Decision Support for Radiologybuilt for Mongolia.
Ark Axiom Bio develops CPU-only geometric analysis software to support radiologists in detecting liver, kidney, and gastric cancer from routine CT and MRI data. The system is designed for hospital pilots without GPU infrastructure, cloud dependency, proprietary scanners, or replacement of clinical judgment.
CPU-only analysis
JSON or PDF report
No cloud dependency
Final authority stays clinical
CPU
No GPU
DICOM
Native intake
LOCAL
No cloud
Decision-support pilot only. Not a replacement for radiologist judgment, regulatory clearance, pathology confirmation, or clinical signoff.
Current Clinical Status
A hospital pilot track for routine radiology workflows in Mongolia.
Ark Bio is a decision-support layer for liver, kidney, gastric, and related oncology imaging work. It is designed to support radiologists without replacing clinical judgment, changing scanner protocols, or requiring proprietary imaging hardware.
The proposed hospital pilot is tightly scoped: 100 anonymized CT/MRI studies, pathology confirmation where available, blind analysis, weekly metric reports, clinician review, and a six-week go/no-go decision.
Deployment constraint
Under 5 minutes per case, CPU-only execution, DICOM-native intake, JSON/PDF output, and on-premise processing so patient data stays inside the hospital.
Performance on Evaluated Cohorts
Current registry and pilot-track metrics from the hospital proposal deck. Prospective clinical validation and site-specific calibration remain ongoing.
The Diagnostic Engine
A structural interpretation layer clinicians can inspect.
Our engine evaluates tissue architecture across six core dimensions and produces a reproducible structural readout that clinicians can review, audit, and compare across cohorts.
Betti-0 Fragmentation Score
Counts disconnected tissue components and surfaces topological fragmentation in the lesion.
Betti-1 Loop / Void Count
Counts internal loops and void-like structures associated with necrotic or cavity patterns.
Geometric Manifold Index
Measures lesion-surface boundary regularity and lobulated morphology.
Sobolev Gradient Energy
Quantifies boundary-surface instability at the tissue interface.
Wasserstein Signal Drift
Measures intra-patient distance between reference tissue and pathological tissue.
Phase Coherence Index
Measures organizational coherence in tissue signal phase.
The output is not a black-box score alone; it is a structured readout of tissue architecture.
Why This Matters
Diagnostic software should fit the hospital infrastructure that actually exists.
Standard imaging AI often assumes GPUs, cloud connectivity, and specialized deployment support. Ark Axiom Bio was built for the opposite setting: routine imaging, local execution, and six interpretable structural readouts that a radiologist can challenge.
Mongolia is the validation market
The clinical need is concrete: among the world's highest liver and gastric cancer mortality burdens, late-stage presentation, and regional hospitals with limited advanced compute capacity.
The constraint shapes the product
The platform is built for CPU-only execution, local processing, and reviewable structural outputs rather than cloud-first GPU dependency.
The constraint is regional
The same deployment reality exists across Central Asia, Southeast Asia, and emerging market hospital networks in East Asia and beyond.
Mongolia is the starting point; the broader opportunity is clinical infrastructure that needs useful diagnostic support without specialized hardware.
Engagement Model
A bounded hospital pilot before any commercial commitment.
The pilot structure is designed for clinical accountability: radiologist review, ethics approval, on-premise processing, weekly metric reports, and an explicit go/no-go point.
Step 1
No-Cost Hospital Pilot
A hospital evaluates Ark Bio on 100 anonymized CT/MRI studies, preferably liver or kidney, with pathology confirmation where available. Ark provides software, local setup support, and a structured six-week evaluation protocol.
Step 2
Expansion Decision
If the pilot reaches the agreed success threshold, the next stage expands to a larger cohort, workflow integration review, and publication or institutional evaluation planning.
Step 3
Institutional License
After successful pilot validation, Ark Bio can move to an institutional deployment with per-scan or annual licensing terms.