Examining Deployment and Refinement of the VIOLA-AI Intracranial Hemorrhage Model Using an Interactive NeoMedSys Platform
Journal:
arXiv
Published Date:
May 14, 2025
Abstract
Background: There are many challenges and opportunities in the clinical
deployment of AI tools in radiology. The current study describes a radiology
software platform called NeoMedSys that can enable efficient deployment and
refinements of AI models. We evaluated the feasibility and effectiveness of
running NeoMedSys for three months in real-world clinical settings and focused
on improvement performance of an in-house developed AI model (VIOLA-AI)
designed for intracranial hemorrhage (ICH) detection.
Methods: NeoMedSys integrates tools for deploying, testing, and optimizing AI
models with a web-based medical image viewer, annotation system, and
hospital-wide radiology information systems. A pragmatic investigation was
deployed using clinical cases of patients presenting to the largest Emergency
Department in Norway (site-1) with suspected traumatic brain injury (TBI) or
patients with suspected stroke (site-2). We assessed ICH classification
performance as VIOLA-AI encountered new data and underwent pre-planned model
retraining. Performance metrics included sensitivity, specificity, accuracy,
and the area under the receiver operating characteristic curve (AUC).
Results: NeoMedSys facilitated iterative improvements in the AI model,
significantly enhancing its diagnostic accuracy. Automated bleed detection and
segmentation were reviewed in near real-time to facilitate re-training
VIOLA-AI. The iterative refinement process yielded a marked improvement in
classification sensitivity, rising to 90.3% (from 79.2%), and specificity that
reached 89.3% (from 80.7%). The bleed detection ROC analysis for the entire
sample demonstrated a high area-under-the-curve (AUC) of 0.949 (from 0.873).
Model refinement stages were associated with notable gains, highlighting the
value of real-time radiologist feedback.
