DermEdge
Private eczema screening and flare tracking: a camera, a wearable, and AI that runs on your own device. No cloud AI; photos and health data never leave the device.
DermEdge checks a photo of skin in three stages, tracks each body site over time, and combines the result with wearable stress and skin-moisture markers into a daily flare-risk score with tips. It runs on a laptop hub or entirely in the browser.
Live demo: https://derm-edge.vercel.app (runs on your device; the first visit downloads the skin model once).
Pitch deck: Canva, with its slide text in docs/PITCH_DECK.md and speaking notes in docs/SPEAKING_POINTS.md.
Built by Team DermEdge (Tishya Yadlapalli, Nitish GB, Shreeya Jetti) for the Telemedicon 2026 AI & Digital Health Hackathon, IIIT Sri City.
| Home | Check |
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| History | Wearable & care |
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How it works
Phone / laptop cam ─► [1] Normal vs rash ─► [2] Eczema vs 7 look-alikes ─► [3] Severity 0–12
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DermEdge band + GSR, moisture ─► scratching, stress, hydration ──────────► Flare risk + tips
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Dashboard: history, body sites, doctor report
| Part | Model | Runs on | Result |
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| 1. Normal vs rash | CLIP ViT-B/16 + fine-tuned head | Browser | AUC 0.92: 88% of rashes caught, 82% of healthy skin cleared on real phone photos (SCIN, 5-fold CV by case); 84% of rashes caught on SkinDisNet (Bangladesh hospitals), up from 33% |
| 2. Eczema vs look-alikes | Head on the same CLIP embedding | Browser | AUC 0.70 on SCIN phone photos, 0.87 on SkinDisNet (5-fold CV). Replaced a ResNet18 that scored 81% on atlas photos but AUC 0.56 and 0.46 on these |
| 3. Severity | Ridge head on CLIP features, 0–12 EASI-signs score | Laptop hub | Mean error 1.2 points; band within one step 97.6% |
| Stress | LightGBM on GSR, skin temp, PPG, motion, calibrated per wearer | Laptop hub | AUC 0.92–0.94 leave-one-person-out (WESAD) |
| Future scope: on-device screen | MobileNetV1-0.25 INT8, 300 KB | ESP32-CAM | 97.1% accuracy on its test set |
Skin temperature is used only inside the stress model, not as an eczema signal. Plan, decisions and future scope: docs/HARDWARE_PLAN.md.
Stage 2 tells eczema apart from seven look-alikes: psoriasis, tinea, candidiasis, insect bites, lichen planus, drug eruption and rosacea (plus scabies and tinea in SkinDisNet).
31 Oct 2026, v2 heads in the browser. Testing on photos the models had never seen showed the first stage-2 ResNet18 at chance on real phone photos, and the rash screen passing only a third of South Asian clinic rashes. We compared CLIP with two dermatology encoders, DermLIP and DermLIP-PanDerm, on a fair test (SkinDisNet, published after their training data). Neither was clearly better (AUC 0.906 and 0.890 vs CLIP's 0.890), so we kept CLIP and retrained the heads on SCIN plus SkinDisNet: scripts/compare_encoders.py, scripts/train_web_heads_v2.py, results in experiments/encoder_comparison/ and experiments/web_heads_v2/. The confusion matrices below are from the first version, and the Python hub (app/) still runs it.
| Stage 1: normal vs rash | Stage 2: eczema vs look-alikes |
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| Real phone photos (SCIN), held out by case: 1182 of 1444 rashes caught, 84 of 90 healthy photos cleared. | Curated test split: of 251 eczema photos it caught 207 and missed 44; of 256 look-alikes it flagged 52 as eczema. |
Fair across skin tones
The first rash screen caught 84% of rashes on Fitzpatrick I–IV but only 67% on V–VI at one shared threshold, so it needed a calibrated threshold for darker skin. After South Asian clinic photos were added to its training (v2), it catches 86% on V–VI at the shared threshold, the same as lighter skin, with no calibration. Only 16 healthy V–VI photos exist in SCIN, so its healthy-skin figure for V–VI (81% cleared) is rough. Details: experiments/web_heads_v2/summary.json.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
