Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 12, 2025Open Access

Clinically Scalable Deep Learning for Stroke Multicenter Validation of an Ultra-Efficient NCCT-Based Diagnostic Framework

View Full Paper
Ask AI
Bookmark
Share

Authors

NSNattavut Sriwiboon

Discussion

Loading...

Member takes

Overview

This multicenter evaluation demonstrates 97.2% accuracy in stroke assessment, suggesting deep learning might enhance clinical workflows.

Key Points

  • The model achieved 97.2% accuracy in detecting cerebral ischemia, addressing a critical clinical need for rapid assessment.
  • With 1,200 scans evaluated, the strong performance includes 97.8% sensitivity and an AUC of 0.984 for classification.
  • Utilizing a multitask approach, the model integrates classification and segmentation with only ~1.8 million parameters.
  • Its computational efficiency, averaging 0.92 seconds per scan, may enable real-time integration into clinical settings.

Cite This Study

Nattavut Sriwiboon (2025) studied this question.

synapsesocial.com/papers/68d41dae713b0b5dfea671fchttps://doi.org/10.21203/rs.3.rs-7240302/v1
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Automated ischemic stroke lesion detection on non-contrast brain CT: a large-scale clinical feasibility test AI stroke lesion detection on NCCT2025 · 3 citations
  2. 2An Efficient Deep Learning Framework for Brain Stroke Diagnosis Using Computed Tomography (CT) Images2025
  3. 3Advanced Deep Learning for Stroke Classification Using Multi-Slice CT Image Analysis2025 · 5 citations
  4. 4A Two Step Deep Learning Framework for Identifying Ischemic Stroke Core: Integration of Inception-v3 and MultiResU-Net on DWI and ADC MRI Images2025 · 1 citations
  5. 5A Deep Learning Model to Detect Acute MCA Occlusion on High Resolution Non-Contrast Head CT.2025