Overall Winner
The Integrated Medical Management and Educational Gateway (IMMEG) Venous Management System (I-VMS) is an AI-enabled, standards-based platform that projects a vectorized atlas of the deep thoracic venous system onto routine chest radiographs. Using deep-learning landmark detection and HL7 FHIR®/DICOM interoperability, the system lets clinicians visualize the catheter trajectory and tip position in patient-specific anatomy, record planned versus actual placement, and build a reusable, longitudinal venous access record across organizations. The project was developed for Vanguard with support from Xzyos.ai.
Clinical Problem and Context
Central venous access is essential for chemotherapy, parenteral nutrition, dialysis and critical care, yet malposition and related complications—venous injury, thrombosis, infection, and device dysfunction—remain common and costly. Post-procedure assessment usually relies on plain chest X-rays, which do not directly visualize venous structures. As a result, clinicians infer anatomy indirectly; documentation is inconsistent; and comparing procedures over time is difficult. There is no consolidated, spatially normalized record of a patient’s venous history to guide future decisions.
Core Innovation
I-VMS predicts anatomical landmarks (e.g., carina, first thoracic vertebra T1, lateral edge of the right rib) on a radiograph with a modified DenseNet121 model implemented in MONAI. These coordinates establish a patient-specific basis for an affine transformation that overlays a standardized, vector-based venous atlas onto the image. Clinicians can accept or adjust landmarks and annotate intended and actual entry and tip positions. Because annotations are stored in a normalized coordinate space, results are comparable across encounters and over time, enabling longitudinal analysis and population-level learning.