🟢 Interactive Demo · HCR-03⏱️ Try in ~2 min

Chest X-Ray AI Assistant

10-second lesion annotation + Lung-RADS grading + report draft.

👉 For
Radiologist / Primary-care physician
😖 Current pain
80 X-rays/day; fatigue causes early-stage nodules to be missed.
✨ With AI
10-sec annotation + grading + report draft · read speed +60% · detection +25%.
📜 Customer story

Dr. Henderson, the sole radiologist at a community hospital, reads 80 chest X-rays a day. She uploads a study — 10 seconds later, the AI highlights an 8mm right upper-lobe nodule with spiculated margins, grades it Lung-RADS 4A, and recommends low-dose CT follow-up.

🎮 Interactive demo

Real functional demo · auto-plays after case selection
🩻 Live Case · Lung Cancer Screening
62yo F · Chest X-ray PA · Annual screening · 30 pack-year smoking history
Margaret Henderson (simulated) · MRN BR2026-0892 · 2026-05-10 09:32
AUTO-PLAYING
→
→
→
→
→

⚙️ DICOM Preprocessing

🛠️ pydicom + medical image normalization

Loading DICOM metadata · windowing · de-noising

PA · Erect · 75 kVp2026-05-10MRN BR2026-0892
DICOM Metadata
ModalityCR (Chest Radiograph)
ViewPA · Erect
kVp75
mAs2.0
Image size2,048 × 2,500
Window/Level2500 / 800
ManufacturerGE Discovery
Image Quality Check
InspirationAdequate ✓
RotationNone ✓
ExposureOptimal ✓
ArtifactsNone ✓

⚙️ AI processing pipeline

Tech stack, concrete operations and processing time at each step

1

DICOM parse & pre-process

⏱ 0.6s
🛠️ Tech stack
pydicom + custom medical-imaging normalization
📋 Operation

Parses DICOM metadata (patient / study params); window-level standardization; deskew; artifact removal (motion blur, metal).

2

Anatomic structure recognition

⏱ 1.2s
🛠️ Tech stack
U-Net++ multi-task segmentation (custom medical model)
📋 Operation

Identifies bilateral lung fields, mediastinum, heart, diaphragm, ribs, clavicles; lung zoning (upper/middle/lower lobe) for lesion localization.

3

Multi-task lesion detection

⏱ 2.8s
🛠️ Tech stack
YOLOv8 + ConvNeXt + multi-head detector
📋 Operation

Simultaneously detects 14 lesion classes: nodule / consolidation / interstitial / pneumothorax / pleural effusion / cardiomegaly / pulmonary edema / atelectasis / etc.

4

Lesion measurement & localization

⏱ 0.9s
🛠️ Tech stack
Computer-vision measurement library + physical-pixel mapping
📋 Operation

Measures nodule size (mm), edge characteristics (smooth/lobulated/spiculated), density; localizes to lobe + segment.

5

Lung-RADS grading

⏱ 0.3s
🛠️ Tech stack
Rule engine + Lung-RADS 1.1 standard
📋 Operation

Auto-grades Lung-RADS 1-4X based on nodule size, density, stability; compares to prior study (if available) for change tracking.

6

Critical-finding alerting

⏱ 0.2s
🛠️ Tech stack
Rule engine + LLM explanation
📋 Operation

Identifies 5 critical findings: pneumothorax / large pleural effusion / pneumomediastinum / tension pneumothorax / acute pulmonary edema — real-time push.

7

Report draft generation

⏱ 4.5s
🛠️ Tech stack
GPT-4o + medical report templates + imaging KG
📋 Operation

Generates 4-section report: (1) Findings (2) Impression (3) Differential (4) Recommendation; compliant with hospital reporting standards.

💰 ROI across three dimensions

Itemized indicators across each dimension for a complete view

💰

Financial

Per-study read 8min → 3min
⏱️

Efficiency

Read speed +60% · early-stage detection +25%
🏢

Organizational

Community hospitals reach AMC-level diagnostic quality
role upgrade "image reader" → "AI reviewer + complex-case specialist"

🧩 AI capabilities used

A6Image / Video Understanding· PerceptionE4Decision Matrix / Scoring· ReasoningD1Long-form Text Generation· Generation

Want to deploy in your Healthcare?

Orion 4-12 weeks delivery · on-premise option · fully compliant

Calculate ROI →Delivery process