ruvector/examples/dragnes/docs/dermlite-integration.md

DrAgnes DermLite Integration Research

Status: Research & Planning Date: 2026-03-21

Overview

DermLite (manufactured by 3Gen Inc., San Juan Capistrano, CA) is the world's most widely used line of dermatoscopes. DrAgnes is designed as a DermLite-native platform, providing purpose-built integration with their device ecosystem for standardized dermoscopic imaging and analysis.

DermLite Device Lineup

DermLite HUD (Heads-Up Display)

• Form Factor: Standalone camera with built-in display and optics
• Magnification: 10x polarized
• Illumination: LED ring with polarization filter
• Camera: Built-in 12MP sensor, 1920x1080 capture
• Connectivity: Wi-Fi (image transfer), Bluetooth (metadata/control)
• Unique Features:
  • Hands-free operation (no phone attachment needed)
  • Built-in display shows magnified real-time view
  • Dual-mode: polarized and non-polarized switching
  • Internal storage for batch capture
• DrAgnes Integration: Wi-Fi direct for image transfer; Bluetooth for device control and metadata. Best suited for high-volume clinical environments.

DermLite DL5

• Form Factor: Handheld dermatoscope with smartphone adapter
• Magnification: 10x, hybrid polarized/non-polarized (toggle)
• Illumination: 20 PigmentBoost LEDs + 4 polarized LEDs
• Adapter: Universal magnetic mount (MagnetiConnect)
• Power: Rechargeable lithium-ion, 4+ hours continuous use
• Unique Features:
  • PigmentBoost mode enhances pigmented structures
  • Hybrid mode allows instant switching without contact loss
  • Crystal-clear optics with minimal distortion
  • Compact enough for pocket carry
• DrAgnes Integration: Phone camera passthrough via adapter. Camera API captures at phone's native resolution. DL5's PigmentBoost mode is flagged in metadata for preprocessing calibration.

DermLite DL4

• Form Factor: Compact pocket dermatoscope
• Magnification: 10x, polarized only
• Illumination: LED ring, polarized
• Adapter: Smartphone adapter available (MagnetiConnect)
• Power: Rechargeable or AA batteries
• Unique Features:
  • Most affordable DermLite model
  • Widely adopted in primary care
  • Lightweight (50g)
• DrAgnes Integration: Same phone camera passthrough as DL5. Lower-tier device but adequate for DrAgnes classification. Ideal for primary care adoption.

DermLite DL200 Hybrid

• Form Factor: Handheld with contact/non-contact dual mode
• Magnification: 10x
• Illumination: Hybrid LED system
• Contact Mode: Immersion fluid or direct contact with glass plate
• Non-Contact Mode: Cross-polarized at distance
• Adapter: Magnetic smartphone mount
• Unique Features:
  • Contact mode reveals subsurface structures (vessels, deeper pigment)
  • Non-contact mode for mucosal surfaces, painful areas
  • Dual-mode in single device
• DrAgnes Integration: Contact mode detection via metadata or image analysis (presence of glass plate reflection). Different preprocessing paths for contact vs. non-contact images.

Image Capture Integration

MediaStream API (Browser-Based)

DrAgnes Camera Module
    │
    ├── navigator.mediaDevices.getUserMedia({
    │       video: {
    │           facingMode: 'environment',    // Rear camera (DermLite side)
    │           width: { ideal: 1920 },
    │           height: { ideal: 1080 },
    │           frameRate: { ideal: 30 },
    │           focusMode: 'manual',          // Lock focus for dermoscopy
    │           whiteBalanceMode: 'manual',   // Calibrated for DermLite LEDs
    │       }
    │   })
    │
    ├── Live Preview (Canvas)
    │       ├── Real-time focus quality indicator
    │       ├── Lesion centering guide (circle overlay)
    │       ├── Exposure warning (over/under)
    │       └── DermLite detection indicator
    │
    ├── Capture (requestVideoFrameCallback)
    │       ├── High-res still capture (max sensor resolution)
    │       ├── Multi-frame averaging (3 frames for noise reduction)
    │       └── Auto-rotation correction
    │
    └── Storage (IndexedDB)
            ├── Original capture (encrypted)
            ├── Preprocessed 224x224 tensor
            └── Metadata (device, timestamp, settings)

DermLite Device Detection

DrAgnes auto-detects DermLite attachment through multiple signals:

1. Image analysis: DermLite images have characteristic features:

   • Circular field of view (dark corners from circular optics)
   • Consistent illumination pattern (LED ring)
   • Magnification level (10x produces distinctive scale)
   • Polarization artifacts (cross-polarized light produces specific color shifts)

2. EXIF metadata: Some DermLite-phone combinations include device info

3. User confirmation: Manual DermLite model selection in UI as fallback

Image Quality Assessment

Before classification, DrAgnes assesses image quality:

Quality Assessment Pipeline
    │
    ├── Focus Quality (Laplacian variance)
    │       ├── Score < 100: "Blurry -- please refocus"
    │       ├── Score 100-500: "Acceptable"
    │       └── Score > 500: "Sharp"
    │
    ├── Exposure Check (histogram analysis)
    │       ├── Mean intensity < 50: "Underexposed"
    │       ├── Mean intensity > 200: "Overexposed"
    │       └── Dynamic range < 100: "Low contrast"
    │
    ├── Lesion Coverage (center ROI analysis)
    │       ├── Lesion < 10% of frame: "Too far -- zoom in"
    │       ├── Lesion > 90% of frame: "Too close -- zoom out"
    │       └── Lesion off-center: "Center the lesion"
    │
    ├── Hair Occlusion (line detection)
    │       ├── > 20% coverage: "Excessive hair -- consider removal"
    │       └── Software hair removal applied regardless
    │
    └── Artifact Detection
            ├── Bubble artifacts (contact dermoscopy)
            ├── Reflection artifacts (glass plate)
            └── Motion blur (movement during capture)

Dermoscopic Analysis Modules

ABCDE Criteria Automation

The ABCDE mnemonic is the most widely taught screening tool for melanoma detection.

A - Asymmetry:

Method: Divide lesion along two perpendicular axes of maximum symmetry
    │
    ├── Segmentation: Otsu thresholding + morphological cleanup
    ├── Axis detection: Principal Component Analysis on contour points
    ├── Mirror comparison: XOR of left/right and top/bottom halves
    ├── Scoring:
    │       ├── 0: Symmetric along both axes
    │       ├── 1: Asymmetric along one axis
    │       └── 2: Asymmetric along both axes
    └── Weight: 1.3x (highest discriminative power for melanoma)

B - Border Irregularity:

Method: Divide border into 8 equal segments, assess each
    │
    ├── Contour extraction: Canny edge detection on segmentation mask
    ├── Segment division: 8 equal arc-length segments from centroid
    ├── Irregularity metrics per segment:
    │       ├── Fractal dimension (box-counting method)
    │       ├── Curvature variation (second derivative of contour)
    │       └── Abrupt border cutoff (gradient magnitude at boundary)
    ├── Scoring: 0-8 (count of irregular segments)
    └── Weight: 0.1x per segment

C - Color:

Method: Count distinct colors present in lesion
    │
    ├── Color space: Convert to perceptually uniform CIELAB
    ├── Reference colors (6 clinically significant):
    │       ├── Light brown (tan)
    │       ├── Dark brown
    │       ├── Black
    │       ├── Red
    │       ├── Blue-gray
    │       └── White (regression)
    ├── Detection: K-means clustering (k=6) + distance to reference
    ├── Scoring: 1-6 (count of colors present)
    └── Weight: 0.5x

D - Diameter:

Method: Maximum diameter of lesion in mm
    │
    ├── Calibration: DermLite ruler overlay or known magnification (10x)
    ├── Measurement: Maximum Feret diameter of segmentation contour
    ├── Threshold: 6mm is the clinical cutoff
    ├── Note: Nodular melanomas can be < 6mm; size alone is insufficient
    └── Weight: Binary (>= 6mm adds to risk score)

E - Evolution:

Method: Compare current image to prior captures of same lesion
    │
    ├── Registration: Affine alignment using lesion contour landmarks
    ├── Change detection:
    │       ├── Area change (growth rate in mm^2/month)
    │       ├── Color change (new colors appearing)
    │       ├── Shape change (symmetry score delta)
    │       ├── Border change (irregularity score delta)
    │       └── New structures (dermoscopic features appearing/disappearing)
    ├── Scoring: Composite change score normalized to 0-1
    └── Note: Most powerful criterion but requires longitudinal data

7-Point Checklist (Argenziano Method)

A structured scoring system for dermoscopic evaluation:

Criterion	Points	Detection Method
Atypical pigment network	2 (major)	CNN feature detection on dermoscopic structures
Blue-whitish veil	2 (major)	Color analysis in blue-gray spectrum + opacity detection
Atypical vascular pattern	2 (major)	Red channel analysis + vessel topology extraction
Irregular streaks	1 (minor)	Directional filter banks + radial analysis from center
Irregular dots/globules	1 (minor)	Blob detection (LoG) + regularity analysis
Irregular blotches	1 (minor)	Connected component analysis in dark regions
Regression structures	1 (minor)	White scar-like areas + blue-gray peppering detection

Interpretation: Total score >= 3 suggests melanoma. Sensitivity ~95%, specificity ~75% in clinical studies.

DrAgnes Implementation: Each criterion has a dedicated CNN sub-head trained on the Derm7pt dataset which provides expert annotations for all 7 criteria. The sub-heads share the MobileNetV3 backbone but have independent classification layers.

Menzies Method

A simplified 2-step approach used in clinical practice:

Step 1 - Negative Features (must be absent for melanoma):

• Point symmetry of pigmentation
• Single color presence

Step 2 - Positive Features (at least one must be present for melanoma):

1. Blue-white veil
2. Multiple brown dots
3. Pseudopods
4. Radial streaming
5. Scar-like depigmentation
6. Peripheral black dots/globules
7. Multiple colors (5-6)
8. Multiple blue-gray dots
9. Broadened network

DrAgnes Implementation: Binary classifiers for each positive and negative feature. If both negative features are absent AND at least one positive feature is present, flag for melanoma consideration.

Pattern Analysis (Advanced Dermoscopy)

Beyond ABCDE and checklists, DrAgnes performs pattern-level analysis:

Global Patterns:

Pattern	Association	Detection
Reticular	Benign melanocytic	Network detection via Gabor filters
Globular	Benign melanocytic	Blob detection (LoG, DoG)
Homogeneous	Benign (blue nevus, dermatofibroma)	Variance analysis (low variance = homogeneous)
Starburst	Spitz nevus or melanoma	Radial streaks from center + symmetry
Multicomponent	Melanoma (multiple patterns)	Pattern diversity score (entropy)
Nonspecific	Various	Low confidence flag for expert review

Local Structures:

Structure	Clinical Significance	Detection Method
Pigment network	Regular=benign, irregular=suspicious	Gabor filter response + regularity metrics
Dots	Regular=benign, irregular=melanoma	LoG blob detection + spatial distribution analysis
Globules	Regular=benign, irregular=melanoma	Larger blob detection + shape analysis
Streaks	Radial=melanoma, regular=Spitz	Directional filter + radial pattern detection
Blue-white veil	Melanoma indicator	Color segmentation + opacity detection
Regression structures	Melanoma regression	White+blue-gray area detection
Vascular structures	Various (type-dependent)	Red channel + vessel topology
Milia-like cysts	Seborrheic keratosis	Bright spot detection with specific shape
Comedo-like openings	Seborrheic keratosis	Dark spot detection + shape analysis
Leaf-like structures	BCC	Edge structure detection + morphology
Large blue-gray ovoid nests	BCC	Connected component + color analysis

EHR Integration Research

FHIR R4 Resources

DrAgnes maps to standard FHIR resources for EHR interoperability:

DrAgnes Entity	FHIR Resource	Notes
DermImage	Media	With bodySite coding (SNOMED CT)
LesionClassification	DiagnosticReport	observationResult references
ABCDE Scores	Observation	One per criterion, grouped
Clinician Feedback	ClinicalImpression	Links to DiagnosticReport
Biopsy Result	DiagnosticReport	histopathology category
Follow-Up	ServiceRequest	scheduled monitoring

Practice Management Systems

System	Integration Method	Coverage
Epic	Epic on FHIR (R4), CDS Hooks	~38% US market
Cerner (Oracle Health)	FHIR R4 API	~25% US market
athenahealth	athenaFlex (FHIR R4)	~10% US market
Modernizing Medicine (EMA)	Proprietary API + FHIR	Dermatology specialty leader
Nextech	Proprietary API	Dermatology/plastic surgery focus

Priority Integration: Modernizing Medicine's EMA (Electronic Medical Assistant) is the dominant EHR for dermatology practices. Integration with EMA should be a Phase 2 priority.

Calibration & Quality Assurance

Color Calibration

DermLite LEDs have a known color temperature (~4500K). DrAgnes calibrates:

1. Capture image of ColorChecker (X-Rite) chart through DermLite
2. Compute color correction matrix (3x3 affine in CIELAB)
3. Apply correction to all subsequent captures
4. Re-calibrate monthly or when device changes

Magnification Calibration

1. Capture image of known-size reference (DermLite ruler or 1mm grid)
2. Compute pixels-per-mm at 10x magnification
3. Store calibration factor per device
4. Use for accurate diameter measurements (ABCDE "D" criterion)

Inter-Device Consistency

Different DermLite models produce subtly different images. DrAgnes normalizes:

• Color normalization: Shades of Gray algorithm standardizes illumination
• Magnification normalization: Resize to consistent pixels-per-mm
• Polarization normalization: Separate processing paths for polarized vs. non-polarized
• Contact artifact handling: Detect and compensate for contact plate reflections

DermLite SDK & API Research

Current State (2026)

3Gen Inc. does not provide a public SDK for DermLite devices. Integration relies on:

• Phone camera passthrough (DermLite acts as optical adapter)
• Wi-Fi direct for HUD model image transfer
• Bluetooth for HUD model control
• EXIF metadata extraction where available

Recommended API Strategy

1. Phase 1: Camera API integration (no DermLite SDK dependency)

   • Works with all DermLite models via phone camera
   • Auto-detect DermLite presence via image analysis
   • Manual device selection fallback

2. Phase 2: Partner with 3Gen for official SDK access

   • Direct device control (focus, illumination, capture)
   • Device serial number for calibration persistence
   • Firmware version tracking for compatibility

3. Phase 3: Co-develop next-gen DermLite with embedded AI

   • On-device CNN inference (edge deployment)
   • Built-in calibration reference
   • Direct brain connectivity
   • Real-time AR overlay with diagnostic guidance