SwiftDx turns fresh surgical tissue into a pathologist-familiar H&E slide and an AI margin report — without a cryostat, a stain, or a wait. From excision to readout in under five minutes.
H&E Concordance
Turnaround Reduction
Saved per Case
Gross Margin at Scale
Standard pathology turnaround runs 24–72 hours. SwiftDx targets under 5 minutes, right at the bedside.
Positive margins missed intraoperatively drive re-excision — extra trauma, extra cost, extra time under anaesthesia.
Most hospitals worldwide have no intraoperative pathology capability at all — no specialist on call, no equipment on hand.
Traditional histopathology requires fixation, sectioning, staining, and manual review before a margin can be verified.
Histopathologist expertise and cryostat lab infrastructure are simply unavailable in most operating environments.
We turn a three-day lab process into a five-minute bedside workflow — eliminating physical sectioning and staining with autofluorescence imaging, virtual staining, and AI-assisted analysis.
The surgeon provides a fresh tissue specimen immediately after excision — no fixation, no chemical prep required.
The tissue is illuminated inside a dark chamber under 280nm UV excitation. Surface fluorescence creates a virtual optical section roughly 2–3 μm deep.
A Pix2Pix / U-Net model converts the raw fluorescence capture into a pathologist-familiar H&E representation — no chemical staining required.
Raw capture
AI generated
A MONAI ResUnit + vision-language head analyses tissue morphology and flags suspicious regions with a confidence-scored margin report.
The generated slide and margin report are reviewed by a pathologist, who remains the final clinical decision maker — validated against the gold-standard H&E slide.
Virtual section depth from surface fluorescence.
Per-capture imaging exposure time.
No microtome or physical sectioning required.
Structured margin report ready for sign-off.
Results derived from internal testing and limited-scale comparative studies — not yet clinically validated.
H&E CONCORDANCE
SENSITIVITY
31.4M parameters · 180ms per patch · SSIM 0.90–0.92.
86M parameters · MONAI ResUnit backbone · >90% precision.
Margin label, confidence, evidence patches, heatmap, HL7/FHIR JSON.
Audit logs and locked validation built in from the start.
Growing roughly 3× in a decade at a ~17% CAGR, driven by AI, cloud imaging, and rising cancer burden. Total addressable market: $36.7B. SwiftDx 5-year target: $130M.
41% Regulatory · 28% Clinical Pilots · 21% Hardware & Ops · 10% Data Annotation & Model Improvement.
Six partner organisations already on board across network access, hospital pilots, and research.
Healthcare AI
AI Engineering
Epidemiology & AI
Clinical Adoption
Legal & Regulatory
Biology
Advisor · Public Health
Advisor · UCSI Group
SwiftDx combines virtual staining, digital pathology, reinforcement learning, and AI-assisted tumour analysis into a single surgical intelligence platform.