Imaging Protocol
This page describes the image capture methodology for psoriasis PASI assessment, including the 11-perspective full-body protocol, decentralised capture, and quality control.
Smartphone-based capture
Legit.Health uses standard smartphone cameras for image acquisition. No specialised photography equipment is required.
Traditional clinical photography often relies on systems like Canfield VISIA, which require per-site hardware, per-site calibration, and significant rental or purchase costs. Smartphone-based capture eliminates these costs while maintaining the image quality needed for AI scoring.
The Legit.Health mobile application guides investigators through the capture process with visual perspective silhouettes, real-time DIQA quality checks, and immediate feedback on image adequacy.
For psoriasis, the full-body protocol captures all four PASI regions using a standard smartphone; no dermatoscope or specialised camera is needed.
Standard 11-perspective protocol
The default protocol captures 11 images: 7 full-body perspectives for BSA measurement and 4 close-ups for intensity scoring:
| Protocol | Perspectives | Use case |
|---|---|---|
| Standard full-body (11-perspective) | 11 views: Head front, Head back, Trunk front, Trunk back, Legs front, Legs back, Foot back, Close-up: head, Close-up: trunk, Close-up: arms, Close-up: legs | Full-body BSA assessment for moderate-to-severe plaque psoriasis. 7 full-body perspectives for area measurement + 4 close-ups for intensity scoring. Supports decentralised (home-based) capture. |
| Reduced (4-region close-ups only) | 4 views: Close-up: head, Close-up: trunk, Close-up: arms, Close-up: legs | Close-up images only for severity intensity assessment without BSA measurement. Suitable for studies where BSA is assessed manually. |
| Custom | Any combination of perspectives | Any combination of perspectives, defined in collaboration with the sponsor during protocol design. |
Full-body perspectives (7 images)
These provide coverage of the entire body surface for BSA segmentation:
- Head front: face and anterior scalp
- Head back: posterior scalp and neck
- Trunk front: chest, abdomen
- Trunk back: upper and lower back
- Legs front: anterior thighs, knees, shins
- Legs back: posterior thighs, calves
- Foot back: soles of feet
Close-up perspectives (4 images)
One close-up per PASI region for detailed intensity assessment:
- Close-up: head: representative psoriatic plaque on head/scalp
- Close-up: trunk: representative plaque on trunk
- Close-up: arms: representative plaque on upper extremities
- Close-up: legs: representative plaque on lower extremities
The close-ups should capture the most representative psoriatic plaque in each region. The AI uses these for fine-grained erythema, desquamation, and induration scoring.
Decentralised (home-based) capture
The 11-perspective protocol is specifically designed for decentralised imaging, allowing patients to capture images at home without visiting the clinic. This has been validated in a Phase 3 clinical trial for JNJ-77242113.
The Legit.Health mobile application provides:
- Step-by-step perspective guidance: Visual silhouettes for each of the 11 perspectives
- Real-time DIQA quality check: Each image is validated before acceptance
- Sequential capture flow: The app guides through all 11 perspectives in order
- Immediate feedback: If an image fails quality standards, the patient recaptures immediately
DIQA: Dermatology Image Quality Assessment
What is DIQA?
DIQA (Dermatology Image Quality Assessment) is an AI-powered image quality assessment algorithm that evaluates every captured image in real time before it is accepted for analysis. It was developed by Legit.Health and published in the Journal of the American Academy of Dermatology (Hernández Montilla et al., 2023).
What DIQA evaluates
| Quality dimension | What it checks | Why it matters |
|---|---|---|
| Focus | Sharpness of the image; absence of motion blur | Out-of-focus images can obscure small lesions, leading to undercounting |
| Lighting | Adequate, even illumination; absence of harsh shadows or glare | Poor lighting creates shadows that mimic or hide lesions |
| Framing | Correct anatomical region captured at the required angle | Incorrect framing means the AI analyses the wrong area |
| Resolution | Sufficient pixel density for lesion detection | Low resolution makes small features undetectable |
How it works in the workflow
- The investigator captures an image through the mobile application
- DIQA evaluates the image immediately (sub-second processing)
- If the image passes: it is accepted and queued for AI scoring
- If the image fails: the investigator receives immediate feedback explaining the quality issue and must recapture
Configurable thresholds
The DIQA pass/fail threshold is configurable per study protocol. Sponsors can choose stricter thresholds for pivotal studies (rejecting more images to ensure the highest quality) or more lenient thresholds for real-world evidence studies.
Patient preparation
- Wear minimal, loose-fitting clothing: Full-body images require exposure of all four PASI regions (head, trunk, arms, legs)
- Remove topical treatments: Emollients and topical steroids can alter the appearance of erythema and scaling
- Consistent lighting: Even illumination across the body is critical for accurate erythema assessment. Avoid harsh shadows.
- Neutral background: A plain, non-reflective background helps the body segmentation AI delineate body boundaries accurately
Environmental conditions
- Well-lit room with even illumination; avoid harsh shadows that distort erythema assessment
- Neutral, non-reflective background, essential for body segmentation
- Consistent distance from camera (~1.5–2 metres for full-body, ~30 cm for close-ups)
- Same conditions at every visit: lighting, background, and distance should be consistent for reliable longitudinal comparison