& Cosmetic Allergen Tracking — DermaGuard AI is an AI-powered mobile dermatology platform that evaluates nail and scalp/hair health under a single unified platform. This brochure focuses on the Scalp module. The system non-invasively assesses scalp health, disease screening and cosmetic product allergen tracking from a standard scalp/hair photograph. It analyzes scalp color, density, thickness, sebum, dandruff and hair strands to screen for androgenetic alopecia, alopecia areata, tinea capitis, seborrheic dermatitis, FFA and lichen planopilaris.

Density, thickness, erythema and hair-loss patterns in the scalp alter light reflection/scattering characteristics. The AI quantifies these optical signatures through image processing and spectral analysis to produce a hair health score, Norwood/Ludwig staging, miniaturization score and scarring alopecia assessment.

Core Capabilities

• Disease screening: androgenetic alopecia, areata, tinea, dermatitis, FFA, lichen planopilaris
• Norwood (male) and Ludwig (female) staging + miniaturization score
• Health score: density, thickness, sebum, dandruff, erythema, anagen/telogen, brittleness, shine
• Product allergen: OCR ingredient reading + PPD/SLS/paraben/ammonia flagging

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

Processing Flow

• Image capture & quality audit (brightness, contrast, sharpness, scalp bounding box)
• Scalp segmentation and region-based feature extraction
• Multimodal LLM inference: disease score, health metrics, Norwood/Ludwig, allergen list, clinical commentary
• Summary metrics: hair_density_index, 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, norwood_stage, ludwig_stage, miniaturization_score, scarring_flag
• Health: hair_density_index, hair_thickness_score, scalp_sebum_score, dandruff_score, scalp_erythema, anagen_telogen_ratio, brittleness_score, hair_shine_score
• 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. Scalp 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 scalp and hair diseases. Our DermaGuard AI platform draws inspiration from this literature but does not replace these methods.