& Cosmetic Allergen Tracking — NailGuard AI is an AI-powered mobile imaging support tool that non-invasively assesses nail health, disease screening and cosmetic product allergen tracking from a standard nail photograph. The system analyzes nail surface, color, thickness, shape and cuticle condition to screen for onychomycosis, psoriasis, melanonychia, onycholysis and ingrown nails.

Geometric and color changes in the nail alter light reflection/scattering characteristics. The AI quantifies these optical signatures through image processing and spectral analysis to produce a nail health score, brittleness/yellowing/thickness metrics and an ABCDEF melanonychia assessment.

Core Capabilities

• Disease screening: onychomycosis, psoriasis, melanonychia, onycholysis, ingrown nail
• ABCDEF criteria for melanonychia malignancy assessment
• Health score: brittleness, yellowing, thickness/hardness, shape, cuticle
• Product allergen: OCR ingredient reading + toxin/allergen flagging (PPD, formaldehyde, toluene, DBP)

The platform combines image processing and large language model (LLM)-based multimodal inference. From a standard RGB image, the nail region is segmented; surface, color and shape features are extracted and clinical inference is performed with a multimodal LLM.

Processing Flow

• Image capture & quality audit (brightness, contrast, sharpness, nail bounding box)
• Nail segmentation and zone-based feature extraction
• Multimodal LLM inference: disease score, health metrics, ABCDEF, allergen list, clinical commentary
• Summary metrics: health_score, confidence, risk_level, exposure_score

Software-Centric Architecture

The analysis provides multi-layer parameters across disease screening, health score and product allergen modules. Each parameter is reported as percentage (0-100), description, severity and risk level.

Output Format

• Disease: primary_suspect, alternatives[], confidence, risk_level, abcdef_assessment
• Health: brittleness_index, yellowing_score, thickness_score, shape_assessment, cuticle_health
• Product: product_name, ingredients[], flagged_allergens[], exposure_score, free_claim_verified
• AI clinical commentary + personalized recommendations + printable full report (PDF)

The platform supports clinical workflows as a screening and pre-triage tool. Nail parameter mapping provides structured, reproducible and comparable data to the physician. Final diagnosis and treatment decisions always rest with the specialist physician.

Safety Architecture (Human-in-the-loop)

• AI output is not a diagnosis; it is information supporting the physician's assessment
• AI commentary does not contain prescription/treatment recommendations
• Per-patient data isolation (RLS) and KVKK/GDPR-aligned sensitive-data approach

Integration Scenarios

The software-centric architecture aligns with European Union carbon-reduction and ESG (Environmental, Social, Governance) goals. Hardware independence brings medical consumable use close to zero and reduces the carbon footprint from patients' physical travel through telemedicine.

Roadmap

The following studies establish the clinical validity and methodological foundations of image-based AI assessment of nail diseases and nail condition. Our NailGuard AI platform draws inspiration from this literature but does not replace these methods.