three-dimensional infrared wound imaging device based on machine learning (WoundAide [WA] imaging system, Konica Minolta Inc., Tokyo, Japan). For the WA imaging systems, good intra- and inter-rater reliability was attained. The WA imaging system is a helpful clinical adjunct in the oversight of VLU wound documentation [59].

2.2.2. Plethysmography

Plethysmography is used in the current care of venous diseases to help manage complex venous diseases when many anatomic abnormalities are found and to verify improvements in venous function after therapies [60]. It can detect reflux, blockage, and muscle pump dysfunction, among other probable pathophysiologic processes of chronic venous insufficiency components [61]. Ejection fraction, segmental venous capacitance, maximum venous outflow, venous volume, venous refilling times, and maximum venous outflow can all be calculated [62]. Its parameters enable a quantitative assessment of the degree of chronic venous insufficiency. The venous filling index assesses the total degree of venous valve insufficiency [63].

2.2.3. Light Reflection Rheography

By observing changes in the dermal blood content of the lower limb during exercise, light reflection rheography (LRR), is a quick, easy, and non-invasive test of venous function. By analyzing changes in dermal blood content during movement, LRR replicates venous pressure dynamics and offers a reliable alternative for preoperative and postoperative venous function evaluation [64].

2.2.4. CT and MRI

CT and MRI imaging are rarely necessary to diagnose the source of CVI and plan treatment. These approaches are especially useful for assessing intrinsic or extrinsic blockage and evaluating isolated or complex lesions found in proximal veins and their surrounding structures [62].

The combined use of CT and ultrasonography can serve as a noninvasive method for the diagnosis of unusual lower extremity VVs arising from the pelvis and vulvoperineal region. Venous reflux from the pelvis and vulvoperineal region can manifest even without evidence of pelvic congestion syndrome or ovarian vein dilatation [65]. For the majority of patients, CT venography proved useful in planning the procedure. A great road map for VV surgery can be obtained using CT venography with few risks. It can deliver potent 3D images for designing operations, education, and research. Although Duplex ultrasonography cannot be replaced by this technology [66]. Computed tomography venography (CTV) is accurate for the diagnosis and severity evaluation of IVCS and the recurrence of VV. IVCS might be a contributing factor to VV recurrence [67].

2.3. Current Treatment and AI in Enhancing the Management of VVs

There exists a range of therapeutic options for the treatment of VVs, encompassing both conservative approaches and surgical treatments [68].

2.3.1. Conservative Management

Conservative measures include stockings, leg elevation, weight loss, and medications [69]. Compression stockings are considered to be the initial line of treatment [70]. Medications known to be used are horse chestnut seed extract and micronized pure flavonoid fraction [71]. Horse chestnut seed extract decreases platelet aggregation, edema, capillary permeability, and lymphatic drainage [72]. Lipoxygenase, cyclo-oxygenase, and phosphodiesterase inhibition may work. Whereas micronized pure flavonoid fraction (diosmin) may reduce discomfort and heaviness and enhance compression therapy. Micronized pure flavonoid fraction can heal ulcers and relieve discomfort and heaviness by affecting the functions of leukocytes and reducing inflammation [73].

2.3.2. Invasive Treatment Option

Invasive treatment strategies are tailored to the patient’s symptoms, vein characteristics, and etiology. These include endovenous thermal ablation, sclerotherapy, and surgical ligation [73].

Table 1. Various treatment options for VVs, their mechanisms, indications, and comparing efficacies.

Therapy	Mechanism	Indication	Overall Results	Comparative Results
Conservative treatment: Stockings/lifestyle modifications/medications		Relief from pain, edema and pressure	Temporary	Surgery vs. conservative Management: Surgery enhanced quality-adjusted life-years and symptoms
Sclerotherapy	A foreign substance is injected into a vascular lumen, causing thrombosis and fibrosis. Endofibrosis then ensues, which finally causes the vein to be ablated [74].	Spider veins and small VV	60–70% Cosmetic improvement	Foam sclerotherapy vs. endovenous Ablation: Foam sclerotherapy can cause neurologic or retinal complications.
Thermocoagulation/Laser	An extremely small needle put into the target vessel transmits heat from a radio frequency pulse resulting in endothelial damage and hence occluded [76].	Spider veins and small VV	60–70% Cosmetic improvement
Endovenous ablation: Radiofrequency ablation	Heats the vein wall, denaturing the collagen fibers, and causing constriction of the artery wall rather than thrombosis. The collagen in the venous wall contracts as a result, causing vein shrinkage or blockage [75].	Ablation of GSV or SSV	70–90% Durable GSV occlusion	Surgery vs. radiofrequency ablation: Radiofrequency ablation reduces pain, speeds up recovery, and improves short-term quality of life. Radiofrequency ablation vs. endovenous laser therapy: Radiofrequency ablation reduces bruising and tenderness.
Endovenous ablation: Endovenous laser therapy	ELA causes localized tissue injury and transfers thermal energy into the blood and venous wall, encouraging vein collapse [75].	Ablation of GSV or SSV	70–90% Durable GSV occlusion	Surgery vs. endovenous laser therapy: Endovenous laser therapy showed faster recovery and shorter postoperative impairment.
Stripping and excision	A stripper can be used to remove a non-tortuous part of the VV from proximal to distal incisions or vice versa [77].	Removal of GSV or SSV (axial reflux) and excision of branch VV	80% Intermediate term
Microphlebectomy	Specialized hooks and clamps remove superficial VV by many tiny “stab” incisions [78].	Removal of branch VV alone or after endovenous ablation	90% Intermediate term

discusses the various treatment options available for managing VVs and compares the overall efficacy of each mode of treatment [73,74,75].

3. Primary Studies on AI in Varicose Veins

Our paper elucidates the multifaceted role of AI in enhancing VV management. It details the pathogenesis and risk factors of VVs, emphasizing venous hypertension, valvular incompetence, and structural vein wall changes, alongside risk factors like age, gender, obesity, and genetic predisposition. AI applications are highlighted across several domains: machine learning models identify biomarkers (e.g., WISP2, CRIP1, OSR1) and novel genetic loci, improving risk factor detection; deep learning algorithms achieve high diagnostic accuracy (e.g., 94% sensitivity, 93% specificity in ultrasound analysis) and tissue classification (99.55% accuracy via multidimensional CNNs); and artificial neural networks predict venous leg ulcer healing times (68% accuracy) and recurrence risks. AI also enhances treatment planning through surgical navigation and intraoperative imaging. However, challenges such as limited large-scale studies, data biases, and ethical concerns hinder widespread adoption, underscoring the need for further validation and regulatory frameworks.

3.1. Role of AI in Risk Factor Identification and Recurrence Estimation

AI has emerged as a key pillar in science and engineering, enabling machines to replicate human reasoning, process vast datasets, and enhance decision-making. In medicine, AI is being integrated into diagnostic and prognostic models, revolutionizing routine clinical practice [79].

AI has been shown to be effective in personalizing treatment options for every patient. Taylor R. J. et al. carried out a retrospective cohort study using an artificial neural network (ANN) to predict the healing time in patients with venous leg ulcers. They aimed to identify risk factors like previous venous leg ulcers, high BMI, and male sex that would help in predicting healing time. This study assessed data collected prospectively from 325 patients with 345 venous leg ulcers at the Salford Primary Care Trust Leg Ulcer Clinic using the ANN technique, which accurately predicted healing time in 68% of patients. Furthermore, the Artificial neuronal network technique was able to predict presentation ulcers that could be resistant to standard treatment therapy [2].

AI algorithms can be employed for image recognition and analysis to aid in surgical planning, navigation, and intraoperative imaging [80]. The application of all four subfields of AI has been observed in the healthcare sector, including their utilization in surgical procedures. Vascular surgery heavily relies on diagnostic imaging and extensive patient data [81]. The capacity of AI to analyze data, identify patterns, and make inferences exceeds that of humans, and has already demonstrated its advantages in various aspects of patient care and outcomes, including vascular diagnostics, perioperative medicine, risk assessment, and outcome estimation [82].

3.2. Role of AI in Risk Factor Identification and Recurrence Estimation

3.2.1. Machine Learning in Risk Factor Identification

Machine learning, a subset of AI, enables computers to detect patterns in vast datasets and optimize task completion [83]. Wang et al. demonstrated the application of machine learning in identifying biomarkers associated with VVs [82]. Their study utilized transcriptome data analysis to explore DNA methylation mechanisms in VVs and applied three machine learning models—random forest (RF), support vector machine (SVM), and generalized linear model (GLM)—to identify three key biomarkers:

• WNT1-inducible-signaling pathway protein 2 (WISP2);
• Cysteine Rich Protein 1 (CRIP1);
• Odd-Skipped Related Transcription Factor 1 (OSR1).

This study marked the first attempt to elucidate the molecular mechanisms underlying VVs through machine learning [83].

• Fukaya E. et al. conducted a community-based study on approximately 500,000 individuals aged 40 to 69 from the United Kingdom (UK) Biobank [83]. The application of machine learning facilitated the confirmation of established risk factors such as age, sex, obesity, pregnancy, and history of deep vein thrombosis for VVs. Additionally, the utilization of this approach uncovered novel risk factors, including height. The application of machine learning techniques in genome-wide association studies (GWAS) has facilitated the identification of 30 novel genetic loci that are associated with the occurrence of VVs;
• Blood pressure regulation;
• Vascular mechanosensing channels;
• Vascular maturation and development;
• Structural integrity of veins;
• Genes located near the hemochromatosis gene [84].

Another study conducted by Seddik EH. et al. introduced the idea of using fuzzy modeling in assessing risk factors and the anticipated output on the vein state and anatomy [85].

3.2.2. AI in Recurrence Estimation

• Multiple factors are also involved in the recurrence of VVs, but they are difficult to identify.
• Age and gender;
• Obesity;
• Genetic predisposition;
• Inadequate preoperative assessment;
• Presence of double short or long saphenous veins;
• Neovascularization;
• Surgical incompetence;
• Length of the recurrence period [86].

A study conducted by Bouharati, I., et al. on 62 patients operated on for VVs at the Setif University Hospital in Algeria from January 2016 to September 2017 implemented AI in estimating the recurrence risk of VVs after invasive procedures. They utilized artificial neural network (ANN) systems, which would take variables like probable causes of recurrence as input and recurrence rate as output. The study was not able to publish its results. However, it was suggested that this technique is capable of handling a large quantity of complex data, and its implementation in the analysis could prove to be very efficient in reducing the recurrence of VVs [85].

3.3. Role of AI in Diagnosing VVs

For effective treatment of VVs, an accurate diagnosis is crucial. Traditional diagnostic methods for VVs frequently rely on subjective and inter-observer variability in physical examination and medical history [86]. However, the use of AI in diagnosing VVs has shown remarkable promise in producing objective and reliable diagnostic results.

The field of AI has made notable advancements in the domain of medical image analysis, particularly in the identification and categorization of VVs. AI algorithms, specifically those utilizing deep learning methodologies, have exhibited a notable level of precision in autonomously identifying and differentiating VVs from healthy veins [16].

Liu et al. conducted a study wherein they devised an AI algorithm based on deep learning techniques [86]. This algorithm was trained using a substantial collection of ultrasound images. The algorithm demonstrated a sensitivity rate of 94% and a specificity rate of 93% in its automated detection of VVs, surpassing the proficiency of human experts. The algorithm’s capacity to effectively detect VVs exemplifies the potential of AI as a dependable diagnostic instrument [87].

Image processing and segmentation are proving to be necessary tools in the early diagnosis of various diseases. Fuzzy C-means have utilized the ability of pattern recognition to analyze images of VV. A study conducted by Mirunalini S. et. al. using Fuzzy C-means with images from MRI for image processing, extraction and pattern recognition has resulted in increased accuracy and degree of identification of VVs [88].

Bhavani R. et al. used a Multidimensional convolutional neural network (CNN) to classify varicose venous ulcers [88]. They classified images using a convolutional neural network and a recurrent neural network. This study aims to better diagnose and treat the wound based on tissue classifications like granulation tissue, slough tissue, necrotic tissue, and epithelial tissue. Various steps are used, which include preprocessing of images (by removing the flashlight), active contour segmentation, and multidimensional CNN. Their dataset used approximately 1250 images and has two parts—training set (65%) and the validation set (35%) images for tissue classification. Finally, it is found that Multidimensional CNN has an accuracy of 99.55, a specificity of 98.06, and a sensitivity of 95.66 [88].

4. Discussion

Our paper offers a comprehensive, VV-specific review of AI applications, distinguishing itself from related reviews like Butova et al. (2021) by its focused scope, detailed pathophysiology, and quantitative evidence (e.g., 94% sensitivity in ultrasound diagnostics, 99.55% accuracy in tissue classification) [16]. It covers risk factor identification, diagnosis, treatment planning, and recurrence estimation using techniques like machine learning, deep learning, and artificial neural networks, supported by structured tables and recent studies (2019–2022). Unlike broader vascular reviews, such as Butova et al., which address multiple conditions with less VV specificity, this paper provides actionable insights, emphasizes ethical considerations like patient data privacy, and calls for large-scale validation, making it a pivotal resource for VV researchers and clinicians [16].

VVs of the lower extremities are a common condition that affects a significant portion of the global population [3,4]. The use of AI in the management of VVs has the potential to significantly improve healthcare outcomes and optimize treatment. In this review, we reviewed the current understanding of the pathogenesis, risk factors, diagnostic techniques, and treatment options for VVs, as well as the role of AI in enhancing the identification and treatment of this condition.

The development of VVs is influenced by multiple factors, including venous hypertension, venous insufficiency, structural changes in the vein walls, vein wall stress, and genetic predisposition [1]. A thorough understanding of these mechanisms is crucial for developing effective treatment strategies.

AI plays a pivotal role in analyzing large datasets to detect patterns and correlations that might elude human observation. For example, machine learning techniques have been utilized to identify biomarkers associated with VVs and to predict the healing time of venous leg ulcers, providing valuable insights into disease progression and patient outcomes [2]. These approaches have the potential to improve risk assessment and personalize treatment plans for patients.

Precise diagnosis is essential for effective management of VVs. AI algorithms, particularly those based on deep learning techniques, have shown promise in autonomously identifying and categorizing VVs from medical images such as ultrasound scans and MRI [88]. These algorithms have exhibited high sensitivity and specificity rates. The Utilization of AI in diagnostic imaging can provide objective and reliable results, leading to more precise treatment decisions. Figure 2 illustrates the integration of AI with traditional diagnostic methods for VVs and varicose venous ulcers, highlighting enhanced accuracy and efficiency. It depicts conventional tools like plethysmography (detecting reflux and blockages), ultrasound (identifying venous incompetence), and CT/MRI (assessing blockages and lesions), all linked to a diagram of varicose veins in a leg. AI techniques, such as Fuzzy C for image processing and pattern recognition and deep learning for training on large datasets like ultrasound images, are shown to improve these methods, leading to “increased accuracy & degree of identification of varicose veins”.

4.1. CN—Convolutional Neural Network

In addition to diagnosis, AI has the potential to enhance treatment strategies for VVs. By analyzing patient data and medical images, AI can assist in surgical planning, navigation, and intraoperative imaging [83]. It can provide valuable insights to surgeons, improving precision and outcomes. Moreover, AI can aid in predicting the recurrence risk of VVs after invasive procedures, allowing for targeted interventions and preventive measures [12]. This can significantly reduce the burden of recurrent VVs and optimize long-term patient outcomes.

Despite the promising potential of AI in the management of VVs, there are challenges that need to be addressed. AI algorithms can only be as accurate as the data they are trained on, and biases in the data or limits in the algorithms can result in mistakes or incorrect diagnoses. To optimize the precision and dependability of AI models, it is essential to ensure thorough data gathering, validation, and continual model improvement.

One of the main challenges is the availability of sufficient data to validate the efficacy and validity of AI algorithms in real-world clinical settings. Large-scale studies and continued research are necessary to unlock the full potential of AI in VV management. Despite the fact that our research may use AI to predict or prevent the recurrence of VVs, the study did not specifically address how our findings would affect current patient treatment. AI has the potential to significantly improve the treatment of VVs with further research and development.

Additionally, there are ethical considerations and concerns regarding the integration of AI into clinical practice. The development of robust regulatory frameworks and guidelines is essential to ensure patient safety and maintaining the trust of healthcare providers.

Table 2. Identifying risk factors and recurrence risk estimation of VVs.

Risk Factor Identification and Recurrence Estimation of Varicose Veins
AI Method	Process/Method	Results
Machine Learning [83] random forest model (RF) support vector machine model (SVM) generalized linear model (GLM)	Explore biomarkers associated with VVs Biomarkers were then screened using the 3 machine learning techniques. Gene set enrichment analysis (GSEA) was performed to explore the functions of biomarkers. Verified their mRNA expression using quantitative real-time polymerase chain reaction (qRT-PCR).	Identified genetic biomarkers of VVs, namely WNT1-inducible-signaling pathway protein 2 (WISP2), Cysteine Rich Protein 1 (CRIP1), and Odd-Skipped Related Transcription Factor 1 (OSR1).
Fuzzy modeling [85]	Assessing risk factors and the anticipated output on the veins.	Identified risk factors such as age, sex, obesity, pregnancy, history of deep vein thrombosis and height were associated with VVs.
Artificial Neural Network (ANN) systems [86]	Input is variables like probable causes of recurrence and output is recurrence rate.	Does not have published results yet.

,

Table 3. AI methods used to diagnose VVs.

Diagnosis/Detection of Varicose Veins
AI Method	Process/Method	Results
Deep Learning Techniques [87]	Algorithm was trained using a substantial collection of ultrasound images to diagnose VVs more effectively.	Sensitivity rate of 94% and a specificity rate of 93% in its automated detection of VVs.
Fuzzy C [88]	Fuzzy C means images from MRI for image processing, extraction and pattern recognition	Increased accuracy and degree of identification of VVs.
Multidimensional convolutional neural network (CNN) [88]	Pre-processing of images (by removing the flashlight), active contour segmentation, and multidimensional CNN. Their dataset used images and has two parts—training set (65%) and the validation set (35%) images for tissue classification.	Accuracy of 99.55, a specificity of 98.06, and a sensitivity of 95.66. Helps in classifying the wound and plan management accordingly.

and

Table 4. AI method used in management of VVs.

Management of VVs
AI Method	Process/Method	Results
Artificial Neural Network [86]	Identify risk factors like previous venous leg ulcers, high BMI, and male sex that would help in predicting healing time.	Accurately predicted healing time in 68% of patients.

summarize all the ways in which AI has been used in the detection, diagnosis and management of VVs.

4.2. Current Challenges

The adoption of AI in VV management faces several significant challenges that must be addressed to achieve widespread clinical integration. A primary obstacle is the lack of large-scale, multicenter clinical studies to validate AI algorithms, limiting their generalizability across diverse patient populations and clinical settings [16]. Many AI models, such as those for ultrasound-based diagnosis or recurrence estimation, rely on datasets that may suffer from biases or incomplete representation of demographic and clinical variability, potentially leading to inaccurate predictions or misdiagnoses [5]. Data quality and availability further complicate progress, as standardized, comprehensive VV datasets integrating genetic, imaging, and clinical information are scarce, hindering the development of robust machine learning and deep learning models. Ethical concerns, including patient data privacy, informed consent, and the risk of algorithmic bias exacerbating healthcare disparities, remain inadequately addressed, necessitating robust regulatory frameworks to ensure trust and safety [13]. Practical implementation barriers, such as the need for clinician training, high infrastructure costs, and interoperability with existing healthcare systems, also impede seamless adoption. Additionally, the variability in AI algorithm performance, as seen in studies with unpublished results or limited quantitative synthesis, underscores the need for rigorous validation and transparency [86]. These challenges collectively highlight the gap between AI’s potential and its current application in VV management, requiring collaborative efforts to overcome technical, ethical, and logistical hurdles.

4.3. Limitations

Our study has several limitations that warrant consideration. This review does not follow a systematic review methodology. As a result, it cannot ensure comprehensive inclusion and analysis of all relevant studies. The reliance on the recent (2019–2022) but not exhaustive literature, coupled with unpublished results from some studies (e.g., Bouharati et al., 2018) [85], weakens the evidential strength of certain claims. Additionally, practical implementation barriers, such as clinician training and infrastructure costs, are underexplored, and the discussion of ethical concerns, like patient data privacy and algorithmic bias, lacks depth.

4.4. Future Perspective

The integration of AI into VV management holds transformative potential, yet several avenues require exploration to fully realize its clinical impact. Future research should prioritize large-scale, multicenter clinical trials to validate AI algorithms across diverse patient populations, addressing current limitations in generalizability and data bias. Developing standardized datasets with comprehensive VV patient profiles, including genetic, imaging, and clinical data, will enhance the robustness of machine learning and deep learning models, improving diagnostic accuracy and treatment personalization. Advancements in real-time AI-driven imaging tools, such as automated ultrasound analysis or augmented reality for surgical navigation, could streamline intraoperative decision-making and reduce recurrence rates. Additionally, integrating AI with wearable devices and telehealth platforms may enable continuous monitoring of VV progression and early risk detection in high-risk individuals, such as those with genetic predispositions or occupational exposures. Ethical frameworks and regulatory guidelines must evolve to address patient data privacy, algorithmic transparency, and equitable access, ensuring trust and safety in clinical adoption. Collaborative efforts between clinicians, data scientists, and policymakers are essential to bridge implementation gaps, including clinician training and infrastructure development, to seamlessly incorporate AI into routine VV care. These advancements promise to reduce disease burden, optimize healthcare efficiency, and improve patient outcomes in VV management.

5. Conclusions

AI holds transformative potential to revolutionize VV management by enhancing risk factor identification, diagnostic accuracy, treatment personalization, and recurrence prediction. This review demonstrates AI’s efficacy in leveraging machine learning to identify biomarkers (e.g., WISP2, CRIP1, OSR1), deep learning for high-precision ultrasound diagnostics (94% sensitivity, 93% specificity), and neural networks for predicting venous leg ulcer healing (68% accuracy) and recurrence risks. By analyzing extensive patient data, AI enables objective, personalized approaches that promise to reduce disease burden and improve clinical outcomes. However, challenges such as limited large-scale validation, data biases, ethical concerns, and implementation barriers hinder widespread adoption. Future research must prioritize robust clinical trials, standardized datasets, and ethical frameworks to ensure reliability, transparency, and equitable access. Collaborative efforts among clinicians, data scientists, and policymakers are essential to integrate AI seamlessly into VV care, ultimately optimizing healthcare efficiency and patient well-being. With continued advancement, AI can redefine VV management, offering precise, safe, and effective solutions to a prevalent global health challenge.