LipoVena AI framework: A physician-centered artificial intelligence-assisted approach for longitudinal assessment in lipedema and chronic venous disorders.

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BackgroundChronic venous disorders and lipedema are complex and frequently underdiagnosed conditions characterized by heterogeneous clinical presentations, progressive tissue remodeling, and substantial variability in treatment response assessment. Despite advances in minimally invasive vascular interventions, longitudinal evaluation in routine clinical practice continues to rely heavily on subjective interpretation, non-standardized comparative imaging, and physician-dependent assessment pathways. The integration of multimodal vascular data into structured longitudinal clinical workflows remains limited.ObjectiveThis study introduces the <i>LipoVena AI Framework</i>, a physician-centered conceptual artificial intelligence-assisted clinical support architecture designed to facilitate multimodal integration, longitudinal treatment-response evaluation, and personalized monitoring in lipedema and chronic venous disease management.MethodsA retrospective observational clinical dataset consisting of 32 patients with chronic venous insufficiency, lipedema, reticular veins, spider veins, and mixed venous-lymphatic phenotypes was reviewed. Standardized standing-position photography, duplex ultrasound imaging, procedural documentation, and longitudinal 1-month follow-up assessments were integrated within a conceptual AI-assisted workflow. Representative patients underwent multimodal minimally invasive treatment strategies including endovenous laser therapy (EVLT), CLACS-guided sclerotherapy, radiofrequency-based tissue remodeling, and compression therapy. The proposed framework conceptually integrates multimodal imaging analysis, vascular mapping, comparative longitudinal assessment, and physician-supervised AI-assisted interpretation.ResultsRepresentative 1-month follow-up observations demonstrated visible improvements in contour harmonization, superficial venous prominence, tissue irregularity, skin texture uniformity, and vascular burden across multiple venous and lipedema phenotypes. Comparative multimodal analysis integrating clinical photography and duplex ultrasound findings enabled structured longitudinal assessment of therapeutic response patterns. Upper-extremity lipedema cases additionally demonstrated measurable contour and tissue-texture improvement following multimodal treatment strategies. The proposed framework illustrated the potential role of AI-assisted longitudinal comparison and multimodal integration in supporting objective treatment-response evaluation within physician-centered vascular care workflows.ConclusionThe LipoVena AI Framework presents a conceptual translational model for future AI-assisted vascular and lipedema medicine by integrating standardized clinical imaging, duplex ultrasound evaluation, multimodal treatment documentation, and longitudinal follow-up analysis within a physician-centered ecosystem. Although prospective multicenter validation and quantitative AI modeling remain necessary, the present framework highlights the potential of explainable and physician-supervised artificial intelligence systems to support personalized, data-driven, and longitudinally optimized vascular care.

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