~~META: status = planning ~~

Late one evening, I arrived at an emergency department with a wound infection (7 days post-injury, clear signs: warmth, pain, redness). The waiting room was almost empty, two people total.

I sat by the door, exposed the wound, and asked the nurse: “Can you look at this for 5 seconds and tell me if it can wait until morning?”

She looked me in the eyes: “No. You're not from our district. Go to Prague 1.”

She could have lowered her head for 5 seconds. Instead, she sent me to reception for a taxi. It would have taken less time to assess the wound than to redirect me. The room was empty. Administrative boundaries mattered more than basic human compassion.

This project exists because:

• Emergency systems fail patients over bureaucracy
• Consistent, accessible first-line care shouldn't depend on luck
• Robots don't have empathy, but they also don't have apathy

An open-source 3D-printed robotic system for autonomous wound assessment and treatment:

Core Functions:

• AI vision assessment (infection detection, severity scoring)
• Ultrasonic wound debridement (bacteria elimination, biofilm removal)
• Automated cleaning and treatment application
• Prescription recommendations (antibiotics yes/no)

Target Applications:

• Emergency department triage (overnight/weekend shifts)
• Rural clinics with limited staff
• Veterinary clinics (regulatory pathway is easier)
• Field medicine (refugee camps, disaster zones, military)

Hardware:

• 3D-printed robotic arm (6-DOF, PETG/ABS)
• 4 tool attachments: air nozzle, liquid spray, ultrasonic probe, ointment applicator
• AI camera + depth sensor
• Raspberry Pi / Arduino control system

Treatment Protocol:

1. AI scan → assess severity
2. Liquid rinse → remove debris
3. Ultrasonic debridement (in antiseptic bath, 30-60 sec)
4. Final rinse → sterile flush
5. Air dry → compressed air
6. Apply ointment → levomekol or equivalent
7. Verification scan → repeat if needed
8. Output → antibiotic recommendation

Key Components:

• Vision: OpenCV + TensorFlow (wound segmentation, infection classifier)
• Control: Custom kinematics + safety monitoring
• Data: Treatment logging for continuous improvement

• ☐ Recruit 2-3 part-time collaborators (robotics, ML, medical background preferred)
• ☐ Design and 3D print basic arm structure
• ☐ Test individual tool attachments on synthetic models
• ☐ Build AI vision system (wound detection accuracy goal: 70%+)
• ☐ First functional demo on synthetic wounds
• ☐ Document build process for open-source community
• ☐ Apply for small grants (EU innovation funds, health-tech accelerators: €10-50k)

Success criteria: Working prototype treats synthetic wounds with 70%+ AI accuracy

• ☐ Attempt human medical partnerships (expect resistance)
• ☐ Pivot to veterinary applications (easier regulation, faster market entry)
• ☐ Partner with 2-3 vet clinics for supervised testing
• ☐ Collect real-world data (target: 100+ treatments)
• ☐ Improve AI accuracy to 85%+
• ☐ Regulatory research: CE Mark pathway, veterinary vs. human certification
• ☐ Explore NGO partnerships (field medicine, refugee camps)

Success criteria: 3 vet clinics piloting system, 85%+ accuracy, clear regulatory pathway identified

• ☐ Establish veterinary market beachhead (€500-1000/month subscriptions)
• ☐ Target revenue: €1500-3000/month from pilot clients
• ☐ Publish results: academic papers, conference presentations
• ☐ Build case study portfolio (video demos, testimonials)
• ☐ Apply for larger grants (EU Horizon, health innovation funds: €50-200k)
• ☐ Return to human medicine with proven veterinary technology
• ☐ Seek pre-seed funding (angels, health-tech VCs: €200-500k)

Success criteria: €2-5k MRR, published validation data, funding secured

• ☐ Grow team to 5-7 people (hire engineers, regulatory specialist)
• ☐ Begin CE Mark certification process (human medical device)
• ☐ Expand deployments: 10+ veterinary sites, 2-3 human pilots (NGO/field)
• ☐ Develop enterprise features (hospital integration, data analytics)
• ☐ Establish dual business model:
  • Open-source community edition
  • Commercial enterprise edition (support, certification, compliance)
• ☐ Seed round fundraising (€500k-1M)

Success criteria: Regulatory approval in progress, €10-30k MRR, seed funding closed

• ☐ CE Mark obtained
• ☐ Scale manufacturing and distribution
• ☐ Expand to multiple markets (EU, refugee/NGO sector, veterinary)
• ☐ Series A consideration or sustainable profitability path

6 months:

• ✓ Working prototype
• ✓ 2-3 active collaborators
• ✓ AI accuracy >70%

12 months:

• ✓ 3+ veterinary pilot sites
• ✓ AI accuracy >85%
• ✓ €10-50k grant secured

24 months:

• ✓ €2-5k MRR
• ✓ Published validation study
• ✓ Pre-seed funding (€200-500k)

36 months:

• ✓ CE Mark process underway
• ✓ €10-30k MRR
• ✓ Seed funding (€500k-1M)

Open-Source Strategy:

• Core hardware/software: MIT/Apache license
• Community innovation → rapid iteration
• Marketing effect → credibility & visibility

Revenue Streams:

• Hardware sales: Certified systems to clinics (€10-20k per unit)
• SaaS subscription: AI updates, analytics, compliance reporting (€500-1000/month)
• Consumables: Sterile single-use attachments, treatment solutions (€30-50 per patient)
• Support contracts: Training, maintenance, warranty (€200-500/month)

Target Markets (prioritized):

1. Veterinary clinics (Year 1-2: easier regulation, faster revenue)
2. NGO/Field medicine (Year 2-3: high impact, grant funding)
3. Human emergency care (Year 3+: after CE Mark)

Current:

• Founder: artem (project lead, systems integration)

Needed:

• Robotics engineer (arm design, motor control)
• ML engineer (vision system, AI models)
• Medical advisor (protocol validation, regulatory guidance)
• Part-time contributors (CAD, embedded systems, documentation)

Advisors (future):

• Regulatory consultant (medical device certification)
• Veterinary partner (pilot testing)
• VC/business mentor (fundraising strategy)

Project Resources:

• Parent project: 3d_printed_robotics_initiative
• GitHub repository: (to be created)
• Weekly meetings: TBD (part of robotics initiative)
• Communication: Brmlab Slack #robotics

Reference Technologies:

• Ultrasonic debridement: SonicOne, UltraMIST, QOUSTIC
• AI wound assessment: FDA-approved smartphone apps, academic research
• Budget robotic arms: BCN3D Moveo, Thor, SO-ARM100
• Medical robotics: da Vinci, STAR robot

Funding Sources:

• EU Horizon grants (health innovation)
• Health-tech accelerators (Y Combinator, Startup Health)
• Angels (health-tech focus)
• VCs (post-traction)

• Which veterinary clinic in Prague would pilot first?
• What grants should we target in Q1 2026?
• Should we focus on cats/dogs or larger animals first?
• Who knows regulatory consultants for medical devices?

Status: Planning phase — seeking initial team members and grant opportunities

Next Steps:

1. [ ] Recruit 1-2 collaborators
2. [ ] Research EU health innovation grants
3. [ ] Design first arm prototype (CAD)
4. [ ] Contact veterinary clinics for pilot interest

Last Updated: 2026-01-18