AI Algorithms Catalogue

Legit.Health operates 59 distinct AI models in production, covering diagnosis support, severity quantification across 20+ clinical scales, wound assessment, surface segmentation, lesion detection, and phenotype classification. This catalogue summarises every model currently deployed in the platform. For clinical validation studies and peer-reviewed publications backing these models, see the Clinical Evidence page.

Total AI models

59

In production

Clinical models

54

Diagnosis, severity, assessment

Clinical scales

22+

PASI · EASI · SCORAD · IHS4 · AWOSI ...

Pathologies

300+

Covered through ICD-11 mapping

Note on novelty since seed close

Many algorithms in this catalogue were developed or significantly improved since seed close (Q4 2024). The specific list of post-seed additions is being finalised internally and will be marked with 🆕 badges in a subsequent update of this page.

1. Diagnosis Support

ICD Category Distribution & Binary Indicators

Multi-class classification of dermatological conditions into ICD-11 categories + binary indicators for malignancy, pre-malignancy, pigmented lesions, urgent referral (≤48h) and high-priority referral (≤2 weeks).

Outputs: Top-5 ICD-11 categories · Malignant · Pre-malignant · Associated with malignancy · Pigmented lesion · Urgent referral · High-priority referral · Lead: Ignacio Hernández

2. Severity Intensity Quantification (visual signs)

Ordinal classification on a 0-9 scale for 10 distinct clinical visual signs. These are the foundational building blocks of composite clinical scales (PASI, EASI, SCORAD, GPPGA, PPPASI, ODS).

#	Visual sign	Category	Clinical scales supported
1	Erythema	Inflammatory	PASI · EASI · SCORAD · GPPGA · PPPASI
2	Desquamation	Morphological	PASI · GPPGA · PPPASI
3	Induration	Textural	PASI
4	Pustule	Lesion-type	PPPASI · GPPGA · Acne
5	Crusting	Morphological	EASI · SCORAD
6	Xerosis	Textural	EASI · SCORAD · ODS
7	Swelling	Inflammatory	EASI · SCORAD
8	Oozing	Inflammatory	EASI · SCORAD
9	Excoriation	Morphological	EASI · SCORAD
10	Lichenification	Textural	EASI · SCORAD

Lead: Daniel Dagnino. Output format: ordinal 0-9 + continuous confidence score.

3. Wound Assessment (binary classification)

Wound care has been a major expansion area. 22 binary classifiers cover the AWOSI framework, NPUAP staging, TIME framework, and infection surveillance.

Wound edges (6 models)

#	Model	Detects
1	Perilesional Erythema	Inflammation around wound
2	Damaged Edges	Compromised wound margins
3	Delimited Edges	Well-defined boundaries
4	Diffuse Edges	Poorly defined boundaries
5	Thickened Edges	Hyperkeratotic / rolled edges
6	Indistinguishable Edges	Severely compromised edges

Exudate characterisation (5 models)

#	Model	Detects
7	Perilesional Maceration	Moisture damage in periwound skin
8	Fibrinous Exudate	Normal healing response indicator
9	Purulent Exudate	Infection indicator
10	Bloody Exudate	Tissue fragility / hemorrhage
11	Serous Exudate	Clear/watery exudate

Wound tissue assessment (11 models)

#	Model	Detects
12	Biofilm-Compatible Tissue	Biofilm presence indicators
13	Affected Tissue: Bone	Bone involvement / osteomyelitis risk
14	Affected Tissue: Subcutaneous	Partial-thickness loss
15	Affected Tissue: Muscle	Muscle involvement
16	Affected Tissue: Intact Skin	Stage I pressure injury
17	Affected Tissue: Dermis-Epidermis	Partial-thickness skin loss
18	Wound Bed: Necrotic	Necrotic tissue presence
19	Wound Bed: Closed	Healed vs open
20	Wound Bed: Granulation	Healthy granulation
21	Wound Bed: Epithelial	Epithelialisation phase
22	Wound Bed: Slough	Devitalised tissue

Lead: Daniel Dagnino. Use cases: AWOSI scoring, NPUAP staging, debridement planning, infection detection, healing prognosis.

4. Wound Staging & Composite Scores (2 models)

Wound Stage Classification

Multi-class assignment to NPUAP Stages 0, I, II, III, IV. Treatment planning input.

Lead: Daniel Dagnino

Wound AWOSI Score

Ordinal regression quantifying wound severity on the AWOSI scale (0-20). Composite output of underlying binary + intensity models.

Lead: Daniel Dagnino · Paper in pipeline (Daniel Dagnino, JAAD)

5. Surface Quantification (segmentation models, 13)

Pixel-level segmentation for surface area measurement. Outputs include percentage, absolute area (cm² when calibration available), and clinical alerts.

#	Model	Application
1	Body Surface Segmentation	5-class (Head/Neck, Upper Extremities, Trunk, Lower Extremities) for BSA calculation. PASI/EASI/Burn assessment.
2	Erythema Surface	Wound erythema area + perilesional. Infection surveillance, AWOSI.
3	Wound Bed Surface	Total wound area + perimeter + max length/width. Healing rate.
4	Granulation Tissue Surface	Healing progression indicator. AWOSI, wound bed preparation.
5	Biofilm and Slough Surface	Debridement guidance. TIME framework.
6	Necrosis Surface	Urgent debridement indicator. Infection risk.
7	Maceration Surface	Moisture management. Dressing selection.
8	Orthopedic Material Surface	Exposed hardware detection. Surgical revision alerts.
9	Bone/Cartilage/Tendon Surface	Deep structure exposure. Osteomyelitis risk. Amputation risk.
10	Hair Loss Surface	3-class (Hair, No Hair, Non-Scalp). SALT, APULSI scoring.
11	Nail Lesion Surface	NAPSI, OSI scoring.
12	Hypopigmentation / Depigmentation Surface	VASI, VETF. Vitiligo assessment.
13	Hyperpigmentation Surface	MASI, mMASI. Melasma + PIH assessment.

Lead: Daniel Dagnino (most) + Ignacio Hernández (nail).

6. Lesion Detection (object detection, 5 models)

Object detection (YOLO architecture) for individual lesion counting and localisation. Bounding boxes + confidence scores.

Inflammatory Nodular Lesion (HS)

4-class: nodules, abscesses, non-draining tunnels, draining tunnels. IHS4 scoring for Hidradenitis Suppurativa.

Lead: Alberto Sabater

Acneiform Lesion Type

5-class: papules, pustules, cysts, comedones, nodules. GAGS / IGA scoring.

Lead: Alberto Sabater · ALADIN paper accepted Q2 2026 (Skin Health and Disease)

Acneiform Inflammatory Lesion (count)

General inflammatory skin lesion counting. GAGS, EASI, inflammatory dermatoses.

Lead: Alberto Sabater

Hive Lesion (urticaria)

Urticarial wheal counting. UAS7, UCT scoring. Pharma trial endpoint.

Lead: Alberto Sabater · AUAS paper published JID Innovations 2024

Hair Follicle

Hair follicle counting on scalp images. Androgenetic alopecia, alopecia areata, telogen effluvium, hair transplantation, treatment monitoring.

Lead: Ignacio Hernández

7. Phenotype & Pattern Classification (2 models)

Follicular & Inflammatory Pattern (HS)

Multi-class HS phenotype identification (follicular, inflammatory, mixed). Martorell Classification.

Lead: Ignacio Hernández

Inflammatory Pattern + Hurley Staging

Multi-task: Hurley Stage (I/II/III) + binary inflammatory activity. HS-PGA, IHS4 inputs.

Lead: Alberto Sabater

8. Operational / Non-clinical Models (5)

Pipeline and quality assurance models that enable the rest of the platform to operate at clinical-grade reliability.

#	Model	Function
1	DIQA (Dermatology Image Quality Assessment)	0-10 quality score with dimension subscores. Telemedicine + quality control. Published JAAD 2023.
2	Domain Validation	Multi-class: non-skin / skin clinical / skin dermoscopic. Image routing.
3	Skin Surface Segmentation	Binary skin region detection. Preprocessing, ROI extraction.
4	Body Surface Segmentation	5-class anatomical regions. BSA calculation (clinical use, listed in §5 above).
5	Head Detection	Privacy protection + patient counting + multi-patient flag.

Leads: Ignacio Hernández (DIQA, Domain Validation), Alberto Sabater (Skin Surface, Head Detection).

Strategic implications for investors

★ The technical moat in one diagram ─────────

Competitor	Approximate model count	Coverage
Legit.Health	59	Multi-pathology platform · 22+ clinical scales
Skin Analytics	~1	Melanoma detection only
DermaSensor (FDA cleared)	~1	Skin cancer detection only
SkinVision	1-2	Skin cancer self-screening
MetaOptima (DermEngine)	3-5	Diagnosis support + analysis

Building a multi-pathology platform with 59 distinct models requires 3-5 years of catch-up investment in data, annotation, and clinical validation. This is the durable technical moat that justifies higher valuation multiples vs. single-purpose competitors. ─────────────────────────────────────────────────

Pipeline coverage map by clinical area

Therapeutic area	Models supporting this area	Clinical scales
Psoriasis	4 (intensity) + 1 segmentation	PASI · PPPASI · GPPGA
Atopic dermatitis	7 (intensity) + 1 segmentation	EASI · SCORAD · ODS
Hidradenitis suppurativa	1 detection (4 classes) + 2 phenotype	IHS4 · Martorell · Hurley · HS-PGA
Acne	2 detection + 1 intensity	GAGS · IGA · ALADIN
Urticaria	1 detection	UAS7 · UCT · AUAS
Wound care	22 binary + 1 stage + 1 composite + 7 segmentation	AWOSI · NPUAP · TIME framework
Alopecia	1 detection + 1 segmentation	SALT · APULSI
Nail diseases	1 segmentation	NAPSI · OSI
Vitiligo	1 segmentation	VASI · VETF
Melasma / PIH	1 segmentation	MASI · mMASI
Skin cancer	ICD diagnosis + binary indicators	Triage urgency
Quality control	DIQA · Domain · Skin Surface · Head Detection	—

See also

• Clinical Evidence: 7 peer-reviewed papers + 10-paper pipeline validating these algorithms
• Medical Device Certification: regulatory framework for these models
• Commercial Metrics: pharma + clinical use cases enabled by each algorithm