CT Scan AI: Emergency & Routine Analysis

3D convolutional neural networks for CT interpretation. Detects intracranial bleeding, pulmonary embolism, and critical findings in minutes. Automated organ segmentation for quantitative analysis. Backed by peer-reviewed research.

3B+
Training Images
Powering CT analysis
0.93
F1 Score
Across pathologies
30 min
Avg TAT
Routine CT reports
< 15 min
Critical TAT
Emergency findings

Critical Finding Escalation

AI automatically flags critical findings (intracranial hemorrhage, PE, aortic dissection) for immediate radiologist review. Under 15-minute turnaround for emergencies with direct physician notification.

How CT AI Works

1

3D Volume Processing

Unlike 2D slice-by-slice analysis, 3D CNNs process the entire CT volume, capturing relationships between adjacent slices for better detection of subtle findings.

2

Priority Classification

AI classifies studies by urgency: STAT (critical findings), Urgent (significant abnormalities), and Routine. Critical cases jump the queue automatically.

3

Automated Segmentation

Organs, vessels, and structures are automatically segmented for precise measurements. Lesion volumes, organ sizes, and anatomical landmarks identified.

4

Specialist Review

CT studies are routed to appropriate subspecialists - neuroradiologists for head CT, body imagers for abdomen/pelvis, emergency radiologists for trauma.

CT Analysis Capabilities

AI-assisted analysis across all CT body regions:

Head CT

Critical pathway
  • Intracranial hemorrhage
  • Stroke/infarct
  • Skull fractures
  • Masses/tumors
  • Hydrocephalus
  • Sinusitis

Chest CT

Critical pathway
  • Pulmonary embolism
  • Lung nodules
  • Pneumonia/COVID
  • Aortic dissection
  • Pleural effusion
  • Mediastinal masses

Abdominal CT

  • Appendicitis
  • Bowel obstruction
  • Liver lesions
  • Kidney stones
  • Pancreatitis
  • Aneurysm

MSK CT

  • Fractures
  • Dislocations
  • Bone tumors
  • Degenerative changes
  • Post-surgical
  • Trauma workup

Peer-Reviewed Research

Our CT AI is backed by published research on emergency detection and automated segmentation.

3D Convolutional Neural Networks for Intracranial Bleeding Detection in CT

arXiv:2503.20306

Advanced 3D CNNs for improved detection of intracranial hemorrhage in CT imaging, designed for emergency radiology applications.

Key Findings:

  • Rapid detection critical for patient outcomes
  • 3D architecture captures volumetric relationships
  • High sensitivity for subtle hemorrhages
Read Full Paper on arXiv

Deep Learning for Automated Segmentation of Abdominal Organs in CT

arXiv:2503.10717

Automated workflow for accurate segmentation and measurement of abdominal organs, supporting diagnostic workflows.

Key Findings:

  • Comprehensive organ analysis capabilities
  • Automated volumetric measurements
  • Supports treatment planning decisions
Read Full Paper on arXiv

Emergency vs Routine CT Workflow

Emergency CT

  • TAT: Under 15 minutes
  • Triggers: Intracranial hemorrhage, PE, aortic dissection, trauma
  • Alert: Direct phone/SMS to ordering physician
  • Review: Senior radiologist sign-off required

Routine CT

  • TAT: 30 minutes average
  • Studies: Follow-up scans, staging, chronic conditions
  • Alert: Report delivered to PACS/EMR
  • Review: QC agents + radiologist sign-off

Add AI to Your CT Workflow

3D CNNs for critical finding detection. Automated segmentation. Under 15 minutes for emergencies. Join 1,500+ facilities using 5C Network.