AI Wound Assessment · For Visiting Nurses

A Wound Expert in every nurse's hand

WoundSys assesses wound healing progress from a smartphone picture in seconds

Tells nurse the wound type, size, severity, infection status, healing progress score, whether to refer the patient and exactly what to do next.
Get step-by-step care guidance and dressing recommendations instantly
Recommends referrals automatically — so nothing falls through the cracks

Join the waitlist → Schedule a demo

Developed and validated with clinicians at UMass Memorial Medical Center

Live AI Result

WoundSys app showing assessment results with PWAT score and wound metrics

The problem

Wound care is hard. And the current system makes it harder.

😰

"I'm standing over a wound with no specialist on call. I have to guess — and I know I might be wrong."

Chronic wounds can take up to 13 months to heal and recur in 70% of cases. Wrong staging means wrong treatment from day one.

🏥

"There aren't enough wound-specialist nurses to go around. We're stretched thin."

Demand for wound care specialists is exploding. Many visiting nurses treat complex wounds without specialist backup

🦿

"A missed classification or late referral can cost a patient their limb."

Misclassification errors carry real consequences — delayed referrals and wrong dressings contribute to 160,000 preventable amputations in the US every year.

The solution

AI-guided co-pilot for wound assessment, built for the bedside

WoundSys turns a smartphone picture into a medical image — wound type, stage, healing score, infection signals, and care recommendations — all in one app, in seconds.

Accuracy

Before: Inexperienced nursing staff without immediate specialist support guesses wound stage

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Now: AI determines wound type and pressure injury stage with 86–90% accuracy, every time

Speed

Before: Waiting days for a specialist review means the wrong dressing stays on longer than it should.

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Now: Full assessment with dressing guidance and care plan delivered at point of care in seconds

Safety

Before: Referral decisions depend on individual nurse judgment — inconsistent and easy to miss.

↓

Now: Referral recommendations trigger automatically based on AI findings, with a full audit trail.

How it works

Three steps. One smartphone. Full assessment.

1 📸

Photograph the wound

Open the app, select the patient, and take a photo.Built-in guardrails guides and a lighting check ensures the image is high quality

~1 minute

2 🧠

AI analyses the image

WoundSys runs the photo through five AI stages in the cloud — detecting wound type, size, infection status, healing progress and referral recommendation.

~12 seconds

3 📋

Nurse reviews and acts

The nurse gets advise in plain-language: wound type, size, infection status, healing progress referral guidance and dressing and care guidance. Nurse can take advise or override — WoundSys recommends, not replaces, the clinical decision.

~ seconds

Features

Everything a visiting nurse needs to assess wounds with confidence

🔍

Automatic Wound type determination

Know the wound type — pressure injury, diabetic foot ulcer, venous ulcer, arterial ulcer, or surgical wound — without calling a specialist.

AI analyzes a picture of the wound in seconds, provides a confidence rating so nurses know how much to trust the result.

📊

Healing Progress Score (PWAT)

Score wound healing progress, track whether a wound is getting better or worse across visits, with a medically standardized score that is 90% accurate.

The PWAT healing score is calculated automatically from each photo, making progress visible over the patient's entire care timeline.

📐

AI Wound Measurements

Get wound area and depth measurements from a single picture in seconds — no ruler required at the bedside.

AI corrects for bad camera angle and lighting to ensure accurate wound dimensions, minimizing manual measurement errors.

🔔

Automatic Referral Recommendations

Never miss a referral. WoundSys AI flags wounds that need specialist attention and makes the recommendation.

Referral triggers are based on comprehensive wound assessment including severity, infection signals, and healing trajectory — giving nurses a documented reason to escalate, not just a gut feeling.

🩹

Personalized Care Recommendations

Get step-by-step dressing guidance and care actions tailored to each wound — not a generic protocol.

Recommendations are generated based on wound type, stage, exudate level, and infection risk, with red-flag escalation prompts built in.

📅

Patient Timeline & Photo History

See every assessment side by side. Share a wound report with complete history, as a PDF in one tap — ready for handoffs, referrals, or clinical records.

Pictures, measurements, and care notes are stored chronologically per patient, making trend review fast and thorough.

Use cases

Where WoundSys fits into your workflow

Visiting Nurse, Home Assessment

A nurse arrives at a patient's home to re-dress a diabetic foot ulcer. She photographs the wound, gets an instant classification and updated healing score, and follows the AI-generated care steps — all before she leaves. She doesn't need to call her supervisor or wait for a specialist callback.

👩‍⚕️ For: Visiting nurses, home health aides

Care Coordinator, Multiple Patients

A care coordinator manages 40 patients with chronic wounds across a home health agency. With WoundSys, assessments made by every are documented consistently — the coordinator can review healing trends, catch deteriorating cases early, and generate and follow-up on referral summaries without chasing down individual nurses for notes. Nurses doing a great job are easy to see to support promotion or salary increase decisions

📋 For: Home health agency coordinators, clinical leads

Why WoundSys

A fully automatic wound system delivering recommendations from assessment to referral guidance.

No manual steps. Just a photo.

Most wound apps require nurses to manually trace wound edges, input measurements, or select tissue types. WoundSys does all of that automatically from a single smartphone photo — no accessories, no extra training.

Fully Automatic

Built over 12 years of NIH-funded research

WoundSys is developed by WPI researchers with $5.3M in NSF and NIH grants, 22 peer-reviewed research papers, and one awarded patent. This isn't a startup pivot — it's over a decade of wound AI research turned into a product that solves a massive pain point.

Research-Backed

AI that tells you what to do, not just what it sees

Most competitors classify the wound and stop. WoundSys goes further: it generates care recommendations, dressing guidance, and referral recommendation — giving nurses an action, not just a label.

Decision Support

Clinically validated, not just lab-tested

WoundSys is in pre-clinical trial at the UMass Memorial Medical Center. The accuracy figures — 87–90% on healing scores and wound classification — are from real clinical data, not controlled demos.

Clinical Validation

The nurse always decides

WoundSys recommends; the nurse confirms. Every AI output is to support the nurse's decision not replace them. Every override is logged, and every result includes a confidence indicator — so nurses stay in control.

Nurse-First Design

Proof

Over a decade of research. Real accuracy numbers.

87–90%

Healing progress accuracy

86–90%

Wound type & stage accuracy

81%

Referral decision accuracy

$5.3M+

NSF / NIH research grants

Peer-reviewed publications

🔬

Preclinical Progress

Active collaboration with UMass Memorial Medical Center — one of New England's leading academic medical centers — validating WoundSys in a real clinical environment.

🏥 UMass Memorial Medical Center

⚖️

Patent Protection

1 awarded patent covering key wound AI, with 5 additional provisional patents filed.

✅ 1 Awarded + 5 Provisional

💬

Customer Validation

Validated through 23 interviews across home health and clinical settings, where nurses, care coordinators, and clinical leads confirmed the urgency of the problem and the need for a solution worth paying for.

👥 23 Customer Discovery Interviews

Get early access

Be first when WoundSys launches

Join the waitlist for early access, or schedule a 30-minute demo to see live WoundSys demo with your team.

Join the Waitlist

Get early access, product updates, and first access to our pilot programme.

✅

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FAQ

Questions we hear most often

WoundSys is built for visiting nurses, home health agency staff, and clinical teams who assess chronic wounds without a specialist nearby. It's most useful for nurses managing pressure injuries, diabetic foot ulcers, venous ulcers, arterial ulcers, and post-surgical wound follow-ups.

No. WoundSys is a decision support tool. Every result is clearly labelled as AI-assisted guidance, not a clinical diagnosis. The nurse reviews every output and makes the final clinical call. We believe strongly that WoundSys should support clinical judgment — not replace it.

Based on published research and preclinical work: 87–90% on healing progress scoring (PWAT), 86–90% on wound type and pressure injury stage, and 81% on referral decision accuracy. Every result includes a confidence indicator (High, Medium, or Low) so nurses know how much weight to give the AI output.

WoundSys is designed for HIPAA-compliant environments. Image processing runs on secure AWS cloud infrastructure. We do not share patient data with third parties. Full data privacy and compliance details will be confirmed ahead of any pilot programme — please ask during your demo.

For pilot participants, onboarding includes app setup, a short clinical orientation (typically one session), and ongoing support from the WoundSys team. The app is designed so nurses can complete their first assessment with minimal training — the workflow mirrors how they already document wounds.

WoundSys is currently in pre-commercial development, with preclinical work ongoing at UMass Memorial Medical Center. Waitlist members and demo participants will be among the first to access the pilot programme when it opens. We'll keep you informed at each stage — no surprises.

Pricing is not yet finalised for commercial launch. The deck references a freemium model for early users and subscription tiers for agencies. Early waitlist and pilot participants will be offered the most favourable terms. [Placeholder: confirm pricing tiers before launch.]

Waitlist members get: first access to the pilot programme, regular product updates, direct input into features before launch, and the most favourable pricing when commercial access opens. This is the single best way to get WoundSys before anyone else at your agency does.