Clinical Trial Workflow and Data Integration
This page describes the end-to-end workflow for acne 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: Two diagonal photographs of the face, with real-time DIQA quality control
- AI scoring and review: Lesion count and ALADIN (Acne Lesion And Density INdex), reviewed by the investigator, with change from baseline 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 |
|---|---|
| Endpoints | Inflammatory lesion count, the continuous IGA score (0 to 4), ALADIN (0 to 10), or any combination |
| Capture scope | The standard left and right diagonal views; see the imaging protocol |
| Global score | The mean of the two half-face scores |
| Response definition | IGA success as defined in the FDA guidance (clear or almost clear, and at least two grades better than baseline), or a protocol-specific definition; see response definitions |
| Visit schedule | Assessment timepoints aligned with the study calendar |
For studies with significant dark skin representation, the lesion detector's performance is reported by Fitzpatrick group to inform protocol design; see Lesion detector performance.
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 views, endpoints, response definition)
- 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
Each visit needs the same two photographs: the left and the right diagonal views of the face. The distance, lighting and patient preparation are set out in the imaging protocol. Photographs are sent as they were taken, without anonymisation, filters or cropping; anonymisation is applied after scoring. In decentralised and hybrid designs, patients can take the photographs at home with a tripod and the phone's timer. The application provides:
- Perspective guidance: visual silhouettes showing the required angle for each view
- 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 both images are submitted, the AI processes them automatically in seconds and generates the report, which the investigator reviews directly in the platform. There is no central reader and no delay.
The report contains:
- Each photograph with every detected lesion marked, so the count can be checked against the image
- Lesion count and density for each half of the face
- IGA score (0 to 4) and ALADIN (0 to 10) for each half of the face and for the visit, with the severity grade
- DIQA quality score per image
The investigator verifies that the AI's assessment aligns with their clinical observation. If an image is inadequate, they can recapture and resubmit; 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 change in lesion count and in ALADIN from baseline.
The platform stores the baseline visit and compares every follow-up visit against it, so the response definition set in the protocol, such as IGA success, is read from the per-visit results without re-scoring.
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 annotated image path for each view, so each count is traceable to the source photograph |
| Per view | Inflammatory lesion count, density, IGA score and ALADIN for the left and the right diagonal |
| Visit scores | IGA score (0 to 4) and ALADIN (0 to 10) for the visit, and the IGA grade |
| Change from baseline | Absolute and percentage change in lesion count, change in ALADIN, and whether the response definition is met |
| Quality | DIQA quality score per image |
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.