(untitled) (Asian Journal of Pharmaceutics (AJP), 2026)
Deep vein thrombosis (DVT) remains a significant contributor to global morbidity and mortality, particularly because of its association with life-threatening complications, such as pulmonary embolism and post-thrombotic syndrome. This review critically explores advancements in DVT diagnostic strategies, including traditional approaches, emerging imaging modalities, biomarker integration, and artificial intelligence (AI)-driven innovations. Conventional diagnostic pathways relying on clinical scores (e.g., Wells and Geneva), D-dimer assays, and duplex ultrasonography, though widely used, exhibit limitations in terms of sensitivity, specificity, and adaptability across special populations such as pregnant women and cancer patients. New imaging modalities, including magnetic resonance venography, computed tomography venography, intravascular ultrasound, elastography, and photoacoustic imaging, offer enhanced anatomical and functional insights, addressing gaps in thrombus age characterization and venous outflow obstruction. AI tools leveraging machine learning, natural language processing, and electronic health records are revolutionizing risk stratification, imaging interpretation, and decision support. These technologies aim to reduce diagnostic uncertainty, minimize unnecessary interventions, and enable personalized care for patients. Challenges persist in standardizing protocols, ensuring ethical AI deployment, and validating novel biomarkers, such as urinary proteomics and thrombin generation profiles. Special emphasis is placed on tailoring diagnostic algorithms for vulnerable subgroups and optimizing the timing of therapeutic interventions. This review highlights the clinical implications of these advancements and underscores the need for future translational research to bridge innovation and bedside applications.