The Science of Inclusivity
Where clinical expertise meets machine learning. Discover how we're building the future of inclusive skin analysis through the intersection of sociology, biology, and advanced computation.
Clinical Precision
99.4% Accuracy Rate
The Monk Skin Tone Scale (MST)
For decades, dermatology relied on the Fitzpatrick scale—a 6-point system developed in 1975 that often failed to represent diverse populations. CeySkin adopts the 10-point MST scale developed by Harvard's Dr. Ellis Monk.
- 10-Point Precision: Captures the true breadth of human melanin levels across all ethnicities.
- Nuanced Representation: Better identification of conditions like erythema in darker skin tones.
MST VISUAL SPECTRUM
CIELAB Color Space Analysis
How our neural networks perceive color with mathematical precision.
L* (Lightness)
Measures the perceived brightness of the skin. This dimension allows the AI to adjust for environmental lighting conditions during photo analysis.
a* (Redness/Greenness)
Essential for detecting inflammation, rosacea, or allergic reactions. It maps the spectrum from deep magenta to olive green undertones.
b* (Blueness/Yellowness)
Identifies skin warmth and sallowness. This helps in distinguishing between benign pigment variations and potential nutritional deficiencies.
Real-Time Ingredient Cross-Referencing
Our proprietary engine scans millions of formulations. It doesn't just look for “bad” ingredients—it analyzes how 15,000+ compounds interact with your specific MST profile and sensitivities.
2.4M+
Products Tracked
<200ms
Analysis Speed
Grounded in Diverse Data & Clinical Integrity
Training an AI requires more than just code; it requires truth. We've collaborated with dermatologists globally to build a dataset of over 100,000 clinically validated images across the entire MST spectrum.
Multicultural Datasets
Curated specifically to eliminate racial bias in medical AI.
Peer-Reviewed Methodology
Our analysis logic is verified by board-certified dermatologists.
Continuous Learning
Algorithms updated monthly with new clinical trial findings.