Dermatolify automatically validates the technical quality of patient-submitted skin photographs — before they reach the dermatologist. Three-stage pipeline. REST API. Fully auditable.
In async teledermatology, patients submit photos without clinical supervision. Dermatologists must open, assess, and reject unusable images — a step that generates zero revenue.
Patients photograph feet, furniture, or pets instead of the affected skin area. No automated system currently catches this before the physician queue.
Handheld smartphone photography produces a high rate of blurry images — especially with close-up macro shots. Standard quality checks miss this.
Overexposed or underexposed images hide the very features a dermatologist needs to assess. Dark skin tones are disproportionately affected.
Low-resolution images uploaded by older devices or compressed by messaging apps fall below the minimum threshold for remote diagnosis.
Every image passes three independent validation stages. A rejection at any stage returns an actionable reason to the patient immediately.
A frozen classification model (DINOv2 + SVM) checks whether the image shows a recognisable skin region before any pixel-level analysis is performed. Non-skin images are rejected immediately.
Privacy: image → embedding → image discarded. No pixel data stored or transmitted.
Five deterministic metrics are extracted with Pillow / NumPy / SciPy: resolution, total blur score, skin-region blur score, brightness, and file size. Fully reproducible — no ML inference.
Metrics are evaluated against a human-readable OMG DMN 1.3 rule file (FIRST-hit policy, FEEL syntax). Every rejection is traceable to a named rule and a measured value.
Thresholds are updated in the DMN file — no code change required. Fully auditable for MDR and FDA 510(k) reviews.
Independently validated on a completely held-out test set — images never seen during training. LOO-CV results confirmed.
Dermatolify is a quality assurance pre-filter, not a diagnostic tool. This distinction is foundational — documented from day one.
Dermatolify is intended to automatically assess the technical image quality and formal suitability of patient-submitted skin photographs prior to clinical review.
The system checks criteria such as resolution, exposure, sharpness, framing, and the presence of a recognisable skin region. It determines whether an image meets the minimum requirements for teledermatological evaluation.
It does not make diagnoses, classify skin conditions, or interpret dermatological findings. Clinical assessment remains exclusively with qualified medical professionals.
DL-IP-001 · DL-RA-001 · MDCG 2019-11
Dermatolify processes images in memory only. The moment analysis is complete, all pixel data is discarded. Nothing is written to disk, no image is cached, no data is transmitted to third parties.
Dermatolify is a stateless microservice. Plug it into your upload pipeline in an afternoon.
FastAPI-based endpoints for single image, batch, and FHIR validation. JSON responses with structured rejection reasons.
The /validate/fhir endpoint returns a standards-compliant FHIR R4 Observation resource — including all five metrics and SVM confidence as auditable components.
Native EHR integration. Meets § 371 SGB V interoperability requirements.
Fully containerised. The DMN rule file is mounted as a read-only volume — update thresholds without rebuilding the image.
Available as cloud-hosted SaaS or on-premise licensed deployment.
Non-exclusive SaaS licence. IP remains with Dermatolify. No code shared without a signed agreement.
We're currently onboarding pilot partners. Integration takes less than a day. No ML expertise required on your end.