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The Science of Early Detection

Weapproachedearlydetectionnotasachecklistoftests,butasasystembuilttomaximizediseasecoverage,reduceblindspots,andimproveearlydetectionaccuracy

Why Routine Checkups Fall Short

They are built to detect today's diseases, not to uncover future risks growing in your body.

Not Built for Early Detection

Most health packages are built for general assessment or symptomatic care, not for systematically detecting life threatening diseases.

Limited Coverage

Most routine packages are designed around cost, not disease burden, resulting in limited coverage and early detection rates (often below 20%).

No Technology-Driven Detection

Without structured AI and quantitative analysis, subtle early abnormalities often go unnoticed until disease progresses.

The Cent Detection Architecture

A structured, multi-layered system engineered to maximize early detection accuracy and coverage

CCNM™ Framework

A structured disease-targeting model focused on the major causes of premature mortality

Multi-Omics Integration

Combining imaging, biomarkers, and genetics to reduce blind spots

AI-Led Precision

Advanced algorithms that optimize imaging and detect subtle early abnormalities.

The CENT SCAN

The CENT SCAN

Structured detection. Multi-layer analysis. AI precision

TRU10™

Health Score

An organ-wise, trackable score translating complex data into clear health insight


300+

Conditions Covered

Expanded detection across 300+ clinically significant conditions

Early Detection Index: 83%

Traditional checkups typically achieve early detection coverage of approximately 15–20%

CCNM™ Protocol

The CCNM™ protocol defines a targeted set of tests designed to detect the major disease categories responsible for most premature deaths cardiovascular, cancer, neurological, and metabolic conditions. Rather than performing random investigations, CCNM™ ensures screening is structured, evidence-aligned, and focused on high-impact diseases in early detection.

CCNM™ Protocol

Multi-Omics Integration

We do not rely on a single test or marker. By integrating imaging, blood biomarkers, genetics, and other biological data, we synthesize signals across systems, reducing false positives, minimizing false negatives, and improving overall detection clarity.

Multi-Omics Integration

AI-Driven Imaging Analysis

Our AI models enhance imaging scan quality and assists in identifying subtle structural changes that may be difficult to detect in early stages. Every AI output is validated by specialists, combining computational precision with medical expertise.

AI-Driven Imaging Analysis

Early Detection Index (EDI)

A measurable indicator of how comprehensively and accurately a screening system detects high-impact diseases

AccuracyCurrently Available Basic TestCent Scan

Cardiac

30%83%

Cancer

10%60%

Neuro (Including Stroke)

12%80%

Metabolic Disease

55%95%

Others

35%75%

Early Detection Index(EDI)

27%77%

*Early Detection Index is calculated using weighted disease burden, detection sensitivity, and system-level coverage across high-impact conditions

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