Clinical Intelligence Platform — v2.5.0

Diagnose faster.
Treat sooner.

Omnia's AI instantly detects anomalies in clinical imaging, giving your care team the certainty to act fast.

99.68% Classification accuracy
99.6% Normal specificity
99.6% Pathology sensitivity
ARIA SCAN ENGINE — LIVE ANALYSIS LIVE
S3 — Right Lower Lobe
9mm nodule — Suspicious 87%
No consolidation 94%
Mediastinum unremarkable 91%
RISK LEVEL
HIGH
Recommended: Immediate specialist consultation
FLEISCHNER GUIDELINE
Category 4A — Surveillance
Based on 8mm nodule, age 62, smoker history

Precision at
every layer.

Sub-millimeter Nodule Detection

Deep convolutional networks identify pulmonary anomalies across routine CT scans earlier than conventional screening. ResNet-18 backbone trained on 1,000+ Kaggle CT scans.

99.4% Sensitivity — <1mm resolution

Grad-CAM Visualization

AI attention heatmaps overlay the scan, highlighting suspicious regions with adjustable opacity. Radiologists see exactly where the model is looking — building trust in the output.

3D Volumetric Heatmaps

Rotating lung model with lesion hotspot rendering. Volumetric attention mapping gives radiologists a spatial sense of where abnormalities sit — in three dimensions, not just slices.

Smart Worklist Prioritization

Patients auto-sorted by urgency, malignancy probability, lesion size, and AI confidence. STAT cases rise to the top with emergency banners. No manual triage.

Structured Report Generation

Aria drafts Findings, Impression, Recommendation, and Follow-up in proper radiology format. One click to approve or edit. Export to PDF or HL7/FHIR-compatible formats.

Nodule Tracking

Track lesions across visits over years. See growth timelines, size comparisons, and progression alerts for any nodule exceeding 2mm change.

Performance at clinical scale.

Benchmarked against 1,000+ Kaggle chest CT scans and LUNA16 expert-annotated nodule datasets.

99.68%
Classification Accuracy
3-class ResNet-18: Normal / Benign / Malignant. Validated on 315 held-out test cases.
99.6%
Normal Specificity (NPV)
Minimizes false-positive recall examinations. Clinical threshold ≥90% — exceeded.
99.6%
Pathology Sensitivity (PPV)
Minimizes missed pathology. Clinical threshold ≥95% — exceeded.
0.998
AUC-ROC (Macro-averaged)
Discriminative power across all classes. Clinical threshold ≥0.95 — exceeded.

Omnia AI is a computer-aided diagnostic assist tool. All AI-generated findings require verification by a board-certified radiologist before integration into patient care pathways.

Built for clinical environments.

01

Upload or Connect

Upload DICOM, CT, or X-ray directly — or connect to PACS via standard integration. Studies enter Omnia's queue automatically.

02

AI Analysis Runs

Aria analyzes images with ResNet-18 — classifying Normal / Benign / Malignant with Grad-CAM spatial attribution. Results in seconds.

03

Visualize & Review

Heatmap overlays and 3D volumetric views highlight suspicious regions. Radiologist reviews with full AI context visible alongside the scan.

04

Draft, Approve, Export

Aria drafts the structured report. One click to approve or edit. Export to PDF or push via HL7/FHIR to the hospital system.

Omnia AI doesn't replace radiologists.

It gives them a second pair of eyes that never get tired, never miss a case because the queue is too long, and never loses track of a patient's imaging history across years.

The radiologist shortage is the single greatest threat to diagnostic quality in hospitals today. Omnia AI is built to absorb the volume that would otherwise compromise care — not by replacing judgment, but by handling the routine so the expert can focus on the complex.

Every patient who waits days for a report is a patient whose outcome is at risk. Omnia AI exists to close that gap — systematically, reliably, at scale.