The Problem: Manual Hair Analysis Doesn't Scale
Every trichologist knows the drill: squint at a scalp image, count follicles one by one, estimate thickness, judge direction, write it all down. Thirty minutes later, you've analyzed a single image.
Your patients want objective data. Your clinic needs consistent assessments. But manual counting is:
- Slow: 30+ minutes per image
- Subjective: Different practitioners, different results
- Hard to track: Comparing assessments over time is nearly impossible
- Not scalable: You can't grow your practice while spending hours on analysis
What if you could analyze a scalp image in 8 seconds with 96% accuracy?
What We Built: AI-Powered Hair Follicle Analysis
We partnered with a trichology clinic to build an automated analysis system. Upload an image, get a complete diagnostic report—instantly.
The Results Dashboard
The system provides:
| Metric | What It Shows |
|---|---|
| Total Hair Follicles | Complete count (81 follicles in this scan) |
| Detection Success Rate | 100% of follicles classified |
| Confidence Score | AI certainty for each detection (avg 59.1%) |
| Hair Thickness Distribution | Breakdown by category (μm) |
Hair Classification Categories
The AI categorizes each follicle by thickness:
- Large Terminal Hair (>90 μm) — Strong, healthy follicles
- Intermediate Terminal Hair (60-90 μm) — Normal thickness
- Small Terminal Hair (30-60 μm) — Thinning indicators
- Vellus/Miniaturized (<30 μm) — Weak follicles requiring attention
Instant Visual Analysis
The processed image shows directional arrows color-coded by strength:
- 🟢 Green arrows: Strong follicles
- 🟡 Yellow arrows: Medium strength
- 🔴 Red arrows: Weak/miniaturized
One glance tells you the health distribution across the scalp region.
How It Works: 3 Simple Steps
Step 1: Upload Drag and drop your trichoscopy image. The system auto-crops and optimizes for analysis.
Step 2: AI Analysis Our computer vision model—trained on 4,000+ annotated images—segments each follicle and calculates:
- Position and count
- Thickness classification
- Growth direction
- Confidence score
Step 3: Download Report Get a clinic-ready PDF with all metrics, visualizations, and tracking data. Perfect for patient records.
The Technical Edge
Why We Used Segmentation (Not Just Detection)
Most AI systems use bounding boxes—rectangles around detected objects. We use pixel-level segmentation.
The difference matters:
| Approach | Accuracy | Detail Level |
|---|---|---|
| Bounding boxes | Good | Shape approximation |
| Segmentation | Better | Exact follicle boundaries |
Segmentation lets us:
- Map actual hair shapes precisely
- Handle overlapping follicles
- Calculate geometric direction from contour shape
- Provide explainable results clinicians trust
Direction Analysis: The Geometric Proxy
For each follicle, we:
- Fit a minimal enclosing triangle around the contour
- Find the shortest side's midpoint
- Draw a vector to the opposite vertex
This creates consistent, repeatable directional arrows that align with visual intuition—no noisy centerline estimation required.
Results for the Clinic
| Before AI | After AI | Impact |
|---|---|---|
| 30 min per image | 8 seconds | 99.5% faster |
| Subjective ratings | Objective metrics | Consistent across practitioners |
| Manual PDF creation | Auto-generated reports | 5+ hours/week saved |
| Difficult progress tracking | Standardized data | Easy longitudinal comparison |
What the Clinic Director Said:
"We used to spend half our day on analysis. Now we analyze more patients in an hour than we used to in a week. The standardized reports have completely changed how we track treatment progress."
— Hair health clinic, Malaysia
Sample Analysis Output
From a single uploaded image, you get:
Hair Follicle Analysis Report
- Total Hair Follicles: 81
- Successful Analyses: 81 (100% rate)
- Average Confidence: 59.1%
Distribution by Strength:
- Strong (>90 μm): 25 follicles — Avg confidence 60.7%
- Medium (60-90 μm): 23 follicles — Avg confidence 74.7%
- Weak (<60 μm): 33 follicles — Avg confidence 47.0%
Analysis Summary: Detected 81 hair follicles with 81 successful directional analyses. The dominant hair type is weak with 33 follicles—indicating potential treatment focus areas.
Built for Clinical Use
Security & Compliance
- Password-protected access
- No images stored after processing
- Environment-based configuration for HIPAA workflows
Practical UX
- Demo images for testing
- Session state preserves results
- One-click PDF download
- Works on any modern browser
Operations-Friendly
- API-based architecture
- Configurable confidence thresholds
- Ready for batch processing upgrades
What's Next
We're actively developing:
- Batch processing: Analyze entire patient folders at once
- Density heatmaps: Visual maps of follicle distribution
- Longitudinal tracking: Automatic comparison between visits
- Research export: Raw metrics for academic analysis
Is This Right for Your Clinic?
This solution works for:
- Trichology clinics tracking hair loss treatment
- Dermatology practices assessing alopecia
- Hair transplant surgeons planning procedures
- Research institutions studying follicle health
- Wellness centers offering scalp analysis services
If you're doing manual hair analysis, we can typically automate 90%+ of the work while improving accuracy.