Clinical Trial Workflow and Data Integration
This page describes the end-to-end workflow for alopecia severity assessment in a clinical trial using the Legit.Health platform, from protocol design through to EDC data delivery.
Workflow overview
The workflow has two phases: a one-time study setup, and a per-visit cycle that repeats at every assessment.
Study setup (once per study)
- Protocol design: Configure the study with Legit.Health
- Site setup: Deploy the platform to investigator sites
Per-visit cycle (each assessment)
- Patient enrolment: Register the patient in the platform
- Image capture: Four scalp photographs at the site or at home, with real-time DIQA quality control
- AI scoring and review: Automated SALT, reviewed by the investigator, with change from baseline and alerts tracked across visits
- Data export: Structured delivery to the EDC system
1. Protocol design
Before the study begins, Legit.Health works with the sponsor or CRO to configure the study protocol:
| Configuration | Options |
|---|---|
| Scoring system | Total automated SALT, regional scores per quadrant, or both |
| Capture scope | The standard four quadrants (top, back, left and right), or custom views; see the imaging protocol |
| Severity bands | AA-IGA categories of the total SALT by default, or protocol-specific cut-offs; see severity bands |
| Visit schedule | Assessment timepoints aligned with the study calendar |
| Alert thresholds | Configurable increase in SALT from baseline that triggers a notification to the site, for example ≥25% |
For studies with significant dark skin representation, the hair loss model's error is reported by Fitzpatrick group to inform protocol design; see Performance across skin phototypes.
A study-specific investigator manual is generated for each trial, providing site personnel with step-by-step instructions, example images, and a knowledge test to confirm training.
The scoring model version is locked at study initiation. No mid-study model updates occur, so every patient is scored by the same validated model and endpoint integrity is preserved across the trial. Any model update requires full re-validation before deployment.
2. Site setup
The Legit.Health clinical trials platform is deployed as a web application accessible from any browser. Each investigator site receives:
- Login credentials for all study personnel
- Pre-configured protocol (capture scope, endpoints, alert thresholds)
- Investigator manual (digital, accessible from the platform)
- Training resources and knowledge test
No hardware installation is required; investigators use their existing smartphones for image capture.
3. Patient enrolment
After enrolling a patient in the study per the clinical protocol, the investigator creates the patient record in the Legit.Health platform. Each patient is identified by a study-specific pseudonymised identifier; no personal data (name, date of birth) is stored in the platform.
4. Image capture
Images can be taken at the investigator site or, in decentralised and hybrid designs, at home, with the patient seated so that the top of the scalp can be captured consistently. Each visit needs the same four photographs, one per scalp quadrant: top, back, left and right. The distance, lighting and patient preparation are set out in the imaging protocol. The application provides:
- Perspective guidance: visual silhouettes showing the required angle for each of the four quadrants
- Real-time quality control: the DIQA quality gate checks each image at capture and flags substandard images for immediate recapture, so only adequate images proceed to scoring
5. AI scoring and review
Once the four images are submitted, the AI processes them automatically in seconds and generates the SALT report, which the investigator reviews directly in the platform. There is no central reader and no delay.
The report contains:
- Per-quadrant hair loss percentage, with the segmentation mask for each photograph
- Per-quadrant SALT contribution, weighted by each quadrant's share of the scalp
- Total SALT score on the standard 0 to 100 scale, with its AA-IGA severity category
- DIQA quality score per image
The investigator verifies that the AI's assessment aligns with their clinical observation. If any image is inadequate, they can recapture and resubmit the full set of four; only the latest submission for each visit is retained.
From the follow-up visits onward, the same report tracks how severity evolves across the study, with the absolute and percentage change in SALT from baseline.
The platform stores the baseline score and compares every follow-up visit against it. When hair loss increases by the configured threshold, for example ≥25% from baseline, the site receives an email notification, and the investigator confirms the finding through clinical assessment. The threshold is inclusive, so borderline cases are flagged for review rather than missed. This supports both efficacy trials, where SALT tracks regrowth, and safety monitoring, where drug-induced hair loss is an adverse event.
Responder definitions such as SALT 20 or less, SALT 50, SALT 75 and SALT 90 are derived from the same per-visit scores; see SALT response thresholds.
6. Data export and EDC integration
Where the in-platform report (step 5) is the investigator's human-readable view, the export is a structured, analysis-ready dataset built to load straight into the sponsor's EDC. Each assessment is delivered as column-level records, not a re-rendering of the report.
Exported data fields
Field names below are representative; the exact schema is agreed during study setup.
| Group | Fields |
|---|---|
| Identifiers | study_code, site_code, subject_code, visit_code, diagnostic_report_id, timestamp, condition_code |
| Source images | Original image path and segmentation mask path for each of the four quadrants, so each score is traceable to the source photograph |
| Regional scores | Hair loss percentage and SALT contribution per quadrant (top, back, left, right), plus the total SALT score |
| Change from baseline | Absolute and percentage change in total SALT from the baseline visit, and whether the visit triggered an alert |
| Quality | DIQA quality score per image, and the severity band when configured |
Every field is delivered per visit, so the same structure supports both single-visit analysis and longitudinal review.
Integration methods
- API-based: Automated data flow from the Legit.Health platform to the sponsor’s EDC system via RESTful API
- CSV export: Structured CSV files for manual import into EDC systems
- CRF field mapping: Data fields pre-mapped to the sponsor’s Case Report Form structure, configured during protocol design
- QuantifiCare platform integration: For studies using the combined QuantifiCare + Legit.Health solution, scoring data flows through QuantifiCare’s platform to the sponsor’s data management system
All exported data is structured for direct mapping to CRF fields in standard EDC systems.
The integration is API-based and system-agnostic; it works with any major EDC system. Export formats include RESTful API, CSV/Excel, and structured JSON.