The Science of Early Detection
Weapproachedearlydetectionnotasachecklistoftests,butasasystembuilttomaximizediseasecoverage,reduceblindspots,andimproveearlydetectionaccuracy
Why Routine Checkups Fall Short
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
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
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.
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.
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.
Early Detection Index (EDI)
| Accuracy | Currently Available Basic Test | Cent Scan |
|---|---|---|
| Cardiac | 30% | 87% |
| Cancer | 10% | 68% |
| Neuro (Including Stroke) | 12% | 84% |
| Metabolic Disease | 55% | 95% |
| Others | 35% | 79% |
| Early Detection Index(EDI) | 27% | 83% |
*Early Detection Index is calculated using weighted disease burden, detection sensitivity, and system-level coverage across high-impact conditions