Research on Identifying Thrombosis in Human BodyBased on Artificial Intelligence

Artificial intelligence in the medical field is increasingly being used, particularly in the critical area of thrombosis diagnosis where rapid and accurate detection can be life-saving. This article explores the current development of AI in the field of thrombosis diagnosis through comprehensive analysis of three distinct applications: Left Atrial Appendage Thrombus (LAT) prediction using machine learning models, real-time detection of Intracranial Hemorrhage (ICH) and Pulmonary Embolism (PE) through integrated radiology workflows, and edge-computing based Deep Vein Thrombosis (DVT) risk assessment using portable devices. The review demonstrates that AI-powered diagnostic tools have achieved promising accuracy rates, with some models reaching over 90% accuracy in specific applications. These technologies show significant potential to reduce diagnostic delays, minimize invasive procedures, and improve clinical outcomes, particularly in resource-limited settings. The LAT prediction model achieved an AUC of 0.85 and could potentially reduce unnecessary invasive procedures by 40%. Real-time AI tools for ICH and PE detection demonstrated sensitivities of 84% and 73% respectively, with processing times of 3-9 minutes. Edge-computing approaches for DVT assessment achieved 90.4% accuracy on low-cost hardware, making advanced diagnostics accessible in remote locations. However, current limitations include sample size constraints, technical processing failures, and the need for continued human oversight. This analysis reveals both the transformative potential and current challenges of AI implementation in thrombosis diagnosis, providing insights into future development directions for these artificial intelligence machines and tools.